MIMIC ause: R epresentation and automatic extraction of causal relation types from clinical notes (2022)

Abstract: Understanding causal narratives communicated in clinical notes can help make strides towards personalized healthcare. Extracted causal information from clinical notes can be combined with structured EHR data such as patients’ demographics, diagnoses, and medications. This will enhance healthcare providers’ ability to identify aspects of a patient’s story communicated in the clinical notes and help make more informed decisions. In this work, we propose annotation guidelines, develop an annotated corpus and provide baseline scores to identify types and direction of causal relations between a pair of biomedical concepts in clinical notes; communicated implicitly or explicitly, identified either in a single sentence or across multiple sentences. We annotate a total of 2714 de-identified examples sampled from the 2018 n2c2 shared task dataset and train four different language model based architectures. Annotation based on our guidelines achieved a high inter-annotator agreement i.e. Fleiss’ kappa ( \kappa ) score of 0.72, and our model for identification of causal relations achieved a macro F1 score of 0.56 on the test data. The high inter-annotator agreement for clinical text shows the quality of our annotation guidelines while the provided baseline F1 score sets the direction for future research towards understanding narratives in clinical texts.

## introduction electronic health records (ehrs) have significant amounts of unstructured clinical notes containing a rich description of patients' states as observed by healthcare professionals over time. our ability to effectively parse and understand clinical narratives depends upon the quality of extracted biomedical concepts and semantic relations. the contemporary advancements in natural language processing (nlp) have led to an increased interest in tasks such as extraction of biomedical concepts, patients' data de-identification, medical question answering and relation extraction. while these tasks have improved our ability for clinical narrative understanding, identification of semantic causal relations between biomedical entities will further enhance it. identification of novel and interesting causal observations from clinical notes can be instrumental to a better understanding of patients' health. it can also help us identify potential causes of diseases and determine their prevention and treatment. despite the usefulness of identification and extraction of causal relation types, our capability to do so is limited and remains a challenge for specialized domains like healthcare. the nlp community has been actively working on causality understanding from text and has proposed various methodologies to represent @xcite @xcite , as well as extract @xcite @xcite @xcite , causal associations between the events expressed in natural language text. in the healthcare domain, most of the related work can be grouped around the problem of adverse drug effect identification from biomedical scientific articles @xcite or clinical notes @xcite @xcite , and identification of cause, effect and their triggers @xcite . there is no work that has yet tried to represent different types of causal associations along with direction (between biomedical concepts) communicated in clinical notes. in this work, we fill the gap by defining types of semantic causal relations between biomedical entities, building detailed annotation guidelines and annotating a large dataset. figure 1 shows a snippet of clinical note extracted from the n2c2 dataset @xcite , different sets of annotated biomedical entities along with the causal relationship between them, and the corresponding narrative based on the proposed guidelines outlined in section 3.1. even with the inherent complexities of clinical text data (e.g., domain knowledge, short hand by doctors, etc.), following our proposed guidelines, we achieved a high inter annotator agreement of fleiss' kappa (κ) score of 0.72. ## related works in linguistics, the focus on representing causality has been on understanding interactions between events. @xcite proposed force-dynamics to decompose the causal interaction between events as "letting", "helping", "hindering" etc. @xcite built upon force-dynamics by incorporating the theory of causal verbs and proposed the dynamicmodel of causation. wolff categorised causation in three categories, "cause", "enable" and "prevent", and provided a set of causal verbs to express these categories. @xcite proposed because corpus to represent linguistic expressions of causation stated explicitly. because 1.0 @xcite consists of a cause span, an effect span, and a causal connective span. their work treats the causal connectives e.g. because of, so etc. as the "centerpiece" of causal language, impacting the se-lection of instances to be annotated. in addition to the types of causation (consequence, motivation, and purpose) and degrees of causation (facilitate and inhibit) introduced in because 1.0, the subsequent work because 2.0 @xcite extended the annotation scheme to include overlapping relations