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text:
\section{Introduction}
\label{sec:intro}
The International Classification of Diseases (ICD) was created in 1893, when a French doctor named Jacques Bertillon named 179 categories of causes of death. It has been revised every ten years since then and has become an important standard for information exchange in the health care sector. Importantly, it is endorsed by the World Health Organisation (WHO) and it has been adopted widely by physicians and other health care providers for reimbursement, storage and retrieval of diagnostic information.

ICD coding is a complex process where clinical coders have to consult multiple sources and navigate through these sources to identify the right codes to use. The label space is very large, with over 15,000 codes in the ICD-9 taxonomy. The more recent ICD-10-CM/PCS has over 140,000 codes combined. (World Health Organisation, 2016).

The diagnosis descriptions written by clinicians and the textual descriptions of ICD codes are written in quite different styles even if they refer to the same disease. Dealing with the clinical text is challenging as it includes irrelevant information,
has an informal tone, does not necessarily follow correct grammatical conventions and contains a large medical vocabulary. On the other hand, the definitions of ICD codes are formally and precisely worded.

While ICD codes are important for making clinical and financial decisions, clinical coding is time-consuming, error-prone and expensive due to the reasons mentioned above.

ICD codes are organised in a hierarchical structure where the top-level codes represent generic disease categories and the bottom-level codes represent more specific diseases. A mistake may happen when the coder matches the diagnosis description to an overly generic code instead of a more specific code or when a code is omitted altogether. Also, it is important to note that all other illnesses or pre-existing conditions (called comorbidities) that affect a patient's care, should be captured with a code.

In this work, we utilise machine learning and natural language processing techniques to help clinicians by automating the ICD coding process using information present in unstructured texts as well as structured data. In particular, we make use of the MIMIC-III dataset~\cite{mimiciii} to empirically evaluate our approach.

Prior work on MIMIC-III dataset has made use of deep learning methods. Specifically, classifying discharge summaries with their corresponding ICD codes~\cite{DBLP:conf/naacl/MullenbachWDSE18,li2020icd,xu2019multimodal} and, descriptions about the ICD codes have been used as label based information to improve the performance.

In our work, we propose a novel multimodal approach to enhance existing work without relying upon external knowledge such as the descriptions of the ICD coding as label-based information or complex architectures. Instead, we combine existing information present within various systems containing structured information along with discharge summaries. Furthermore, we exploit  the geometric properties of the pretrained word embeddings as well as, combining embeddings trained on external knowledge into a common dimensional space using using meta-embedding techniques~\cite{DBLP:conf/naacl/CoatesB18,DBLP:conf/emnlp/KielaWC18,DBLP:conf/ijcai/BollegalaHK18}. Pretrained word embeddings have been successfully used in downstream NLP tasks~\cite{DBLP:conf/emnlp/Kim14} since they capture semantic relationships and provide better text representations. To study the potential advantage of our proposed approach, we consider prior work~\cite{DBLP:conf/naacl/MullenbachWDSE18,li2020icd} utilising pretrained word embeddings specifically trained on the MIMIC dataset in their neural model.

Furthermore, models trained on structured and unstructured information on different modalities are further used in an ensemble and we build a meta-classifier for training on the different predictions from the different models for making the final prediction. We empirically show that the final ensemble model outperforms the baseline methods in this multi-label classification task.

In particular, the contributions of this paper are as follows:
\begin{description}
    \item [1] We propose a simple and effective approach, which we term as \emph{pre-training initialisation steps} on pre-trained word vectors that are fed into a CNN-based architecture for multi-label classification. We evaluate our approach on top 32 and 50 ICD codes similarly to related work and we show an improvement by utilising different initialisation step.
    \item [2] An ensemble approach that utilises various modalities, combining both structured and unstructured information. Our ensemble model empirically outperforms the baseline methods.
\end{description}

\section{Related work}
\label{sec:related}

There has been a growing interest within the field of natural language processing for learning representations of words as vectors, also known as word embeddings. Pretrained word embeddings are obtained by training on a large corpus and two main approaches are followed for learning these vectors namely, count-based and prediction-based. The Glove algorithm~\cite{pennington2014glove} is a popular count-based approach that makes use of the co-occurrences probabilities of words. The Word2Vec~\cite{mikolov2013distributed} approach is a popular prediction-based approach, in which models are trained based on CBOW (continuous bag of words) or the Skip-Gram approach. A growing interest in topics related to word embeddings is reflected by the numerous
papers published in various NLP conferences.

A new area of interest within word embeddings is to learn \emph{meta-embeddings} from different pre-trained word vectors without having the actual text sources on which the word vectors have been trained. One of the simplest approaches proposed is to use concatenation followed by averaging~\cite{DBLP:conf/naacl/CoatesB18}. Yin et al.~\shortcite{yin2016learning} is one of the earliest works in meta-embedding and use a projection layer known as 1TON for computing the meta-embeddings in a linear transformation.
However, few work have studied the use of meta-embeddings for the healthcare domain~\cite{el2019embedding,DBLP:conf/biostec/ChowdhuryZYL20}. To the best of our knowledge, the application of meta-embedding techniques for multilabel classification of ICD coding has not yet been investigated. In this work we investigate several different approaches for doing so, particularly focusing on combining pretrained in-domain word embeddings with pretrained word embeddings derived from external sources such as scientific articles on the Web.

Automatic ICD coding using unstructured text data has been explored by researchers for several years whereby the full breadth of learning approaches has been considered. Koopman et al.~\shortcite{koopman2015automatic} utilised a multi-label classification approach and combined SVM classifiers via a hierarchical model to assign ICD codes to patient death certificates, first identifying whether the cause of death was due to cancer, then identifying the type of cancer and associated ICD code. Whilst the final solution performed well, there were two key limitations to the approach: the coverage of cancers included in the dataset was very imbalanced resulting in cancer types associated with rarer diseases being harder to predict and the cancer identification model was susceptible to false-positives when a patient was cited as having cancer but it was not the primary cause of death.

More recently, the scope of the problem has been extended to include multiple ICD codes and it has been addressed via multi-label methods with researchers more often utilising deep learning approaches, generally centred around CNN and LSTM based architectures. Mullenbach et al.~\shortcite{DBLP:conf/naacl/MullenbachWDSE18} adopted a CNN architecture with a single filter, defining a per-label attention mechanism to identify the relevant parts of the latent space. The CNN architecture~\cite{DBLP:conf/emnlp/Kim14} has been proven to be useful for sentence classification tasks and in this work, they empirically show that CNN is better for this task. The per-label attention provided a means of scanning through the entire document without limiting it to a particular segment of the data. This approach achieved state-of-the-results across several MIMIC datasets. In our approach, we use a similar architecture but instead focus on using \emph{pre-training initialization steps} and also combining structured features, along with the unstructured information. Both approaches are explained in the paper along with empirical evidence of the improved performance that our proposed approach gives.

Vu et al.~\shortcite{ijcai2020-461} proposed a BiLSTM encoder along with a per-label attention mechanism, inspired by the work of Lin et al.~\shortcite{DBLP:conf/iclr/LinFSYXZB17} which proved it to perform well at generating general sentence embeddings. Here, Vu et al.~\cite{ijcai2020-461} extended the attention mechanism by generalising it for multilabel classification by performing an attention hop per label.

Xie et al.~\shortcite{xu2019multimodal} proposed a text-CNN for modelling unstructured clinician notes however, rather than implement an attention mechanism, the authors extracted features via TF-IDF from the unstructured guidelines provided to professionals when defining the ICD classifications. By including these features along with the convolved CNN layer, the authors mimicked the input that the professionals would get from the guidelines. To enrich the predictions of the unstructured data model the authors used an ensemble-based method of three models. Semi-structured data was utilised by embedding the ICD code descriptions in the same latent vector space as the diagnosis descriptions and structured data was utilised through a decision tree model. The imbalance issues in the data was addressed via Label Smoothing Regularization and the resulting model achieved state-of-the-art accuracy, for the time, as well as improving model interpretability. However, they do not disclose what structured data they used specifically and what features were used.

Shi et al.~\shortcite{Shi2017TowardsAI} propose an hierarchical deep learning model with attention mechanism which can automatically assign ICD diagnostic codes given written diagnosis. They also propose an attention mechanism that is designed to address the mismatch between diagnosis description number and assigned code number. The results show that the soft attention mechanism improves performance. However, they only focus on top 50 ICD-9 codes.

