Identifying sections is one of the critical components of understanding medical information from unstructured clinical notes and developing assistive technologies for clinical note-writing tasks. Most state-of-the-art text classification systems require thousands of in-domain text data to achieve high performance. However, collecting in-domain and recent clinical note data with section labels is challenging given the high level of privacy and sensitivity. This paper proposes an algorithmic way to improve the task transferability of meta-learning-based text classification in order to address the issue of low-resource target data. Specifically, we explore how to make the best use of the source dataset and propose a unique task transferability measure named Normalized Negative Conditional Entropy (NNCE). Leveraging the NNCE, we develop strategies for selecting clinical categories and sections from source task data to boost cross-domain meta-learning accuracy. Experimental results show that our task selection strategies improve section classification accuracy significantly compared to meta-learning algorithms. An important part of Electronic Health Records (EHRs) is the digitized clinical notes that contain the medical and treatment histories of patients. The section of clinical notes can be defined as a text segment that clusters consecutive sentences with relevant content of one dimension of a patient's health encounter (Pomares-Quimbaya et al., 2019). Clinical note sections, labeled with either headings or subheadings, make the notes well organized and offer improved clinical information extraction (Wang et al., 2018b). However, many clinical notes contain narratives that are in an unstructured free-text  Work done during an internship at Amazon Care  Work done while Ram Bhakta was a researcher at Amazon Care in 2021, and Ram is now affiliated to Oath Care, Austin, USA format, (e.g., History of Present Illnesses described in paragraph form), which makes it challenging to retrieve and utilize this information. In the United States, physicians generally spend an excessive amount of time interfacing with EHRs and computerized physician order entry (CPOE) workflows in their aftercare work, resulting in burnout, low job satisfaction, and system-wise inefficiencies (Pa-tel et al., 2018). An automated section classifier can play a key role in mitigating this problem. In some cases, section classification serves as an end task of automatic report segmentation. For example, according to an internal survey we conducted with Amazon Care providers, we found evidence that classifying sentences related to the History of Present Illness from medical encounters can greatly assist providers with their documentation. For computer-assisted report generation, understanding clinical notes from an unstructured format is an important data pre-processing (Gopinath et al., 2020). There are some challenges for clinical note section classification in practice. First, it is difficult to collect and access a large amount of in-domain data. Second, section types and medical contents within a section substantially vary depending on care providers, which makes it hard to utilize open-source datasets. Even though some sections exist in multiple different sources, their contents vary across clinical categories. For example, the Diagnosis section for Nutrition specialty and Rehabilitation Service specialty vary in types of content. Recently developed neural network language technologies capture rich contextual information in sentences. Among them, Bidirectional Encoder Representations from Transformers (BERT) achieved significant improvements in multiple Natural Language Processing (NLP) tasks, establishing strong baselines in low-resource scenarios (De-vlin et al., 2019). However, there remains room for performance improvement because BERT uses 6690 source data  data outside of in-domain or target-domain data  in an unsupervised training fashion only. Another approach for low-resource in-domain NLP tasks is Multi-Task Learning (MTL). The MTL adopts shared text encoding layers across all tasks while the top layers are task-specific for each dataset (Liu et al., 2015, 2019). The target task with limited data benefits from the knowledge learned from source tasks. Instead of MTL, which minimizes the loss of the source tasks, Dou et al. (2019) proposed a model-agnostic meta-learning algorithm that finds optimal model parameters for better adaptation capability to new tasks. In classification tasks, Nichol et al. (2018) proposed Reptile, an optimization-based meta-learning algorithm for section classification, and achieved comparable accuracy on well-established benchmarks on low resourced image datasets. In the present paper, we adopted these methods as strong baselines in our experiments and computed the relative performance improvement of our method. Task transferability denotes how easy it is to transfer the representation learned from one task to another task (Tran et al., 2019; Nguyen et al., 2020b). It helps discover the relationship between two types of tasks and provides supporting evidence for developing transfer learning strategies. Task transferability becomes more useful in realistic situations where the assumption of the meta-learning, which is that data of the target task can be drawn from the distribution of the source tasks, does not hold. One common example is that there are outlier tasks' in the training (source) tasks, which are dissimilar from the testing (target) ones (Venkitaraman et al., 2020). For this problem, good selection of relevant source tasks can benefit knowledge transfer to unseen tasks (Zamir