Continuity of care is crucial to ensuring positive health outcomes for patients discharged from an inpatient hospital setting, and improved information sharing can help. To share information, caregivers write discharge notes containing action items to share with patients and their future caregivers, but these action items are easily lost due to the lengthiness of the documents. In this work, we describe our creation of a dataset of clinical action items annotated over MIMIC-III, the largest publicly available dataset of real clinical notes. This dataset, which we call CLIP, is annotated by physicians and covers 718 documents representing 100K sentences. We describe the task of extracting the action items from these documents as multi-aspect extractive summarization, with each aspect representing a type of action to be taken. We evaluate several machine learning models on this task, and show that the best models exploit in-domain language model pre-training on 59K unannotated documents, and incorporate context from neighboring sentences. We also propose an approach to pre-training data selection that allows us to explore the trade-off between size and domain-specificity of pre-training datasets for this task. Transitioning patient care from hospitals to primary care providers (PCPs) can frequently result in medical errors (Kripalani et al., 2007). When patients are discharged, they often require further actions to be taken by their PCP, who manages their long-term health, such as reviewing results for lab tests once they are available (Moore et al., 2007). Yet PCPs often have many patients and little time to review new clinical documents related to a recent  Equal contribution  Work done while at ASAPP. Discharge notes are typically lengthy (Weis and Levy, 2014) and written as free text, so PCPs may fail to identify important pending actions, which inadvertently leads patients to poor outcomes. Spencer et al. (2019) found that PCPs considered the lack of a standardized follow-up section to be a key driver in missing follow-up action items. While discharge notes may include follow-up sections, they are typically aimed at the patient and not curated for PCP use. Jackson et al. (2015) found that following up on pending clinical actions is critical for minimizing risk of medical error during care transitions, especially for patients with complex treatment plans. Automatic extraction of action items can make physicians more efficient by reducing the high cognitive load and time-consuming burden of using electronic health records (Tai-Seale et al., 2017; Sinsky et al., 2016; Singh et al., 2013; Farri et al., 2013). To our knowledge, there has been little previous work using machine learning to address this important clinical problem. Potential impact Successful automatic extraction of action items can have several direct benefits. First, it can improve patient safety by fostering more comprehensive and complete care by PCPs. Second, it might make physicians more efficient at performing a comprehensive review of action items, which is critical as physicians spend an increasing amount of time interacting with electronic health record (EHR) systems (Tai-Seale et al., 2017; Sinsky et al., 2016). Further, reviewing and synthesizing lengthy or complicated patient histories places a significant cognitive load on physicians, which has been associated with increased medical error (Singh et al., 2013; Farri et al., 2013), so reducing this cognitive load is an area of opportunity. Finally, a working system might integrate with EHRs to au-ActionType Description Example Appointment Appointments to be made by the PCP, or monitored to ensure the patient attends them. tomatically address certain action items such as scheduling appointments, thereby improving EHR usability and further reducing medical error. Contributions We introduce a new clinical natural language processing task that accomplishes focused information extraction from intensive care unit (ICU) discharge notes by selecting sentences that contain action items for PCPs or patients. An action item is a statement in a discharge note that explicitly or implicitly directs the reader to an action that should be taken as a result of the hospital stay described in the document. Given a discharge note, the task is to extract all action items in the note. We cast this task as a special case of multi-aspect document summarization, with each aspect representing an area of patient care to monitor or on which to take action (see examples in Table 1). We create the first annotated dataset for this new task, CLIP, a dataset of 718 annotated notes from MIMIC-III (Johnson et al., 2016), comprising over 100K annotated sentences. This will be, to our knowledge, one of the largest annotated datasets for clinical NLP, which tend to be smaller due to the expense of expert annotators. We evaluate machine learning methods to tackle this task. Similar to prior work on multi-aspect extractive summarization, we employ sentence-level multi-label classification techniques (Hayashi et al., 2020). Our proposed architecture consists of passing a sentence, and its neighboring sentences on its left and right, through a pre-trained BERT model (Devlin et al., 2019) with minor modifica-tions. Since there is limited annotated data but a wealth of unlabeled in-domain clinical notes, we also explore the impact of unsupervised learning on this task. We develop