\section{Proposed approach}
\label{sec:proposed}
In this section, we explain our proposed multi-label classification approach on the discharge summaries (unstructured data) as well the multimodal approach (structured and unstructured data) for automatically predicting the ICD coding.

\subsection{Unstructured Data}
\label{sec:proposed_unstructured}
In this subsection, we explain our approaches for classifying unstructured data i.e., discharge summaries. Based on prior work~\cite{DBLP:conf/naacl/MullenbachWDSE18}, we follow a similar deep learning approach using a convolutional neural network with a per-label attention mechanism that can span across the entire document to identify the important portions of the document that correspond to the different labels. Differing from~\cite{DBLP:conf/naacl/MullenbachWDSE18}, we propose the introduction of an additional step, which we term as "$\emph{pre-training initialization steps}$" for obtaining efficient and effective pre-trained word vectors that can provide a better representation of the text that are inputted into the CNN architecture.

Firstly, let us assume that we trained word embeddings on MIMIC discharge summaries using Word2Vec~\cite{mikolov2013distributed} algorithm as (1) our purpose here is to focus on exploiting as much of semantic information captured for better utilisation of embeddings and (2) it serves as a baseline comparison with prior work~\cite{DBLP:conf/naacl/MullenbachWDSE18}. In the first \emph{pre-training initialisation step}, we introduce post-processing steps on the pretrained Word2Vec word vectors. The details of these post-processing steps are described below.

\subsubsection{Post-processing word vectors}
\label{sec:postprocess}
Let us consider a set of words present in a corpus, represented as  $\textrm{w} \in \textrm{V}$, such that each word $\textrm{w}$ is represented by a pretrained word embedding $\boldsymbol{w}_{i} \in \mathbb{R}^{k}$ in some $k$ dimensional vector space.

We term the first step in the post-processing~\cite{DBLP:conf/iclr/MuV18} as
$\textbf{MeanDiff}$ which computes the mean embedding vector, $\hat{\boldsymbol{w}}$, of all embeddings for the words in $\textrm{V}$ and the word embedding of each word is further computed by subtracting the mean from the corresponding word vector as shown below:

 \begin{align}
  \hat{\boldsymbol{w}} = \frac{1}{|\textrm{V}|} \sum_{\textrm{w} \in \textrm{V}} \boldsymbol{w}
  \label{eq:mean}
 \end{align}

 \begin{align}
 \forall_{\textrm{w} \in \textrm{V}} \ \ \  \tilde{\boldsymbol{w}} =  \boldsymbol{w} - \hat{\boldsymbol{w}}
 \label{eq:mean_subtracted}
 \end{align}

Mu and Viswanath~\shortcite{DBLP:conf/iclr/MuV18} observed that the normalised variance ratio decays until some top $l \leq d$ components, and remains constant after that, and proposed removing the top $l$ principle components from the embeddings obtained after subtracting the mean vector (Eq.~\ref{eq:mean_subtracted}).

Based on the above observation, the second step in the post-processing is to apply principal component analysis on the word vectors obtained using Eq.~\ref{eq:mean_subtracted} and, removing the first $d$ principal components from each individual word vector. We term this step as $\textbf{PCADiff}$ for our usage. This is carried out by arranging the word vectors as columns within a matrix $\textbf{A} \in \mathbb{R}^{k \times |\textrm{V}|}$. Here, we denote the principal components as $\boldsymbol{u}_{1}, \ldots, \boldsymbol{u}_{d}$ and remove the first $\textrm{D}$ principal components as follows:

  \begin{align}
 \label{wordpca}
 \boldsymbol{w}' = \tilde{\boldsymbol{w}} - \sum_{i=1}^{l} \left( \boldsymbol{u}^{\top}_{i} \boldsymbol{w} \right) \boldsymbol{u}_{i}
 \end{align}

In the above approach, we focus on the in-domain trained word embeddings i.e the word vectors trained on the MIMIC discharge summaries.

The next approach that we propose is to use external knowledge available in the Web i.e using PubMed scientific articles. We trained word vectors on the PubMed scientific articles using the Word2Vec algorithm. Recent work~\cite{yin2016learning} shows that pretrained word vectors trained on same source of information but different algorithms contain varied quality in the semantics captured. We propose the same intuition can be applied to using the same algorithm but on different datasets. In order to capture the semantics from both the word embeddings i.e MIMIC discharge summaries and the PubMed articles respectively, we combined the information in the form of  "\emph{meta-embeddings}" i.e capturing the different embeddings into a common meta-embedding space. While there are several approaches to achieve meta-embeddings, in this paper we focus on two methodologies which are described below.

\subsubsection{Meta-embeddings}
\label{sec:meta_embeddings}
Let us assume, for simplicity, we have two different sources of information i.e one based on the discharge summaries and the other based on the PubMed articles. Let us represent the vocabulary of words present in the two sources of information as $\mathcal{S}_{1}$ and $\mathcal{S}_{2}$ respectively.

The two methodologies for getting meta-embeddings are explained below.

\begin{description}
\item[Averaging meta-embedding] Here, we assume that the words present in $\mathcal{S}_{1}$ and $\mathcal{S}_{2}$ were trained on the same algorithm i.e Word2Vec to produce word vectors and that the vectors belonging to both sources have a common dimensionality $\textrm{d}$. Prior work~\cite{DBLP:conf/naacl/CoatesB18} shows averaging the embedding vectors is a useful meta-embedding technique that achieves comparable result as that of concatenating the vectors.

To understand this, let us consider the meta-embedding of each word represented by averaging the word vectors from the two sources as follows:
\begin{equation}
    \hat{\boldsymbol{w}} = \frac{\boldsymbol{w}_{\mathcal{S}_{1}} + \boldsymbol{w}_{\mathcal{S}_{2}}}{2}
    \label{eq:avg_meta}
\end{equation}

In this case, the Euclidean distance between two words $\hat{\boldsymbol{w}}_{1}$ and $\hat{\boldsymbol{w}}_{2}$ based on Eq.~\ref{eq:avg_meta} is given as follows:
\begin{equation}
    \textrm{E}_\textrm{AVG} =\left\Vert \hat{\boldsymbol{w}}_{1} - \hat{\boldsymbol{w}}_{2} \right\Vert^2
    \label{eq:meta_avg_dist}
\end{equation}
\begin{equation}
 \textrm{E}_\textrm{AVG} \approx
 \sqrt{\left(\textrm{E}_{\mathcal{S}_{1}}\right)^2 + \left(\textrm{E}_{\mathcal{S}_{2}}\right)^2 -  \left(2\textrm{E}_{\mathcal{S}_{1}}\textrm{E}_{\mathcal{S}_{2}}\textrm{cos}\theta\right)}
 \label{eq:meta_avg_approx}
\end{equation}
\begin{equation}
\textrm{E}_\textrm{AVG} =  \sqrt{\left(\textrm{E}_{\mathcal{S}_{1}}\right)^2 + \left(\textrm{E}_{\mathcal{S}_{2}}\right)^2}
\label{eq:meta_avg_final}
\end{equation}
Coates and Bollegala~\cite{DBLP:conf/naacl/CoatesB18} shows that the source embeddings are approximately orthogonal (Eq.~\ref{eq:meta_avg_approx}) and hence, averaging captures approximately similar information as concatenation, without increasing the dimensionality.
Hence, we use this averaging method for combining the word embeddings as meta-embeddings.
\newline
\item [Locally linear meta-embedding] In the above method, we find that averaging-based meta-embeddings performs comparable to concatenation~\cite{DBLP:conf/naacl/CoatesB18}. However, one of it's limitation is that it does not capture the variations present within the local neighbourhood of the word vectors in different sources. To address this issue, Bollegala et al.~\shortcite{DBLP:conf/ijcai/BollegalaHK18} constructed meta-embeddings in an unsupervised manner where they consider the mapping of embeddings from different sources to a common meta-embedding space based on the local neighbourhood of a word in each of the sources.
Two steps were performed i.e the embeddings in each source for a given word was reconstructed based on its local neighbourhood and this was used for projecting the embeddings into a common meta-embedding space such that the nearest neighbouring words are embedded close to each other.
\newline

Let us assume we have a set of words present in the two sources i.e. $\mathcal{S}_{1}$ and $\mathcal{S}_{2}$ and, let us represent their vocabularies as $\mathcal{V}_{1}$ and $\mathcal{V}_{2}$ respectively such that, $\mathcal{V}$ represents the common set of vocabulary. Further, for each word $\textrm{v} \in \mathcal{V}_{1}$, we represent the word vectors as $\boldsymbol{v}_{\mathcal{V}_{1}} \in \mathbb{R}^{\textrm{d}_{1}}$. Similarly, for each word $\textrm{v} \in \mathcal{V}_{2}$, we represent the word vectors as $\boldsymbol{v}_{\mathcal{V}_{2}} \in \mathbb{R}^{\textrm{d}_{2}}$. Here, $\textrm{d}_{1}$ and $\textrm{d}_{2}$ represent the dimensions of the vectors respectively.