et al., 2018; Achille et al., 2019; Nguyen et al., 2020a). In clinical section classification, we suppose how close a source task is toward the target task is determined by its specialty and the section types included. However, few studies of task transferability estimation have discussed the function of each label. Thus we propose an information-theoretic metric for task transferability, namely Normalized Negative Conditional Entropy (NNCE). The NNCE score is calculated by the classifier of a source task and target data samples without training on the target task, thus saving expensive computation for model optimization. We hypothesize that this score correlates with how well the source data labels (sec-tions) distinguish the target labels. Leveraging the NNCE, we explore strategies of source task selection to improve the performance of meta-learning. The goal is to make the best use of available data from various clinical specialties for any target tasks. Specifically, we explore two strategies: 1) category selection we select a subset of clinical categories that are relevant to the target task; 2) section selection for a clinical category, we filter out the samples of certain section types which are not relevant to the target task and merge similar sections by assigning the same label. The category selection is informed directly by the best NNCE scores. For section selection, however, there are too many combinations, and it is time-consuming to train models for every possible task and find optimal ones. To handle that, we apply a backward selection method for heuristic search. The experiment results show that our task selection strategies improve the meta-transfer learning of section classification in low-resource scenarios. Our work has the following contributions:  We apply the meta-learning for clinical section classification at sentence level in low-resource scenarios utilizing out-of-domain datasets. We propose a task transferability metric for selecting the source tasks relevant to the target tasks by category and section selection, which improves meta-learning performance. We evaluate a computationally efficient backward selection method for section selection and show that it leads to a better knowledge transfer. To the best of our knowledge, this is the first attempt to apply class subset selection to improve the task transferability in the NLP field. The goal of this paper is to address the automated clinical section classification task in low-resource scenarios. Notable early work focused on the extraction of frequency-based features and classified the sections of the clinical narratives with traditional machine learning approaches, including Support Vector Machines (Apostolova et al., 2009), 6691 Maximum Entropy (MaxEnt) models (Tepper et al., 2012) and Bayesian models (Ganesan and Subotin, 2014). Li et al. (2010) framed section mapping as a sequence-labeling problem and adopted a Hidden Markov Model (HMM). Dai et al. (2015) formulated the task as a token-based classification using the conditional random fields (CRF) model. Ni et al. (2015) applied active learning and distant supervision to the section classification. In the study of Tran et al. (2015), the tasks were performed by an object-based section annotator using an ontology to describes the relationship among the section concepts. However, most of the studies above investigate the section classification task for a single domain without exploring how to transfer knowledge from the source dataset to an unseen target domain with limited data. Recently, Rosenthal et al. (2019) leveraged the data from medical literature and performed section classification at the sentence level via transfer learning, recurrent neural networks (RNNs), and BERT in scenarios where a limited amount of in-domain training data was available. This work performs simple transfer learning and only predicts the shared sections across different clinical categories, and in practice, most section labels are domain-specific. This paper applies meta-learning and task transferability to transfer information learned from the source category to the target category with a new section classification task. Meta-learning aims at fast adaptation to new tasks with small amounts of data through learning knowledge from multiple source tasks. Among different approaches to meta-learning, one proposal is learning the initialization of a network that is good at adapting to new jobs. Dou et al. (2019) applied this proposal to the General Language Understanding Evaluation (GLUE) benchmark (Wang et al., 2018a) and explored the model-agnostic meta-learning (MAML) (Finn et al., 2017) and its variants called first-order MAML (FO-MAML) and Reptile. In this paper, we adopted the Reptile algorithm that achieved the best performance in (Dou et al., 2019). Previous work explores the relationship between classification tasks on task similarity using traditional machine learning algorithms (Thrun and O'Sullivan, 1996; Bakker and Heskes, 2003; Xue et al., 2007; Zhang and Yeung, 2010). Other recent work mapped the functions into a vector space (Achille et al., 2019, 2021) to estimate the transferability using a non-symmetric distance. Vu et al. (2020) further developed the task embeddings approach and applied it to the NLP field to predict the most transferable source tasks. Zamir et al. (2018) modeled the underlying structure among different tasks to reduce the number of labeled training data. However, the common theme in all these approaches is that they require fine-tuning the target task and exhaustive optimization of parameters. The transferability estimation, unfortunately, is