a method for task-targeted pre-training data selection, in which a model trained on the downstream task selects unlabeled document segments for fine-tuning a BERT model. We find that this focused pre-training is much faster than pre-training on all available data and achieves competitive results. Our results show that unsupervised pre-training of any form is critical to improving results. Our code is available as open-source software 1 , and our annotations are available via PhysioNet 2 , to fully enable reproduction of our results and to provide a benchmark for evaluating future advances in clinical NLP in the context of this clinically 1 https://github.com/asappresearch/clip 2 As they are built on top of MIMIC-III, which PhysioNet maintains, access to our annotations requires the completion of an ethics course and a Data Use Agreement. Clinical information extraction There has been a wealth of previous work on extracting information from clinical notes, much of which also follows an extractive summarization approach. For example, Were et al. (2010) extracts items such as patient smoking status and obesity comorbidities from discharge notes. Liang et al. (2019) created a hybrid system of regex-based heuristics, neural network models trained on pre-existing datasets, and models such as support vector machines for disease-specific extractive summarization. Liu et al. (2018b) developed a pseudo-labelling, semi-supervised approach, using intrinsic correlation between notes, to train extractive summarization models for disease-specific summaries. We differ from these efforts in that we do not aim to generate general-purpose or disease-specific summaries, rather we focus on extracting specific action items from discharge notes to facilitate care transfer. Clinical datasets Datasets and challenges on the extraction of medication, tests, and procedure mentions in clinical text (Uzuner et al., 2010, 2011; Jagannatha et al., 2019) have been released, but without the focus on providing actionable insight to PCPs. Additionally, multiple datasets (Uzuner et al., 2012; Sun et al., 2013) have been introduced for detecting temporal and co-reference relations between parts of a note. While it may be useful for a model to have a good grasp of co-reference and temporal dependencies to understand what constitutes actionable information for a PCP, we choose to optimize directly for the end task, noting recent work demonstrating that modern pre-trained neural networks will identify and exploit such information as needed (Tenney et al., 2019). Although on different tasks, we note that our dataset of 718 annotated documents is larger than recently released datasets, such as those from the n2c2 shared tasks. For comparison, 500 documents were annotated for adverse drug event extraction (Henry et al., 2020a), 150 documents for family history extraction (Liu et al., 2018a), and 100 documents for clinical concept normalization (Henry et al., 2020b). One of the largest annotated clinical datasets, emrQA, is built on 2,425 clinical notes (Pampari et al., 2018). Summarization Prior summarization work, which we build on, uses pre-trained transformer models to construct sentence representations that are contextualized with the entire document (Liu and Lapata, 2019; Hayashi et al., 2020). Liu and Lapata (2019) evaluate on three benchmark summarization datasets consisting of news articles. They are shorter, with average document lengths from 400-800 words, whereas MIMIC-III discharge notes average over 1,400 words. Liu and Lapata (2019) evaluate with ROUGE scores standard in summarization, whereas we take advantage of having ground truth extracted sentences and evaluate with classification metrics, providing a substantially different task. Liang et al. (2019) develop a disease-specific summary dataset, but it is not public and their methods involve combining a mix of outputs from models performing auxiliary tasks such as concept recognition, adverse drug event extraction, and medication change detection, each of which have to be individually developed and maintained. In this section, we describe the process of creating our CLIP dataset, short for CLINICALFOLLOWUP, and report statistics on the dataset. CLIP is created on top of the popular clinical dataset MIMIC-III (Johnson et al., 2016). The MIMIC-III dataset contains 59,652 critical care discharge notes from the Beth Israel Deaconess Medical Center over the period of 2001 to 2012, among millions of other notes and structured data. We annotated 718 randomly sampled discharge notes from the set of patients that were discharged from the ICU (i.e., survived) and thus brought back to the care of their primary care physician or relevant specialists. Though this dataset is orders of magnitude smaller than general summarization datasets such as Nallapati et al. (2016), we note the relatively large expense associated with clinical annotation due to both the length of documents (  160 sentences on average) and the requirement of domain experts. This dataset is also the first of its kind in the clinical space. The total number of sentences is 107,494, of which 12,079 have at least one label. The sampled MIMIC-III data is further split randomly into training, validation, and test sets, such that all sentences from a document go to the same Patient Instructions 6.55% Appointments 4.59% Medications 1.88% Lab tests 0.69% Procedures 0.28% Imaging 0.18% Other 