In the reconstruction step, for each word $\textrm{v} \in \mathcal{V}$ i.e $\textrm{v} \in \mathcal{V}_{1} \cap \mathcal{V}_{2}$, we obtain the \emph{k} nearest neighbours present within the two sources $\mathcal{S}_{1}$ and $\mathcal{S}_{2}$ respectively. This is carried out using the BallTree algorithm based on prior work~\cite{DBLP:conf/ijcai/BollegalaHK18} since this approximate methodology reduces the time complexity in identifying the approximate \emph{k} neighbours. Let us denote the neighbours as $\mathcal{N}_{\mathcal{V}_{1}}$ and $\mathcal{N}_{\mathcal{V}_{2}}$ respectively.

\begin{equation}
\Psi(\text{\bf{W}}) = \sum_{i=1}^{2} \sum_{\textrm{v} \in \mathcal{V}} \left\Vert \boldsymbol{v}_{\mathcal{V}_{i}} - \sum_{ \textrm{u} \in \mathcal{N}_{\mathcal{V}_{i}} (\textrm{v})} \textrm{w}_{\textrm{u}\textrm{v}}\boldsymbol{u}_{\mathcal{V}_{i}} \right\Vert^{2}_{2}
\label{eq:reconstuction}
\end{equation}

where
$\textrm{w}_{\textrm{u}\textrm{v}} = 0;$ if the words are not \emph{k} neighbours in either of the sources.

\begin{equation}
\begin{split}
& \frac{\partial(\Psi(\text{\bf{W}}))}{\partial\textrm{w}_{\textrm{u}\textrm{v}}} = \\
& -2 \sum_{i=1}^{2}\left( \boldsymbol{v}_{\mathcal{V}_{i}} -  \sum_{\textrm{x}\in \mathcal{N}_{\mathcal{V}_{\textrm{i}}}(\textrm{x})} \textrm{w}_{\textrm{v}\textrm{x}}\boldsymbol{x}_{\mathcal{V}_{i}} \right)
^\top \boldsymbol{u}_{\mathcal{V}_{i}}\mathbb{I} [\textrm{u} \in \mathcal{N}_{\mathcal{V}_{i}}(\textrm{v})]
\end{split}
\label{eq:meta_error_grad}
\end{equation}

Given a word $\textrm{v} \in \mathcal{V}$, for each neighbouring word $\textrm{u}$ in $\mathcal{N}_{\mathcal{V}_{1}}$ and $\mathcal{N}_{\mathcal{V}_{2}}$, the recontruction weights are learned such that the reconstruction error given in Eq.~\ref{eq:reconstuction} is minimized i.e minimizing the sum of the local distortions present in the two sources and to do this, the error gradient is computed using Eq.~\ref{eq:meta_error_grad}.

Based on prior work~\cite{DBLP:conf/ijcai/BollegalaHK18}, the weights are uniformly randomly initialized for each \emph{k} neighbour and optimal weights are obtained using stochastic gradient descent (SGD) with the initial learning rate set as 0.01 and maximum number of iterations is set to
100.

The weights are normalized and used in the projection step.
The projection step makes use of the normalized reconstructed weights and learns the the meta-embeddings of the words $\textrm{u}, \textrm{v} \in \mathcal{V}$ in a common dimensional space $\mathcal{P}$ i.e $\boldsymbol{u}_{\mathcal{P}}, \boldsymbol{v}_{\mathcal{P}} \in \mathbb{R}^{d_{\mathcal{P}}}$ such that
the local neighbourhood from both the sources is preserved. To do this, projection cost given below is minimized:

\begin{equation}
\Psi(\mathcal{P}) = \sum_{i=1}^{2} \sum_{\textrm{v} \in \mathcal{V}} \left\Vert \textrm{v}_{\mathcal{P}} - \sum_{ \textbf{u} \in \mathcal{N}_{\mathcal{V}_{i}} (\textrm{v})} \textrm{w}^{\prime}_{\textrm{u}\textrm{v}}\textbf{u}_{\mathcal{P}} \right\Vert^{2}_{2}
\label{eq:meta_lle}
\end{equation}
where
\begin{equation}
\textrm{w}^{\prime}_{\textrm{u}\textrm{v}} = \textrm{w}_{\textrm{u}\textrm{v}} \sum_{i=1}^{2} \mathbb{I}[\textrm{u} \in \mathcal{N}_{\mathcal{V}_{i}} (\textrm{v})]
\label{eq:lle_expand}
\end{equation}
such that if $\textrm{x}$ = 1; $\mathbb{I}(\textrm(x))$ = \emph{True} and \emph{False} otherwise.

The meta-embeddings are obtained by computing the smallest $(\textrm{d}_{\mathcal{P}} + 1)$ eigenvectors of the matrix given below.

\begin{equation}
 \text{\bf{M}} = (\text{\bf{I}} - \text{\bf{W}}^{\prime})^\top(\text{\bf{I}} - \text{\bf{W}}^{\prime})
 \label{eq:lle_eigen}
\end{equation}
such that matrix $\text{\bf{W}}^{\prime}$ contains the values computed using Eq.~\ref{eq:meta_lle}.
\end{description}

Based on the described \emph{pre-training initialization steps}, we can apply the different post-processing techniques on pretrained word embeddings to obtain final embeddings. We can also combine embedding vectors (post-processed or not) from different sources into meta-embeddings. This provides us with different variants of pretrained word embeddings capturing different semantic information. These different embedding vectors can be fed into a neural model for multilabel classification of the ICD coding.

For the purpose of comparison, we use the CNN-based architecture following prior work~\cite{DBLP:conf/naacl/MullenbachWDSE18} which is described below.

\begin{description}
\item[CNN Encoder] A document is represented as $\textrm{X} = \{ \boldsymbol{x}_{1}, \ldots, \boldsymbol{x}_{N}\}$ such that each word is represented using a pre-trained word vector. A convolutional neural architecture is used to encode the document at each n step as:

\begin{equation}
    \boldsymbol{h}_{i} = \tanh(\text{\bf{W}}_{c}\ast \boldsymbol{x}_{\textrm{i}:\textrm{i}+\textrm{k}-1} + \textrm{b}_\textrm{c})
    \label{eq:cnn}
\end{equation}

where $\text{\bf{W}}_{c} \in \mathbb{R}^{\textrm{k} \times \textrm{d}_{\textrm{e}} \times \textrm{d}_{\textrm{c}}}$ represents the convolutional filter such that $\textrm{k}, \textrm{d}_{\textrm{e}}, \textrm{d}_{\textrm{c}}$ denote filter width, input embedding dimension and filter output size respectively.
\item [Per-Label Attention] Following the base representation of the document as $\text{\bf{H}} = \{\textrm{h}_{1}, \ldots, \textrm{h}_{N}\}$, for each label $\textit{l}$, a per-label attention vector is computed as follows:
\begin{equation}
\boldsymbol{a}_{\textit{l}} = Softmax(\text{\bf{H}}^T\boldsymbol{u}_{\textit{l}})
\label{eq:per_label_attention}
\end{equation}
This attention vector is then used for representing the label-based document representations as follows:
\begin{equation}
    \boldsymbol{v}_{\textit{l}} = \sum_{n=1}^{\textrm{N}} \boldsymbol{a}_{\textit{l},\textit{n}}\boldsymbol{h}_{\textit{n}}
    \label{eq:attn_vec}
\end{equation}
\item[Classification] The probability per label is computed as follows:
\begin{equation}
    \hat{\textrm{y}}_{\textit{l}} = \sigma(\alpha_{\textit{l}}^{\top}\boldsymbol{v}_{\textit{l}} + \textrm{b}_{\textit{l}})
    \label{eq:cnn_classification}
\end{equation}
where $\alpha \in \mathbb{R}^{d_{c}}$ represents the vector containing the prediction weights.