not robust if there are insufficient training samples. Moreover, none of these algorithms have discussed label selection which is crucial for task selection in clinical section classification. Tran et al. (2019) investigated the correlation of the label distributions between those tasks and proposed a negative conditional entropy (NCE) measure to estimate the task transferability. This algorithm only requires the source model and the labeled target samples without fine-tuning the in-domain data. Nguyen et al. (2020b) developed a variant of NCE measure called the Log Expected Empirical Prediction (LEEP) that denotes the average log-likelihood of the expected empirical predictor. Our proposed NNCE is similar in concept to NCE and LEEP. However, we apply the class subset selection to improve the knowledge transfer. Unlike previous work (Manjunatha et al., 2018), which does not use knowledge about the target task while finding the subset, our approach incorporates how the decision boundary of each source label distinguishes the labels of the target task. We conduct experiments on the Medical Information Mart for Intensive Care III (MIMIC-III) database (Johnson et al., 2016), a large open-access dataset of de-identified patient records. We collected data from 9 different clinical categories of MIMIC-III and randomly picked 200 clinical notes for each. There are nearly 1,000 section labels of these categories, and most of them contain very few sentence instances. To handle the sparsity, we only keep the section types of each category satisfying the following conditions:  The section is among the ten most frequent ones. Table 1 shows the number of sentence instances and the lists of selected section types. The section list varies across categories, with only a few section labels in more than one domain. However, some sections in different categories are still related to each other. For example, sentences in the social history section of Discharge Summary Reports' category are similar to the instances in the employment status and previous living situation section of Social Work'. We adopt Reptile, an optimization-based meta-learning algorithm, to be our baseline approach. Assume we have a set of source tasks { T 1 , T 2 ,..., TN } from multiple open-resource clinical datasets. We perform the Reptile with these source tasks to learn the BERT model parameters  to provide a good initialization for fine-tuning the target task. For sampling batches of tasks, we use the same strategy proposed in Dou et al. (2019) that the probability of selecting a task is proportional to the size of its dataset. The training procedure of Reptile is described in Algorithm 1 where  denotes learning rate. In the baseline meta-learning approach, we train the model with all the available datasets without data selection which might suffer from outlier' tasks. In the next step, we leverage the task transferability estimation for selecting the sources tasks bettering transferring knowledge to the target task. Fig. 1 shows the general framework of NNCE. The motivation of the NNCE for estimating the task transferability is the idea of evaluating how well the decision boundaries of source labels distinguish the target labels. Consider a source task defined on X  Y and a target task on X  Z . We denote the target samples as D = { ( x 1 , z 1 ) , ( x 1 , z 2 ) , ..., ( x n , z n ) } and use y  Y = { 1 , 2 , ..., LS } and z  Z = { 1 , 2 , ..., LT } to represent the label variables of source and target data respectively. We train a classifier f on the source task which maps the space X to Y . By feeding the target samples into the source model f , we assign the predicted source labels for the target samples so that  Y = {  y 1 ,  y 2 , ...,  y n } . Thus, every target sample is attached with a true 6693 Figure 1: NNCE measure. label' from Z and a predicted label from Y that can be denoted as ( x i ,  y i , z i ) . We compute the empirical joint distribution and the empirical marginal distribution by  P ( y ) = 1 n n (cid:88) i =1 1 {  y i = y } ,  P ( z ) = 1 n n (cid:88) i =1 1 { z i = z } ,  P ( y, z ) = 1 n n (cid:88) i =1 1 {  y i = y, z i = z } . (1) To measure how the source and task labels are related, we handle the class imbalance issue of the target dataset by normalizing the target class frequency: (cid:101) P ( y, z ) =  P ( y | z ) =  P ( y, z )  P ( z ) . The value of (cid:101) P ( y, z ) represents the ratio of the target samples in class z that are assigned with the predicted label y . Then we compute: (cid:101) P ( z | y ) = (cid:101) P ( y, z ) (cid:80) LT z (cid:101) P ( y, z ) . so that (cid:80) z (cid:101) P ( z | y ) = 1 . We suppose that a good source label y = l that distinguishes the target labels well should have large values of (cid:101) P ( z = l | y ) for some target classes as well as small values for other target classes. On the contrary, if the values of (cid:101) P ( z = l | y ) for different target class z are approximately equal, this label is useless for classifying the target labels. Based on that, we define the NNCE to estimate the task transferability by: Figure 2: Category selection example. NNCE = LS (cid:88) y Y  P ( y ) LT (cid:88) z Z (cid:101) P ( z | y ) log (cid:101) P ( z | y ) = LS (cid:88) y Y  P ( y ) E ( y ) (4) where we use E ( y ) = (cid:80) LT z Z (cid:101) P ( z | y ) log (cid:101) P ( z | y ) to estimate how well the decision boundary of a source label classifies the target classes and NNCE is the overall measurement weighted by the prior  P ( y ) . NNCE score is always negative. For a determined target task, a larger score indicates better transferability between the