0.05% Table 2: Prevalence of each label type in CLIP training set. Our dataset was annotated by four physicians and one resident over the course of three months. We underwent several rounds of initial annotations with calibration processes and instruction refinement in between. Additional annotation details are provided in the appendix and the full guidelines are available on our public repository. We estimated inter-rater reliability by having two physician annotators independently annotate a set of 13 documents comprising 2600 sentences. Comparing predictions on a binary reduction of the task, in which a match indicates that both annotators labeled a sentence (regardless of chosen label types), we measured a Cohen's kappa statistic of 0.925. The seven action item aspects that we labeled in the dataset, along with example discharge note snippets for each one, are presented in Table 1. To emphasize the subtlety and complexity of this task, we highlight here some example rules that state what should not be annotated. For the appointment label, we should exclude sentences that refer to as needed appointments, e.g. See your endocrinologist as needed.; this describes no deviation from status quo behavior and thus does not warrant follow-up action. For the medication label, we specifically exclude sentences describing simple additions to the medication list, e.g. Dis-charged on glargine 10u at bedtime, as these typically do not require further action. However we include instructions to hold and restart medications, new medications with an end date (e.g. antibiotics), and medications requiring dosage adjustment (e.g. ...the plan is to keep patient off diuretics with monitoring of his labs and reinstitution once the kidney function improves), as these are likely to require PCP action. Due to the large amount of discharge note text that has information not directly actionable for followup, most sentences remain without a label after the annotation process; 11.2% of training set sentences have a label. Of the sentences with labels, 28.6% have multiple labels. Table 2 shows the frequency of each label type at the sentence level in the training set. To distinguish the contribution of our dataset in the context of existing text summarization datasets, we performed a manual quantitative comparison between CLIP and the summarization datasets CNN (Hermann et al., 2015) and WikiASP (Hayashi et al., 2020). For WikiASP, we chose sentences from the Event genre of summary, as our dataset describes hospital stays which could be considered events. Inspired by Suhr et al. (2017), we identified five phenomena to compare across datasets quantification (in the numerical sense, as in 300 mg or twenty-three people), temporal expressions, conditional expressions, imperative mood or second-person statements, and out of vocabulary (OOV) terms 3 . We gathered 100 sentences from the summaries of each dataset and counted the occurrences of each phenomena. We see that CLIP has a relative wealth of imperative and second-person statements, which is not surprising due to the prevalence of patient-directed language in Patient instructions-labeled sentences. CLIP and WikiASP both have more temporal expressions than CNN, which are contained in around half of the sample sentences of each. Despite the prevalence of clinical jargon in CLIP, WikiASP actually contained the most OOV words, perhaps due to the diversity of sources of that dataset. Conditional language, such as If you miss any doses of this medication, your stents could clot off again..., were uncommon in all datasets but occurred most in CLIP. With a discharge note as input, the task is to output the clinically actionable follow-up items found 3 By OOV, we mean any token that must be split into multiple WordPiece tokens given the vanilla BERT vocabulary. For example, the common abbreviation for patient, pt, becomes p, ##t. We will evaluate our experiments on this multilabel classification formulation, as well as on a binary reduction of the problem in which the objective is to simply identify which sentences have any type of label. This binary framing is still useful, as surfacing the sentences for a reader is the primary objective that will save time and effort, with classification of the sentence being a secondary benefit. within the note. There are many summarization methods that could appropriately handle this problem. The length of these documents and the high relative risk of missing information in a clinical setting discourages the option of truncating documents to fit into modern neural network models which may have maximum length requirements, so we develop methods that approach the task as multilabel sentence classification. Summarization of a full document can then be accomplished with the resulting model by feeding each sentence into the model in sequence and aggregating the sentences that the model labels. The BERT architecture (Devlin et al., 2019) has been widely used within clinical NLP in the past year with successful results (Lee et al., 2020; Alsentzer et al., 2019; Mulyar et al., 2019; Johnson et al., 2020; McDermott et al., 2020; Zhang et al., 2020). In particular, Si et al. (2020) has shown the effectiveness of BERT for use on small annotated clinical datasets, such as the one we develop. We use BERT as the basis for our proposed model. BERT-based baselines To demonstrate baseline BERT performance, we fine-tune