The training objective aims to minimize the binary-cross entropy loss as given below.
\begin{equation}
\textrm{Loss}(\textrm{X}, \textrm{y}) = - \sum_{\textit{l} = 1}^{\mathcal{L}} \textrm{y}_{\textit{l}}\log(\hat{\textrm{y}}_{\textit{l}}) + (1 - \textrm{y}_{\textit{l}})\log(1 - \hat{\textrm{y}}_{\textit{l}})
\label{eq:cnn_loss}
\end{equation}
\end{description}
The probability scores per label are further used in combination with structured data to improve the performance of the multilabel classification, which is explained in detail in the following subsection.

\subsection{Multimodal approach}
\label{sec:multimodal}
We utilise data from the MIMIC-III dataset specifically, admission information, lab reports, prescriptions, vital signs (chart events table) and microbiology test results. All of the aforementioned data are tabular formats that contain information about a patient's care during their hospital admission as well as some textual information like medication and lab results.
\subsubsection{Structured Data}
We aggregate data from these modalities per admission, and we extract statistical properties, i.e., mean, standard deviation, min and max for each numeric value as well as the number of measurements taken for each patient in each admission. as we require at least some monitoring of the patients health during their stay.
In particular, we look into the 100 most common items that measurements were taken for each patient. These include, \emph{heart rate}, \emph{hemoglobin}, \emph{respiratory rate}, \emph{creatine}, \emph{bun}, \emph{wbc}, \emph{magnesium} etc.
\subsubsection{Categorical Data}
\label{sec:multimodal_categorical}
Textual information contained in tables but with no particular meaning such as medication (drugs) are represented using the Term Frequency -Inverse Document Frequency (TF-IDF) based features. We use Tree-based approaches and specifically eXtreme Gradient Boosting (XGBoost) models~\cite{Chen:2016:XST:2939672.2939785} for all the features we identified but we keep the TF-IDF in separate models.

These models are trained separately on the Train set and make predictions on a second unseen test set, namely Test (as shown on Table~\ref{tab:samples}). The test set is consequently used in an ensemble where we use a 5 fold cross-validation method to train and test our meta-model.

Our meta-classifier is logistic regression on this occasion which utilises the logistic function (sigmoid function) that ensures our  output is between 0 and 1:
\begin{equation}
g(z) = \frac{1}{1 + exp(-z)}
\end{equation}
 The logistic regression hypothesis is then defined as:
\begin{equation}
h_\beta(x) = g(\beta^T x)
\end{equation}
In order to solve the equation we use Maximum Likelihood
\begin{equation}
J(\beta ) = \frac{1}{m} \sum_{i=1}^m[-y_i log(h_\beta (x_i) - (1 - y_i) log(1-h_\beta (x_i))]
\end{equation}
which implies we need to solve:
\begin{equation}
\frac{dJ(\beta)}{d\beta} = \frac{1}{m} \sum_{i=1}^Nx_i(h_\beta(x_i)-y_i) = 0
\end{equation}

\section{Experiments}
\label{sec:experiments}
This section provides a brief overview on the MIMIC-III dataset that we use in our experiments, the different experimental settings conducted on the unstructured data i.e discharge summaries and the experiments using multimodal data (structured and unstructured information).
\subsection{Data and Preprocessing}
\label{sec:data}
\begin{table}
  \caption{Total number of samples in train, test and dev set based on 32 ICD-10 codes and 50 ICD-9 codes respectively.}
  \label{tab:samples}
  \begin{tabular}{cccl}
    \toprule
    Data & Train & Dev & Test\\
    \midrule
    32 ICD-10 & 28201 & 3134 &12430 \\
    50 ICD-9 & 8044 & 804 & 1725 \\
  \bottomrule
\end{tabular}
\label{datasplit}
\end{table}

In this paper we use the well-known MIMIC-III database for empirically evaluating our approach. The dataset has electronic health records of 58,976 patients who stayed in the Intensive Care Unit (ICU) of Beth Israel Deaconess Medical Centre from 2001 to 2012~\cite{mimiciii}. This includes information such as demographics, vital sign measurements, laboratory test results, procedures, medications, caregiver notes and imaging reports. For our purposes, we remove admissions that are not associated with a discharge summary. Also, we remove admissions that have no information on admission, laboratory result data, prescriptions, and as we require at least some monitoring of the patients health during their stay.
The total number of unique admissions after filtering is $44,765$.
A discharge summary is considered very useful to understand what happened during an admission as it includes but is not limited to information about the history of illness, past medical history, medication, allergies, family and social history, physical exam at the point of admission, lab result summary, procedures, discharge condition and status as well as discharge medication, follow-up plans, final diagnosis and other discharge instructions. In terms of preprocessing the summaries, we removed symbols and numbers not associated to a text.
In this work, our aim is to predict the ICD code classification for each of the admissions we are considering. Even though the MIMIC-III dataset includes ICD-9 mappings, we manually map those to the new ICD-10 codes and get the top 32, similarly to~\cite{xu2019multimodal}. Also, for completeness we experiment with the top 50 ICD-9 codes as in previous studies.

Table.~\ref{datasplit} contains the number of samples present within the training, development and test set for experimentation purposes.

\subsection{Experimental settings}
\label{sec:experimental_settings}

Experiments based on both structured and unstructured data are carried out with the top 32 ICD-10 codes and top 50 ICD-codes in order to align with benchmarks in the literature.

\subsubsection{Unstructured Data}
\label{sec:experiment_unstructured}
Experiments based on unstructured information were carried out by extracting the discharge summaries based on top 32-ICD codes as well as top 50 ICD-9 codes separately.

The CNN-based architecture (also known as CAML)~\cite{DBLP:conf/naacl/MullenbachWDSE18} is used for training and comparison purpose. We use this as a baseline approach by using embeddings trained on the MIMIC train dataset for initialising the neural model. The hyperparameters set are: embedding dimension as \emph{200}, dropout as \emph{0.5}, filter size as \emph{4}, learning rate as \emph{0.001}, batch size as \emph{16}, filter maps as \emph{50} and patience as \emph{3}. We train the model for \emph{100} epochs with an early stopping criteria based on the \emph{micro-F1} score such that the training is stopped if the \emph{micro-F1} score does not improve after \emph{3} epochs.

We also experimented with the MultiResCNN architecture using the default hyperparameters~\cite{li2020icd} aside from embedding size, for which we used {200}. Due to the complexity of the model, we report the scores based on the same epoch as the best epoch achieved using the baseline CNN-architecture and found the baseline model to perform better. Henceforth, we use the baseline architecture for our further experiments. In order to understand the adaptability of our proposed methodology, we report the scores using the best meta-embedding technique using the MultiResCNN architecture.

In addition, we extract full scientific articles from PubMed, which consists of 672,589 articles in total for training the word embeddings~\cite{moen2013distributional}.

The baseline experiment reproduces the results based on prior work i.e the per-label attention based CNN architecture~\cite{DBLP:conf/naacl/MullenbachWDSE18} and to do this, we use Word2Vec to generate word embeddings from the MIMIC training dataset. These embeddings are then used as an input to train the CNN neural network.

We investigated different "\emph{pre-initialization steps}" explained in Section.~\ref{sec:proposed_unstructured} and ran experiments by obtaining different input embeddings to train the neural model. Word embeddings trained on MIMIC discharge summaries and those trained on PubMed scientific articles are termed as $\textbf{Word2Vec-MIMIC}$ and $\textbf{Word2Vec-PubMed}$ respectively for our usage.

\begin{enumerate}
\vspace{-0.2cm} \item \textbf{MeanDiff-Word2Vec-MIMIC} Mean vector of the vectors in $\textbf{Word2Vec-MIMIC}$ removed from individual word vectors (Section.~\ref{sec:postprocess}).
\vspace{-0.2cm}\item\textbf{MeanDiff-PCADiff-Word2Vec-MIMIC} Above steps followed and extended by computing the principal components and removing the first $\textit{D}$ principal components from the mean removed word vectors. In our experiments, we consider $D$ = 2~\footnote{Prior work theoretically show that choosing D depends on the length of the embeddings} based on the embedding dimension i.e 200 which is chosen based on best performance.

\vspace{-0.2cm}\item \textbf{Averaging} Meta-embeddings obtained by combining  ($\textbf{Word2Vec-MIMIC}$) and ($\textbf{Word2Vec-PubMed}$) based on technique explained in Section. Both the vectors have the dimensions as 200.
\vspace{-0.2cm}\item \textbf{Locally Linear} Meta-embeddings are obtained by combining ($\textbf{Word2Vec-MIMIC}$) and ($\textbf{Word2Vec-PubMed}$) based on the technique explained in Section.~\ref{sec:meta_embeddings}. The nearest neighbours for each source is set to 1200 since this gives the best result based on prior work~\cite{DBLP:conf/ijcai/BollegalaHK18}. Here, the dimensions are set to 200.