source and target tasks. The advantage of NNCE over some other label correlation methods like LEEP is that it allows us to select the source labels better distinguishing the target class with respect to E ( y ) . The NNCE is related to the NCE proposed by Tran et al. (2019), and it is equal to NCE if we do not normalize the target class frequency in Equation (2). The proof is in the Appendix A. 4.3 Task Selection for Clinical Section Classification We suppose that selecting the source tasks with good task transferability can benefit the meta-learning of the low-resource target task. In clinical section classification tasks, the pattern of the data and the section types vary across categories. So we propose two approaches for choosing the source tasks category selection and section selection. The procedure of category selection is direct. Fig. 2 shows a simple example of category selection. We compute the NNCE score for each of the source tasks from different clinical categories. Then we pick the N best' categories whose task achieves 6694 Figure 3: A single step for section selection. the highest NNCE scores. This approach helps filter out the outlier' tasks by removing the clinical categories irrelevant to the target task. Section selection is a process of searching for the optimal task for each of the clinical categories. It aims to make the best use of the section labels to benefit transferring knowledge to the target task. We modify the list of the section classes by deleting the instances from the useless sections and merge similar ones. However, there are too many combinations for partitioning that lead to high computational costs. To reduce the computational complexity, we propose a backward selection method with three operations for heuristic search. We delete the section l  of the source dataset with the smallest value of empirical marginal distribution  P ( y ) : The motivation behind this operation is that the fewest target samples are tagged with source label l  representing this section is unrelated to the target category. We delete the section l  satisfying: From the demonstration in Section 4.2 we can conclude l  has the smallest value of E ( y ) , which indicates the source section l  is worst at distinguishing the target sections. This operation aims to find the closest' pair of the source sections and merge them into one. To find such sections i  , j  , we adopt the following equation: i  , j  = argmin i,j i (cid:54) = j JSD ( (cid:101) P ( z | y = i ) (cid:107) (cid:101) P ( z | y = j )) (7) where JSD (  ) presents the JensenShannon divergence (Lin, 1991). A small value of JSD (  ) indicates that the (cid:101) P ( z | y = i  ) and (cid:101) P ( z | y = j  ) distribute closely and the source sections i  and j  are similar. In this case, the decision boundary between the source label i  and j  are trivially helpful for discriminating the target labels. We initialize the source task by including all the samples and sections labels, and perform a backward selection algorithm to reduce the section num-bers iteratively. Fig. 3 shows a single step of this process. We apply the NNCE measure with three operations introduced before to generate NNCE scores and produce no more than three new tasks 1 . Then we compute the NNCE score for each of the new tasks. The final picked task at this step is the one that achieves the highest scores among the original one and the newly generated ones. We keep 1 Different operations may result in the same task. performing this process until none of the produced tasks improves the NNCE score anymore. We carry out the experiments with four target tasks of different clinical categories Discharge Summary Report', Nursing Progress', Recab Service Progress' and Social Work' presented in Table 1. For the target task of Social Work', we utilize all the other eight categories for pre-training. For Discharge Summary Report', Nursing Progress' and Recab Service Progress', we remove their close categories Discharge Summary Addendum', Nursing Generic' and Recab Service Evaluation' categories, respectively, and the pre-training is performed by the remaining seven categories. For each target categories, we split the samples into the training and testing set with a roughly 3:1 ratio across the SUBJECT_ID' referring to a unique patient. We randomly pick 200/500/1000 samples from each target datasets to simulate low-resource scenarios and perform BERT, MTL, and Reptile for the clinical section classification. We adopt the PyTorch (version 1.3.0) implementation of BERT 2 for our tasks and the model is initialized with BERT-base . The settings of MTL and Reptile are same as the ones described in (Dou et al., 2019). We threshold the word sequence length to 80, which covers more than 99% of the sentences. We use Adam (Kingman and Ba, 2015) for optimization and a batch size of 32 for all the 2 https://github.com/huggingface/pytorch-pretrained-BERT Figure 4: Convergence of accuracy of fine-tuning for sample size=200. experiments. For both MTL and Reptile, the learning rate is 5e-5, and the number of pre-training epoch is 5. We set the inner update step k to be 5, the inner learning rate to be 5e-5 and the number of sampled tasks in each step to be 8 for Reptile. For BERT fine-tuning, we train the model with the learning rate of 2e-5 for 25 epochs. The classification accuracy results of BERT, MTL, and Meta-learning for different tasks are shown in Table 2. From the table, we find that both MTL and Reptile improve the performance of the low-resource target task while Reptile outperforms multi-task learning and achieves the best results. The comparison between BERT and