pre-trained BERT models on our task. As the simplest approach, we feed a sentence into BERT, take the hidden state of the [CLS] token as the sentence-level representation, and train a linear layer over that representation. To adapt BERT to our domain, we also experiment with a previously released version of BERT which has been further pre-trained on MIMIC-III discharge notes (Alsentzer et al., 2019), and fine-tune it on our task in the same way. We refer to this variant as MIMIC-DNOTE-BERT. Alsentzer et al. (2019) also release a version pretrained on all MIMIC-III notes, which we refer to as MIMIC-FULL-BERT. Both MIMIC-Full-BERT and MIMIC-DNote-BERT are initialized with BioBERT (Lee et al., 2020), which is pretrained on a corpus of biomedical research articles. Incorporating neighboring context Surrounding contexts are critical for the task, for two reasons: 1) an individual sentence may not have the full picture on the type of the action; 2) neighboring sentences tend to share the same label (occurs for 27% of sentences). So, we incorporate context beyond an individual sentence into our BERT-based sentence representations, by concatenating the two sentences each that immediately precede and follow the sentence to the input. To do this, we follow the encoder architecture of Liu and Lapata (2019), which concatenates sentences with special tokens and applies alternating segment embeddings to alternating sentences. We make the following mod-ifications: we exclude the additional transformer layers on top of the BERT output, use only SEP tokens to separate sentences, and apply the segment embedding SA to the tokens in the focus sentence and SB to all other tokens, as pictured in Figure 1. We initialize models of this architecture with various pre-trained BERT parameters in experiments. Given the limited amount of annotated data, we are motivated to pursue semi-supervised approaches. We seek to explore the trade-off between generalized and domainor task-specific data for language model pre-training, by introducing a technique for targeted pre-training which we call T askT argeted P re-training (TTP). TTP requires less data and computation, yet attains comparable performance to pre-training on large in-domain datasets that prior work studied (Alsentzer et al., 2019). The goal of this approach is to surface unlabeled sentences that may be positive examples, in the vein of self-supervision techniques such as Snorkel (Ratner et al., 2017). In contrast to Snorkel, which uses model predictions to generate pseudolabels to train with, TTP uses model predictions to select sentences for pre -training, using auxiliary tasks. To create a task-targeted dataset, we first fine-tune a vanilla BERT model on our task, and then we use the learned model to classify all unlabeled sentences. We select all sentences that the model predicts as having action items, using a fixed threshold. Due to the multi-label nature of our task, we apply the threshold across all labels and select sentences in which at least 1 label score is above the threshold. The threshold used to select the task-targeted sentences can be tweaked to create datasets for pre-training that are smaller and more task-focused (for higher thresholds), or larger and more general (for lower thresholds), which we experiment with. This approach is inspired by and similar to task-adaptive pre-training (TAPT) introduced by Gu-rurangan et al. (2020). In that work, a pre-trained bag-of-words language model encodes sentences in labeled and unlabeled datasets, and for each labeled sentence selects its nearest neighbor unlabeled sentences according to the model. In this paper, we select data points using the full prediction model (rather than just an encoder), and use threshold-ing which provides maximal control over the size of the selected dataset. Further, directly applying TAPT to our case may not work well as it does not distinguish positive and negative samples in the in-domain dataset, so the surfaced sentences from TAPT may be less relevant. Our approach benefits from using an encoding method that is trained on the task we are targeting. After selecting data, we pre-train a BERT-Context model on the targeted dataset, pulling in neighboring sentences of the targeted sentences. As auxiliary tasks, we used masked language modeling (MLM) and a sentence switching task (Wang et al., 2019). For MLM, we mask tokens in the context sentence only, independently with probability 0.15. For sentence switching, with probability 0.25 we swap the focus sentence with another randomly chosen sentence from the same document, and predict whether the focus sentence was swapped using the context sentences. Cross entropy losses for both tasks are computed and summed to compute the total loss for an instance. These tasks encourage the model to learn how to incorporate information from the context sentences into its representation. Figure 1 depicts the entire process. This process can be repeated, by using the final resulting model to then select a new set of sentences for pre-training, however we did not experiment with this as one iteration was enough to produce competitive results. We first generate synthetic surrogates for entities redacted during de-identification, apply a custom sentence tokenizer adapted from open-source software 4 5 to tokenize the document into sentences, and lower case every sentence. Discharge notes in MIMIC often have semi-structured