\vspace{-0.2cm}\item \textbf{MeanDiff-Averaging} First, steps in (1) are carried out on $\textbf{Word2Vec-MIMIC}$ and $\textbf{Word2Vec-PubMed}$ separately and then, combined using the technique explained in (3).

\vspace{-0.2cm}\item \textbf{MeanDiff-PCADiff-Averaging} First, steps in (2) are carried out on  $\textbf{Word2Vec-MIMIC}$ and $\textbf{Word2Vec-PubMed}$ separately and then,
combined using the technique (3).

\vspace{-0.2cm}\item \textbf{MeanDiff-Locally Linear}  First, steps in (1) are carried out on  $\textbf{Word2Vec-MIMIC}$ and $\textbf{Word2Vec-PubMed}$ separately and then,
combined using the technique (4).

\vspace{-0.2cm}\item \textbf{MeanDiff-PCADiff-Locally Linear}
First, steps in (2) are carried out on  $\textbf{Word2Vec-MIMIC}$ and $\textbf{Word2Vec-PubMed}$ separately and then,
combined using the technique (4).
\end{enumerate}

Experiments were conducted using the same hyperparameters to train the baseline approach to benchmark our results against prior work.

\subsubsection{Multimodal data}
\label{sec:experiment_multimodal}
For the experiments we carried out for the structured data we trained 3 separate models. All three are tree-based models (Extreme Gradient Boosting). For each set of experiments, i.e., top 32 ICD-10 codes and top 50 ICD-9 codes  we run a randomised search for the best hyperparameters in a crossvalidation fashion. Their corresponding values are shown in Table~\ref{tab:xgboost32}.

\begin{enumerate}
\vspace{-0.2cm}\item \textbf{XGBoost Structured Data}
This approach utilised only information available in structured format in the MIMIC-III dataset. In particular, as mentioned above, we aggregate information at admission level.

\vspace{-0.2cm}\item \textbf{XGBoost Prescription Data}
This approach utilised only information from the prescription table in the MIMIC-III dataset. In particular, we aggregate drugs of a patient in an admission to a single row and apply TF-IDF technique to extract features. Our decision to exclude this from the unstructured data is because the result is not a comprehensible text but rather a bag of words.

\vspace{-0.2cm}\item \textbf{XGBoost Lab Exam Data}
Similar to the medications, lab exams include labels, fluids and categories,  describing the chemistry exams made, the type of fluid measurements that were taken and the overall category of that exam. The aim is to associate specific examinations with ICD codes. Again, due to the missing context, we treat this as a bag of words and apply TF-IDF to generate features.

\end{enumerate}

All three models are trained separately but on data concerning the same patients. All models make predictions for the same Test set as shown on Table~\ref{datasplit}.

\begin{table}
  \caption{Hyperparameters used for the XGBoost Algorithm for the top 32 ICD-10 and top 50 ICD-9 experiments}
  \label{tab:xgboost32}
  \begin{tabular}{cll}
    \toprule
    Hyperparameter & 32 ICD-10 & 50 ICD-9) \\
   \midrule
    Colsample by tree & 0.85 & 0.98 \\
    Gamma & 0.86 & 0.78\\
    Subsample & 0.66 & 0.67\\
    Number of estimators & 2000 & 2000\\
    Max Depth & 5 & 7\\
    Min Child Weight & 5 & 4\\
    Learning rate & 0.15 & 0.19\\
  \bottomrule
\end{tabular}
\end{table}

\subsubsection{Meta data}
\label{sec:metadata}
It is critical to ensure predictions from the different models are made on the same test set in order to train a meta-classifier namely logistic regression.
This classifier takes inputs from the three XGBoost models above (Section~\ref{sec:experiment_multimodal}) as well as the CNN mentioned earlier (Section~\ref{sec:experiment_unstructured}). We use a 5-Fold crossvalidation approach on the test set to measure the performance of our ensemble model.

\section{Results}
\label{sec:results}
\begin{table*}

\caption{Micro and macro results are presented for multilabel classification of 32 ICD-10 CODES on the test set. \emph{Input Embedding} refers to the different input embedding vectors that are fed into the CNN-based architecture.}
\centering
\footnotesize
 \begin{tabular}{l l l  l l  l l  l l l}
  \toprule
  \textbf{Input Embedding} & \textbf{Dim} &\multicolumn{2}{c}{Macro} &\multicolumn{2}{c}{Micro} &  \\
  \cmidrule{2-7}
  & & \textbf{F1} & \textbf{AUC} & \textbf{F1} & \textbf{AUC} & \textbf{P@8}\\
  \midrule
  \textbf{BASELINE}\\
  Word2Vec-MIMIC (CAML ~\cite{DBLP:conf/naacl/MullenbachWDSE18})  & 200 & 0.5554	& 0.8767 & 0.6749 & 0.9218 & 0.4034\\
  Word2Vec-MIMIC-MultiResCNN~\cite{li2020icd} & 200 & 0.2519 & 0.7195 & 0.4225 & 0.8126 & 0.3151 \\
  \midrule
  \textbf{POST-PROCESSED EMBEDDINGS} \\
  MeanDiff-Word2Vec-MIMIC & 200 & 0.5525 & 0.8749 & 0.6714 & 0.9205 & 0.4011

 \\
  MeanDiff-PCADiff-Word2Vec-MIMIC & 200 & 0.5325 & 0.8726 & 0.6749 & 0.9214 & 0.4021\\
  \midrule
  \textbf{META-EMBEDDING: Word2Vec-MIMIC, Word2Vec-PubMed} \\
  Averaging & 200 & 0.5750	& 0.8852
	& 0.6819	& 0.9248 & 0.4074\\
  Locally-linear & 200 & $\textbf{0.5957}$ & $\textbf{0.8940}$ & $\textbf{0.6840}$ & $\textbf{0.9280}$ & $\textbf{0.4088}$\\
  Locally-linear (MultiResCNN) & 200 &  0.4061 & 0.8139 & 0.5678 & 0.8813 & 0.3695 \\
  \midrule
  \textbf{META-EMBEDDINGS on POST-PROCESSED EMBEDDINGS} \\
  DiffMean + Averaging & 200 & 0.6144 & 0.9043 & 0.6923 & 0.9336 & 0.4135\\
  DiffMean-DiffPCA + Averaging & 200 & 0.6059 & 0.9004 & 0.6988 & 0.9341 & 0.4132 \\
  DiffMean + Locally Linear & 200 & \textbf{0.6212} & \textbf{0.9096} & \textbf{0.7043} & \textbf{0.9381} &\textbf{0.4163}\\
  DiffMean-DiffPCA + Locally Linear & 200 & 0.6205 & 0.9080 & 0.7014 & 0.9372 & 0.4160\\

  \bottomrule
 \end{tabular}

\label{resultunstructured32}

\centering
\caption{Micro and Macro results are presented for the multimodal multilabel classification of 32 ICD-10 codes.}
\footnotesize
 \begin{tabular}{l l l  l l  l l  l l }
  \toprule
  \textbf{Unstructured data features} &\multicolumn{2}{c}{Macro} &\multicolumn{2}{c}{Micro} \\
  \cmidrule{2-5}
   & \textbf{F1} & \textbf{AUC} & \textbf{F1} & \textbf{AUC}\\
  \midrule
  \textbf{BASELINE}\\
  - & 0.33211 & 0.6078 & 0.4521	& 0.6608\\
  Word2Vec-MIMIC & 0.5557 &0.7294 & 0.6771 & 0.7921\\
  \midrule
  \textbf{META-EMBEDDING:} \\
  \textbf{Word2Vec-MIMIC, Word2Vec-PubMed} \\
  Locally linear meta-embedding	& 0.5826 $\pm$ 0.0083 & 0.7972 $\pm$ 0.0028  & 0.6821 $\pm$ 0.0023 & 0.7436 $\pm$ 0.0017 \\

  DiffMean + Locally Linear & \textbf{0.6080} $\pm$ 0.0073 & \textbf{0.7593} $\pm$ 0.0022 & \textbf{0.7057} $\pm$ 0.0022  & \textbf{0.8120} $\pm$ 0.0016\\

   DiffMean-DiffPCA + Locally Linear & 0.6047 $\pm$ 0.0039 & 0.7577 $\pm$  0.0013 & 0.7030 $\pm$ 0.0025 & 0.8103 $\pm$ 0.0019 \\