Reptile demonstrates that the meta-learning approach can benefit the fine-tuning of the target task. The improvement is more significant when we perform the classification task with fewer target samples. Fig. 4 shows the convergence of accuracy of BERT fine-tuning with and without Reptile pretraining. The curves in these figures suggest that meta-learning has the advantage of fast convergence and adapts to the new task more quickly. We also discover that after 15 epochs of fine-tuning, the performance is not sensitive to the epoch number. For any selected target category, we pre-train the model for each of the remaining categories and fine-tune with 200 target samples to obtain the transfer learning accuracies. We compute the NNCE scores for different source tasks and evaluate the NNCE by 6696 Target category Correlation coefficients NCE NNCE Discharge Summary Report 0.671 0.676 Nursing Progress 0.772 0.807  Rehab Service Progress 0.918 0.922 Social Work 0.479 0.703   The correlations between the NNCE scores and transfer learning accuracy are statistically significant with p < 0 . 05 . Table 3: Comparison of Pearson correlation coefficients of NCE and NNCE(Tran et al., 2019). the Pearson correlation coefficients between these scores and their accuracies of adaptation. We also report the correlations using the NCE scores for comparison. By comparing the correlation coefficients presented in Table 3, we find that NNCE receives higher correlations over NCE for all the tasks and is better at task transferability estimation. We set the target sample size to be 200 and explore how task selection strategies category selection and section selection benefit meta-learning. Table 4 shows the results of meta-learning approach with category selection. We report the classification accuracies of picking N = 2/4/6 categories with the highest NNCE scores and compare with including all the source categories. The results reveal that the category selection improves the meta-learning performance, and there is an optimal value of N for each task. If N is too large, it might include outlier' tasks that degrade the performance. If N is too small, it loses the benefit of utilizing large amounts of source data. We also perform the category selection with NCE to compare it with NNCE. The underlined tasks in Table 4 indicate that different subsets of categories are selected if we replace NNCE with NCE. For all these tasks, NNCE achieves higher accuracies. Please see Appendix C for detailed results for different target categories. We discuss whether the section selection benefits the meta-learning in two scenarios. First, we compare the performances of Reptile with and without section selection using all the source categories. In the second scenario, we repeat the first procedure but only use the best subset of the source categories determined in Table 4, and repeat the comparison method in the first scenario. The comparisons presented in Table 5 indicate that adopting section se-Task Nb. 05 . However, the improvement is not statistically significant for most tasks. The average relative gains to Reptile brought by the category selection and section selection are 1.5% and 0.8%, which indicates that category selection contributes more to improving the meta-learning. We also find that combining both category and section selection results in better performance than using each of them independently for most tasks. We show an example in Table 6 to further illustrate section selection. The source and target categories are Rehab Service Progress' and Nursing Progress', and the original section types are presented. The labels in blue are the selected sections, and the merged ones are displayed inside the brackets. We observe that the common section types plan and assessment are kept. Although the content of the same section type is different across categories, there are similarities between their utterance patterns. The source sections in black are irrelevant to any of the target sections, so they are removed. The merged sections balance and gait are of close concepts, both of which describe the patient's progress of mobility. This example shows 6697 that the selection procedure extracts information of the source sections related to the target sections, which benefits the knowledge transfer. In this paper, we explored the clinical section classification with limited in-domain data. We applied a meta-learning algorithm utilizing multiple out-of-domain clinical datasets, improving the classification accuracy and adaptation speed. We proposed a Normalized Negative Conditional Entropy measure to estimate the task transferability and leverage it to select the clinical categories and sections related to the target task that best improves knowledge transfer. In addition, we examined a backward selection method to reduce the computational complexity of section selection. Our study suggests that both category selection and section selection outperform the baseline meta-learning approach, and combining two strategies results in better performance than adopting each of them independently. Future work will look to develop a joint optimization of category selection and section selection. We also plan to apply our approach to other styles of text data. For example, section classification on spoken utterances of doctor-patient conversations is an exciting extension of the present work, which we plan to explore (Krishna et al., 2021). Finally, we will continue to apply the proposed method to other text processing applications, e.g., medical information retrieval (Goeuriot et al., 2016).
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