sections, with headers denoting them, e.g. BRIEF HOSPITAL COURSE: , which the tokenizer is built to identify. Using TTP, we select pre-training datasets of sizes  250K,  500K,  1M, and  2M sentences from the set of MIMIC-III discharge notes. As baselines, we train a TF-IDF-weighted bag-of-words logistic regression model with L1 regularization and a max-pooling 1-D convolutional neural network (CNN). The CNN is initialized with BioWordVec vectors (Zhang et al., 2019; Chen et al., 2019), which are trained on PubMed and MIMIC-III notes, and the CNN is trained with the binary cross-entropy (BCE) loss. All BERT-based models are loaded, pre-trained as appropriate, and fine-tuned using the transformers library (Wolf et al., 2019), using BCE loss, and backpropagating and applying gradient updates through all of BERT's parameters. We used library default parameters, except for the batch size which we adjusted to 32 based on validation set performance and training stability. All neural models are trained with early stopping on the macro-averaged 4 https://github.com/fnl/syntok 5 https://github.com/wboag/mimic-tokenize AUROC metric. Early stopping is also applied to the pre-training step, using the loss on an unlabeled held-out set as the criterion. We report results on the test set using microand macro-averaged metrics common in multilabel classification, and F1 for the binary reduction of the task. Micro-averaged metrics treat each (sentence, label) pair as an individual binary prediction, and macro-averaged metrics compute the metric per-label and then average these results across labels. For binary F1, we transform the label and model predictions into binary variables indicating whether any type of label was predicted for the sentence, and then calculate metrics, ignoring whether the types of the predicted labels were accurate. To ensure the fairest comparison between models and eliminate some arbitrariness in results that may arise when training on imbalanced data and evaluating with a fixed 0.5 threshold, we also tune thresholds for each label such that its F1 score on the validation set is maximized. For micro metrics, we choose the threshold that provides the highest micro F1 score. We then apply these thresholds when evaluating on the test set. The main set of results are reported in Table 4. Models pre-trained with TTP have the size of their Model Patient Appt Medication Lab Procedure Imaging Other Bag-of-words 0.741 0.792 0.546 0.625 0.302 0.343 0.236 CNN 0.759 0.824 0.595 0.629 0.315 0.431 0.228 BERT 0.780 0.855 0.635 0.719 0.415 0.474 0.275 MIMIC-DNote-BERT 0.783 0.854 0.656 0.741 0.524 0.567 0.294 MIMIC-DNote-BERT+Context 0.830 0.882 0.659 0.744 0.597 0.567 0.349 TTP-BERT+Context (250k) 0.841 0.887 0.668 0.745 0.548 0.566 0.365 Table 5: Average balanced F1 scores on the test set for each label across 10 runs. pre-training dataset denoted in parentheses. BERT and both MIMIC-BERT models outperform the logistic regression and CNN baselines. The results using MIMIC-DNote-BERT demonstrate the importance of domain-specific pre-training; it improves in all metrics over BERT. Using neighboring sentences, as we do in -Context models, also provides a performance boost across all metrics save for Macro AUC, comparing MIMIC-DNote-BERT to MIMIC-DNote-BERT+Context. To compare with human performance, our inter-annotator agreement on the binary task, measured in terms of F-1, was 0.930, and the highest mean binary-F1 from the model evaluations approaches 0.86. When using just 250,000 sentences from the MIMIC discharge notes for pre-training (TTP-BERT-Context 250K), task results are competitive with and in some cases exceed MIMIC-DNote-BERT+Context, which is pre-trained on all MIMIC discharge notes, which contain 9M sentences. Our TTP approach is able to complete domain-specific pre-training within  12 hours, while Alsentzer et al. (2019) report a pre-training time of 17-18 days for MIMIC-Full-BERT. We next investigate results on each label (see Table 5), for a subset of models. The in-domain pre-training for MIMIC-DNote-BERT models provides gains for nearly all label types, and including context also gives a boost to the F1 score of most labels. All models perform poorly predicting the Other label, which encompasses a long tail of many different types of follow-ups which we did not further categorize, making modeling difficult. Imaging and Procedure label performance lags others, likely due to their lower prevalence (Table 2). We examine errors made by TTP-BERT-Context (1M), focusing on false negatives, the most costly type of error in this use case. Inspection of the test set with physician input yields two high-level phenomena of the data that occur repeatedly in error cases: clinical jargon / knowledge, and temporal expressions / conditional language. Clinical jargon Perhaps the most obvious drawback of applying general-purpose language models to clinical language data is that clinical language is heavily laden with clinical jargon, abbreviations, and misspellings. Although the WordPiece tokenization used by BERT-based models can tokenize any input, the more separation of clinical terms happens, the more model capacity is reduced, as lower layers in BERT have to learn how to combine the meaning of the WordPieces into word-level representations. We observed several cases in which even common clinical jargon may have interfered with the model's performance in an