  \bottomrule
 \end{tabular}

\label{resultstructured32}

\centering
\caption{Micro and macro results are presented for multilabel classification of 50 ICD-9 CODES on the test set. \emph{Input Embedding} refers to the different input embedding vectors that are fed into the CNN-based architecture.}
\footnotesize
 \begin{tabular}{l l l  l l  l l  l l l}
  \toprule
  \textbf{Input Embedding} & \textbf{Dim} &\multicolumn{2}{c}{Macro} &\multicolumn{2}{c}{Micro} & \\
  \cmidrule{2-7}
  & & \textbf{F1} & \textbf{AUC} & \textbf{F1} & \textbf{AUC} & \textbf{P@5}\\
  \midrule
  \textbf{BASELINE}\\
  Word2Vec-MIMIC (CAML ~\cite{DBLP:conf/naacl/MullenbachWDSE18}) & 200 & 0.5571 & 0.8693 & 0.6084 & 0.8910 & 0.5829\\
  Word2Vec-MIMIC-MultiResCNN (\cite{li2020icd}) & 200 & 0.3616 & 0.7643 & 0.5425 & 0.8492 & 0.3415\\
  \midrule
  \textbf{POST-PROCESSED EMBEDDINGS} \\
  MeanDiff-Word2Vec-MIMIC & 200 & 	0.5597 & 0.8620 & 0.5974 & 0.8848 & 0.5739
\\
  MeanDiff-PCADiff-Word2Vec-MIMIC & 200 & 0.5544 & 0.8577 & 0.6022 & 0.8799 & 0.5730 \\
  \midrule
  \textbf{META-EMBEDDING: Word2Vec-MIMIC, Word2Vec-PubMed} \\
  Averaging & 200 & 0.5663 & 0.8642	& 0.6132 & 0.8894 & 0.5882 \\
  Locally-linear & 200 & \textbf{0.5709} & \textbf{0.8666} & \textbf{0.6161} & \textbf{0.8914} & \textbf{0.5919}\\
  Locally-linear (MultiResCNN) & 200 & 0.4475 & 0.8159 & 0.5761 &  0.8849 & 0.3716 \\
  \midrule
  \textbf{META-EMBEDDINGS on POST-PROCESSED EMBEDDINGS} \\
  DiffMean + Averaging & 200 & 0.5665 & 0.8719 & 0.6079 & 0.8937 & 0.5863 \\
  DiffMean-DiffPCA + Averaging & 200 & 0.5489 & 0.8685 & 0.6049 & 0.8948 & 0.5631\\
  DiffMean + Locally Linear & 200 & 0.5730 & 0.8758 & \textbf{0.6224} & \textbf{0.9011} & \textbf{0.5732}\\
  DiffMean-DiffPCA + Locally Linear & 200 & \textbf{0.5740} &  \textbf{0.8760} & 0.6183 & 0.8967 & 0.5661\\

  \bottomrule
 \end{tabular}
\label{resultunstructured50}
\end{table*}
\begin{table*}
\centering
\caption{Micro and Macro results are presented for the multimodal multilabel classification of 50 ICD-9 codes.}
\footnotesize
 \begin{tabular}{l l l  l l  l l  l l }
  \toprule
  \textbf{Unstructured data features} &\multicolumn{2}{c}{Macro} &\multicolumn{2}{c}{Micro} \\
  \cmidrule{2-5}
   & \textbf{F1} & \textbf{AUC} & \textbf{F1} & \textbf{AUC}\\
  \midrule
  \textbf{BASELINE}\\
  - & 0.3940 & 0.6679 & 0.4662 & 0.6417 \\
  Word2Vec-MIMIC & 0.5457 & 0.7179 & 0.6078 & 0.7499\\
  \midrule
  \textbf{META-EMBEDDING:} \\
  \textbf{Word2Vec-MIMIC, Word2Vec-PubMed} \\
  Locally linear meta-embedding	& 0.5416 $\pm$ 0.012 & 0.7182 $\pm$ 0.0046 & 0.6077 $\pm$ 0.0096 & 0.7506 $\pm$ 0.0051\\

  DiffMean + Locally Linear & \textbf{0.5495} $\pm$ 0.0088 & \textbf{0.7528} $\pm$ 0.0059 & \textbf{0.6122} $\pm$ 0.012 & \textbf{0.7528} $\pm$ 0.0074 \\

  DiffMean-DiffPCA + Locally Linear & 0.5530 $\pm$ 0.010 & 0.7215 $\pm$ 0.0054 & 0.6113 $\pm$ 0.0113 & 0.7523 $\pm$  0.0076\\

  \bottomrule
 \end{tabular}
\vspace{-3mm}
\label{resultstructured50}
\end{table*}

Table.~\ref{resultunstructured32} contains the results based on the different experiments conducted using the proposed approaches on unstructured data (Section.~\ref{sec:proposed_unstructured}) for the multilabel classification of 32 ICD-10 codes. In particular, the baseline performance of CAML~\cite{DBLP:conf/naacl/MullenbachWDSE18} was better than the more complex MultiResCNN~\cite{li2020icd} for less epochs and hence, remaining results are based on the CAML architecture.

The best performance is achieved using the post-processing technique $\textbf{MeanDiff}$ on the different embeddings and then performing the locally linear meta-embedding technique~\cite{DBLP:conf/ijcai/BollegalaHK18} with the result indicating that post-processing was effective only on PubMed trained embeddings. A likely reason may be the inaccurate grammatical and semantic structure of discharge summaries in MIMIC dataset. Furthermore, the results indicate that the information captured by the local neighbourhood in each of the sources used for training meta-embeddings is important for boosting the performance. We find that post-processing word embeddings by removing principal components did not provide any improvement and investigating the reasons for this is out of scope for this paper.

We find that the meta-embeddings were able to capture the different semantic information from the different sources i.e. the discharge summaries and the external knowledge from PubMed articles. Meta-embedding techniques do not require the raw information used to originally train the embeddings but rather, the embeddings trained on this information is sufficient for training. This provides us an efficient way of improving the performance without adding to the complexity of the model.

Similar to Table.~\ref{resultunstructured32}, Table.~\ref{resultunstructured50} contains the results based on the different experiments conducted using the proposed approaches on unstructured data (Section.~\ref{sec:proposed_unstructured}) for the multilabel classification of top 50 ICD-9 codes. We can find similar trend in the performance based on the post-processing techniques followed by using the locally linear meta embedding technique. Overall, our proposed approach clearly outperforms the baseline performance.

Table.~\ref{resultstructured32} presents the results based on our experiments using the multimodal approach for predicting 32 ICD-10 codes and the methodology is explained in Section.~\ref{sec:multimodal}. From the results we can infer that, structured information on it's own does not provide a good performance in comparison with the baseline approach that makes use of the features from the baseline CNN model. Similarly, Table.~\ref{resultstructured50} shows that structured data alone has poor performance that is improved when ensembling with predictions from other models. Crucially, we show that the Locally linear meta-embeddings approach has the highest scores when ensembled with structured data.

Our aim is to understand whether we can enhance the prediction of structured information by capturing better probability scores using our proposed approach for unstructured information. Hence, from the results we can infer that the best performance on structured data is achieved by combining the probability scores of each label obtained using the best performance approach on the unstructured data. Overall, the results based on the unstructured data shows that our proposed approach is effective and outperforms the baseline approach. In addition, the results from our proposed approach enhances the performance of the multimodal multi-label classification.

\section{Limitations}
In this work, we did not focus on the interpretability of the results but rather we refer the reader to related work. Specifically, we would like to refer to previously tackled methods in ~\cite{DBLP:conf/naacl/MullenbachWDSE18} for predictions based on unstructured texts and ~\cite{xu2019multimodal} for multimodal data. Since, these interpretability methods are shown to be useful, we make an assumption that their validation holds for our experiments as well. Also, in this paper we do not attempt to improve on the current architecture~\cite{DBLP:conf/naacl/MullenbachWDSE18} but rather show the benefit of utilising better features as well as structured data.

\section{Conclusion and Future Work}
In this paper, we present a novel multimodal approach for predicting ICD-coding using unstructured information along with structured information. In particular, our proposed approach, which we term as \emph{pre-training initialisation steps} enhances the performance of the current
state-of-the-art model using unstructured information by effectively exploiting the geometric properties of pre-trained word embeddings as well as, combining external knowledge using meta-embedding techniques. We empirically show that our proposed approach can enhance the performance of current state-of-the-art approaches used for multilabel classification of discharge summaries in MIMIC-III dataset without relying on more complex architectures or using additional knowledge from the descriptions of ICD coding. In particular, post-processing word vectors and then, combining different pre-trained word embeddings using locally-linear meta-embeddings provides the best performance. In addition, we empirically show that unstructured information enhances the performance of the multimodal multi-label classification approach.

In future, we would like to investigate softer-measures of performing post-processing of pre-trained word embeddings such as using concept negators~\cite{DBLP:conf/aaai/LiuUS19}. We would also like to investigate the potential to exploit the hierarchical structure of the text present in discharge summaries using hyperbolic-based embedding vectors~\cite{DBLP:conf/aaai/LiuUS19} and also, combine different hyperbolic-based embeddings using meta-embedding techniques~\cite{DBLP:conf/rep4nlp/JawanpuriaDKM20}. Finally, we will experiment with more statistical features and other machine learning algorithms to assess whether we can further improve performance from the structured data.
\bibliographystyle{named}

redpajama_set_name: RedPajamaArXiv


text:
Category: Toronto-Maple-Leafs
Draft Weekend Brings Hope and Help, Turning One Season Into the Next
by Jon Jordan on 06/24/11 at 09:55 AM ET
The NHL Entry Draft is one of those annual "hope springs eternal" events. If the awards show bids farewell to a season gone by, the selection process for the latest prospect crop marks the league's transition into next year.
Just like that, every club starts anew.
Just like that, the page is turned for all.
The Boston Bruins reign as Stanley Cup champions has just begun and yet, as of tonight, even they have work to do. 29 other teams are now gunning for what they have. And for some, reshaping their franchises to make a run at hockey's iconic chalice will start with their first overall selection tonight.
Of course, it's been said that there aren't any immediate game-changers to be had among this crop – and certainly not beyond a half-dozen or so prospects atop most draft boards. But the draft itself has become such an integral aspect of building a championship-caliber team, even the late round selections will be a product of weeks and months of internal debate and study on the part of a team's decision-making hierarchy.
For other clubs, the leap from 2010-11 to 2011-12 will be aided during draft weekend by trade. This year, with the Philadelphia Flyers kicking things off with a pair of earth-rattlers a day in advance, shipping key components Mike Richards and Jeff Carter out of town in separate deals yesterday, the expectation is that trade activity in St. Paul could be high. As teams gear up for free agency next week, some will look to get a jump on crazy season by filling holes and altering their salary scales with a deal or two and there is a wealth of big-name talent reportedly on the trade market already.
Filed in: Anaheim Ducks, Atlanta Thrashers, Boston Bruins, Buffalo Sabres, Calgary Flames, Carolina Hurricanes, Chicago Blackhawks, Colorado Avalanche, Dallas Stars, Detroit Red Wings, Edmonton Oilers, Florida Panthers, Montreal Canadiens, Nashville Predators, New Jersey Devils, New York Islanders, New York Rangers, NHL Entry Draft, Ottawa Senators, Phoenix Coyotes, Pittsburgh Penguins, San Jose Sharks, Tampa Bay Lightning, Toronto Maple Leafs, Vancouver Canucks, Washington Capitals, Winnipeg Jets, |	Beasts of the Southeast | Permalink
This and That: On St. Pat's, Fadden Release, Vinny's Resurgence, Playoff Odds, DP's Return and More
by Jon Jordan on 03/17/11 at 02:02 PM ET
A happy St. Patrick's Day to the readers out there from yours truly! May you all be blessed with a plentiful helping of corned beef and cabbage, a pint or three of Guinness and, hell, a nip of Jameson for those of you feeling a wee bit adventurous! And, for the love of your favorite Irishman (or woman), please do avoid green beer at all costs. Dye the local pond, river or stream, if you must, but beer, my friends, should never be green!
A smorgasbord of news items and thoughts on each, in turn, for this fine Thursday afoot:
Farewell, Young Mr. Fadden
The troubling tenure of forward Mitch Fadden as a Tampa Bay Lightning prospect ends today upon his clearing unconditional release waivers and the termination of his contract. According to a report in the Salmon Arm Observer, Fadden, 22, was arrested in Salmon Arm earlier this month on charges of impaired driving, failing to provide a breath sample, dangerous operation of a motor vehicle and obstructing a peace officer. All of this, according to the report, after police observed Fadden to be "grossly intoxicated" at – wait for it – 12:57 PM and that he became "violent with officers" upon apprehension.
Yikes. Hope it was a good party.
Filed in: Buffalo Sabres, Carolina Hurricanes, Florida Panthers, Montreal Canadiens, New York Islanders, Pittsburgh Penguins, Tampa Bay Lightning, Toronto Maple Leafs, |	Beasts of the Southeast | Permalink
About Beasts of the Southeast
The Bolts Beat Returns to the Airwaves this Season on ESPN1040 in Tampa
Welcome, One and All
Update on Malone Surgery: "Should" be Ready for Training Camp
With Stamkos Re-Inked and Purcell on Deck, Bolts are Sitting Pretty
The Silliness of Summer
So, Yeah… About That Stamkos Deal…
Catching Up on What's Happened (and What Hasn't) for the Bolts This Summer
Great Fit for Smith in Phoenix
Ouellet Returns to Tampa

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text:
Need your record cleared?
Fraud / White Collar Crime
Grand Theft / Petty Theft
Making A Criminal Threat
Home of Guiding Hands Names Isaac Blumberg as Chairman of the Board of Directors
Isaac Blumberg was named Chairman of the Board of Directors of Home of Guiding Hands.
Via Oside News:
San Diego County CA – Home of Guiding Hands (HGH), serving more than 2,500 infants, adolescents and adults with developmental disabilities, has named Isaac Blumberg as its Chairman of the Board of Directors. Blumberg was named Board Member of the Year in 2015 and again in 2017 for providing advice, counsel and support using his legal skills. He also serves on the HGH's Capital Campaign Committee, which raised more than $1,180,000 during the past year.
See Full Article Here
San Diego Daily Transcript Top Attorneys 2015
Hatchet Attack – Judge Reduces an Assault Charge to a Misdemeanor
Preliminary Hearing for Hatchet-Wielding Suspect
Graveyard Damage Remains Unfixed
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text:
November 14, 2019 Girls soccer
Coginchaug's Isabel Milardo: The Ultimate Team Player
By Paul Augeri
DURHAM – Coginchaug sorely needed help in the defensive end this season, and it didn't take long for soccer coach Megan Kavanaugh to tell Isabel "Izzy" Milardo she was the one.
Milardo is a four-year varsity player and a Blue Devils co-captain. A natural forward, Milardo is a power runner, fast and physical, with a big right foot and a competitive streak. She is a threat to score each time Coginchaug mounts a rush in an opponent's end.
She also plays center field and bats leadoff for the softball team. In other words, she is an athlete, one who has value to her teams just by being on the field of play, regardless of position.
This year, Milardo began her final soccer season 15 goals shy of the school record for career goals established in 2012 by Victoria Buonanni. At the very least, the mark was within Milardo's reach.
Kavanaugh though, had to shore up the defense.
Contribute to the bigger picture? Or lobby to remain at forward, with a personal milestone at stake?
Milardo did not think twice.
"The team is definitely No. 1," she said. "I don't want to be selfish. I just go to wherever she needs me to go, whether it's goalie, defense, midfield, forward, wherever."
"She's not going to put her own kind of priorities in front of the team. And for the freshmen to see your senior captain is willing to play a position she doesn't know how to play? And is OK with that?"
— Coginchaug coach Megan Kavanaugh
Milardo's willingness to sacrifice a personal objective for the greater good surprised her coach not one bit.
"When I asked her to play in the back, she knew that that meant she might not be able to do that," Kavanaugh said. "She might have gone home and complained, I don't know, but she never complained or said anything in front of her teammates. And that was a big thing to ask her to give up (in order to) help her team out.
"And she never questioned it. She just did it. That, to me, is the most remarkable part of this whole thing."
Milardo has spent more time this season on defense than at forward for Coginchaug (10-6-1). The Blue Devils were a Class S semifinalist last year and hope to get back there this month. They advanced to the quarterfinals by beating Lyman Memorial 2-0 on Thursday.
"Outside back, up top if we are down or tied late in a game, defense, I played goalie for five minutes versus Portland — I've just had to do what the team needed from me," she said.
Isabel Milardo has 10 goals this season and 43 for her career, five shy of the school record established by Victoria Buonanni in 2012.
While her heart remains at forward, she has enjoyed learning a defensive role and relying on teammates to coach her on the field.
"She's a player who, when she's up top, everyone thinks she can score a goal," said Kavanaugh, who's spent 14 years as the Blue Devils' coach. "When we put her in the back, I think our back players and our goalkeeper (Claire DeFlora) felt a lot more comfortable knowing that she was in the back as opposed to me experimenting (with other options), because she's going to win the ball and she's going to do something positive with it even though she's not a defender by nature.
"So just her being on the field provided a lot of confidence for us."
In turn, senior Ashley Woodward moved up front and has been among the team's top goal scorers.
"I think Ashley stepping up front alleviated some of the fear that we weren't going to score goals," Kavanaugh said. "But if I could clone Izzy and put her in both spots, that would have been awesome."
Milardo scored her ninth goal of the season in Tuesday's Class S first-round win over MLC and added her 10th in Thursday's quarterfinal win, giving her 43 for her career, five behind Buonanni's school record. If she remained at forward full time, player and coach believe getting to 48 and beyond was more than doable.
"Our upperclassmen all knew that's just the kind of kid she is," Kavanaugh said. "She's not going to put her own kind of priorities in front of the team. And for the freshmen to see your senior captain is willing to play a position she doesn't know how to play? And is OK with that? Then seeing her do it made it a lot easier for them to do it (if asked)."
This wasn't the first time Milardo has changed positions. In her sophomore season, Kavanaugh needed a goalie. Milardo was up for it.
"I know she wasn't thrilled about it, because she wants to be up top and score goals, but she's never going to complain about that," Kavanaugh said. "She wants to be on the field and she wants to win, and she wants to do whatever it takes to make that happen."
Milardo believes her teammates were grateful for her readily embracing a new role for the betterment of the team, and she's just thankful for being a big part of the program.
"Coginchaug soccer has done a lot for me," she said. "I have definitely made some of my best friendships on this team. Being in this program has taught me how to work hard. Even during my freshman and sophomore seasons, when we didn't make the Shoreline or state tournament, we all worked as hard as we could."
"I'm very sad about this," she added. "I've been on the team for four years and this will be the last time I'll probably ever play soccer again. This program, and softball too, have brought out the best in me."
Come next season, when Kavanaugh doesn't have Izzy Milardo to lean on, she will draw on the example she set for those who come after her. For the betterment of the team.
"You get a lot of forwards who are good athletes who could play every position, but the mental part of knowing 'I'm giving up something really big for me in order to make my team better,' that was the part that is most impressive about her," Kavanaugh said. "She just gets it."
Coginchaugfeatured
Suite Experience: NY Jets Honor MHS Coach Morello
'Gave Everything We Had:' Blue Devils Lose In Quarters