otherwise unambiguous sentence. Bolded words are OOV's: <-please take medications as directed -follow up with pcp mark carter using> , <plan for repeat chest xray pa/ lat and lordotic view to reevaluate when returns 12-18 for wound check> . Many cases of this type of error also suggest that a lack of explicit clinical knowledge could be a barrier, in addition to the technical issue of WordPiece tokenization. In this example promethazine is a drug that can be prescribed for a short defined period: 3 . promethazine 25 mg tablet sig : 0.5 tablet po q6h ( every 6 hours ) as needed for nausea . In the following example, the procedures described are required but do not need an appointment, and the model erroneously applied the Appointment label: however , the patient will need aggressive pulmonary toilet including good oral suctioning care and chest pt as pt is at risk for aspiration . Temporal expressions The model may also struggle with temporal expressions, which are common especially in the Medication label type. This label is intended to surface cases of medications that need to be tweaked, started, or stopped after a specified time period. Example: ...you should go back to your regular home dosing of 20 units in the morning and 24 units at dinner time after completing your prednisone . While many training examples gave explicit durations (e.g. for 14 days), many of the false negative examples described dependencies between future patient actions, including with conditional if statements. Example: if he needs further management he may do well with clonidine . Our results show that the common regime of fine-tuning a large pre-trained model is a useful method for our task of extracting clinical action items. Additionally, we investigated the trade-off between task-specificity and pre-training data size, and found our task-targeted pre-training method enables one to navigate this trade-off, producing models with comparable performance on the end task that require less data for pre-training. While trading off these concerns may not be needed if effective public models exist for a given task, we believe this technique is useful in scenarios in which users have large, domain-specific, private datasets and specific tasks in mind. This is often the case for healthcare institutions and developers of clinical machine learning software, as privacy concerns tend to preclude data sharing between institutions. From a modeling perspective, there are many possible avenues for future work. Taking a structured prediction lens and leveraging sentence-level label dependencies or applying structured prediction models could be helpful, although Cohan et al. (2019) note that CRF layers did not improve their performance for a sequential sentence classification task. We acknowledge that our sentence classification approach is a simplification of the more general span detection problem, and this approach could bring improved precision by focusing on which parts of sentences matter, which may be important as we found that sentence tokenization was non-trivial for clinical notes. Finally, the question of whether such an approach to follow-up workflow augmentation is successful in increasing patient safety, clinician effi-ciency, or EHR usability is an empirical one. We hope to evaluate in the future whether a highlighted note such as one these models could provide will reduce the time a physician takes to, for example, answer certain questions about a patient's hospital stay. In alignment with recent calls for increased rigor in the evaluation of machine learning-derived clinical decision support systems (Kelly et al., 2019), future work should include further prospective, controlled evaluation of the generalizability, stability, interpretability, unbiasedness, usability, and efficacy of this approach. We hope that our dataset and initial model development can lay the groundwork for future investigation. We introduce the task of detecting clinical action items from discharge notes to help primary care physicians more quickly and comprehensively identify actionable information, and present the CLIP dataset, which we will release to the community. Given perfect performance, this would reduce the number of sentences a PCP may need to read by 88%. The best model's binary F1 is near 0.9, compared to the human benchmark of 0.93. These models could additionally be used for clinical research. For example, a calibrated model could derive statistics for how often each type of action item is seen for different patient populations, which can provide insight into typical patient or PCP burden after hospital discharge. We evaluated BERT-based models that incorporate multi-sentence context, and introduced a novel task-targeted pre-training approach that can reduce pre-training time while maintaining similar performance to models pre-trained on much larger in-domain datasets. The models have promising results, however we anticipate there is still room for improvement, particularly for the rare labels. We encourage the clinical NLP community to further investigate the problem of detecting action items from hospital discharge notes, which can help improve reliably safe transitions of care for the most vulnerable patients. We thank our team of physician annotators for their fruitful collaboration and the reviewers for their comments which improved this paper.
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