text:
[ 21 January 2020 ] Students, faculty discuss perceptions of King, Malcolm X	Articles
HomeNewsCommunityA community in mourning
A community in mourning
A vigil was held for Kim Jones, who was shot and killed last Tuesday.
20 January 2015 Joe Brandt Community, News
Residents gathered at the intersection of 12th and Jefferson streets on Friday to mourn Kim Jones, who was shot and killed at the corner three days earlier. | Jenny Kerrigan TTN
Fifty-six-year-old Kim Jones, who worked helping abused children in collaboration with a nonprofit and the city's school district, who had just gotten married and was relishing in the achievement of receiving her MBA, was killed last week as she began her commute.
Jones was shot once in the head at 12th and Jefferson streets around 9:15 a.m. on Jan. 13 while waiting for a bus to her job at Turning Points for Children, an organization that aims to foster nurturing families and protect children from abuse.
When the gunfire rang out through Yorktown just two blocks from Main Campus, neighbors within earshot thought they heard a popping tire or other malfunction on the No. 23 SEPTA bus – few, if any, expected something more sinister. But there lay Jones, a woman they respected; gone in an instant, her blood running into the storm drain.
They mourned her in a Friday vigil at the site of her death and called for justice around a memorial of stuffed animals and candles below a sign that read 'PEACE' as an acronym, an anti-violence guideline behind each letter.
For the letter 'P': "Please stop the killing…let them live a healthy old age."
The memorial was dedicated "in loving memory to Mrs. Kim Jones and the many others that lost their lives due to crime."
Andre Jourden, Jones' 33-year-old son who resides in Jacksonville, Florida, spoke to the crowd at the vigil and with media afterward.
"My mother was a great person and she deserved a lot better than this," he told reporters. "She was 100 percent selfless. Her life was helping other people. She dedicated her life to that. She worked hard at Turning Points for Children, but she worked just as hard in the church soup kitchen. … it was her passion."
Jourden said the faith in his family – his mother was listening to gospel music before she died – helped in the mourning process.
"That's keeping a lot of people sane," he said, fighting back tears.
He urged the crowd to "speak up" if anyone had any information to help in locating his mother's killer.
According to Philadelphia CeaseFire, an anti-youth-violence advocacy organization in the School of Medicine which is located at 1700 N. Broad St., losing someone in a community can have adverse effects beyond the immediate family.
"Violence affects the security [of communities] tremendously," Robert Warner, program manager of Philadelphia CeaseFire, said. "Especially when a person like [Kim Jones] is killed, she could have sons or daughters. That could be my mother, or somebody else's mother … so it hurts the community a lot."
One resident asked the community to "please come out and support the family."
"I need you all out here to support them and let them know that we love them, and that [Kim Jones] will be missed," she said.
Kim's co-worker for more than 10 years, Mary Bernard, said the community would band together after what happened.
"We stick together, in good times and in bad times," she said through a megaphone provided by Philadelphia CeaseFire. "Whoever did this, they're not going to sleep right. They're never going to sleep right … but we are one … and we're going to keep being one."
As of Monday, police made public only that they could track Jones' suspected killer, through surveillance camera footage, to the Hunting Park subway station. But the trail goes cold there. There is not even certainty of the suspect's race or gender: while previously described as an African-American male, that description has since been withdrawn.
As seen in footage compiled by police, the ambiguous suspect, carrying a duffel bag, wore a black jacket and pants, an aviator cap with fur-trim flaps and white or silver Beats brand headphones.
After the murder, the suspect passed The Fresh Grocer and Morgan Hall before boarding the subway at the Cecil B. Moore station and riding it north.
The individual is considered armed and very dangerous. Police are offering a reward of $20,000 for his capture.
Anyone with information is asked to call Philadelphia Police at 215-686-3388.
Joe Brandt, Steve Bohnel and Lian Parsons can be reached at news@temple-news.com, 215.204.7419 or on Twitter @TheTempleNews.
Patrick McCarthy contributed reporting.
Joe Brandt
Looking for an answer
Second-half surge helps Owls top Cincinnati
Argentinian food, a family effort
24 February 2015 Eamon Dreisbach Features, Food
Known for its award-winning empanadas, Gavin's Café serves up traditional South American and European eats.
14 April 2015 Eamon Dreisbach Around Campus, Features, People You Should Know
Yasmine Mustafa founded ROAR for Good, a company that produces wearable self-defense devices for women.
Show tackles race in satirical play
14 April 2015 Nydja Hood Features, Theater
As part of Temple Theater Sidestage, "The Colored Museum" ran on campus this month.


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My first role in 27 years sans audition: Sooraj Thapar
Mumbai, July 10 (IANS) Veteran television actor Sooraj Thapar, who has joined the ensemble cast of television show "Ishaaron Ishaaron Mein", says this is the first role he has bagged without an audition or mock test in his show business career of 27 years.
"After 27 years I have bagged a show without any audition or mock shoot or look test. The producer of the show has known me for some time and wanted me to take up this part," Sooraj said.
The actor is known for his work in shows such as "Sasural Genda Phool", "Ek Nayi Pehchaan" and "Chhal - Sheh Aur Maat".
In "Ishaaron Ishaaron Mein", Sooraj will be seen playing lead actress Simran Pareenja's father.
Talking about his character, he said: "I play a rich industrialist who wishes to gain the best match for his daughter, and his love and protective nature towards his daughter changes to obsession.
"The role is different from the ones I have done in the past and I am looking forward to playing it," Sooraj added.
"Ishaaron Ishaaron Mein" will air on Sony Entertainment Television from July 15.
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Fantastic home with an amazing location! Just 2 blocks to walk to the beach, only 6 blocks to casinos, and only 5 blocks to walk to the Gondola for spectacular skiing at Heavenly Ski Resort! All of your vacation needs are within walking distance of this home in addition to the fact that you can sleep 9 people, has a private hot tub, and will allow pets (fenced in yard) with an additonal refundable deposit. Great home in a great place!