other than causal. in contrast, our work focuses on both explicit (indicated by connectives) and implicit (lack of connectives) identification of types of causal associations between biomedical concepts as communicated in clinical notes. more another interesting decomposition of causation is proposed by @xcite as a necessary and sufficient condition, but such detailed information is seldom communicated in clinical notes. there have been several other recent attempts of modeling and extracting causality from unstructured text. @xcite created a causality dataset using the wall street journal corpus and captured the directionality of causal interaction with simple temporal relations (e.g., before, after, no-rel) but did not focus on the types of causality between the events. the work of gorman et al. on richer event description (red) @xcite describes causality types as cause and precondition and uses negative polarity to capture the context of hinder and prevent. this is in line with the annotation guidelines proposed in our current work, but we also defined explicit hinder and prevent causality types along with directionality. @xcite proposed the use of explicit linguistic markers, i.e., clinks (due to, because of, etc.) to extended timeml tlinks @xcite based temporal annotations to capture causality between identified events. the resulting dataset had temporal as well as casual relations but still lacks the causality types between events. @xcite proposed the use of binary questions to extract causal knowledge from unstructured text data but did not focus on types and directionality of causal relations. more recently, @xcite used language models combining event descriptions with events' contexts to predict causal relationships. their network architecture wasn't trained to predict the type or directionality of causal relations. furthermore, they removed the directionality provided in semeval-2007 @xcite , and @xcite datasets to evaluate their model on a larger causal relation dataset. our causality extraction network is built upon their methodology, i.e., causal-bert but also focuses on directionality as well as types of causality communicated in clinical notes. although causality lies at the heart of biomedical knowledge, there are only a handful of works (mostly adverse drug effect (e.g. gurulingappa et al. 2012)) extracting causality from biomedical or clinical text data. @xcite proposed tasks to extract concepts, assertions, and relations in clinical text. in their dataset, drugs and procedures are combined as a single concept, i.e., treatment and the defined relations are also dependent upon the concept types under consideration. whereas, the relations defined in our work are based on the overall context in any given example and make no assumption about the type of concepts/entities under consideration. another interesting work is biocause by @xcite , which annotates existing bio-event corpora from biomedical scientific articles to capture biomedical causality. instead of identifying the types (and direction) of causal relations in the already provided events of interest, they are an-notating two types of text spans, i.e., arguments and triggers. arguments are text spans that can be represented as events with type cause, effect, and evidence while trigger spans (can be empty) are connectives between the casual events. our work proposes comprehensive guidelines to represent the types and direction of causal associations between biomedical entities, expressed explicitly or implicitly in the same or multiple sentences in clinical notes, and is not covered by any related work. ## mimicause dataset creation we used publicly available 2018 n2c2 shared task @xcite dataset on adverse drug events and medication extraction to build the mimicause dataset. the n2c2 dataset was used because it is built upon the de-identified discharge summaries from the mimic-iii clinical care database @xcite and has nine different annotations of biomedical entities e.g. drug, dose, ade, reason, route etc. the types of biomedical concepts/entities with a few examples as defined in the n2c2 dataset are shown in however, the provided relationships in the n2c2 dataset are simply defined by the identified concepts linked with related medications and hold no semantic meaning. to create the mimicause dataset, we extracted foot_0 examples from each entitypair available in the n2c2 dataset. our final dataset has 1107 "ade-drug" , 1007 "reason-drug" and 100 from each of "strength-drug", "form-drug", "dosage-drug", "frequency-drug", "route-drug" and "duration-drug" entity-pair examples. ## problem definition and experiments we defined our task of causality understanding as the identification of semantic causal relations between biomedical entities as expressed in clinical notes. we have a total of 2714 examples annotated with these 9 different classes (8 causal and 1 non-causal). ## conclusion in this work, we proposed annotation guidelines to capture the types and direction of causal associations, annotated a dataset of 2714 examples from de-identified clinical notes and built models to provide a baseline score for our dataset. even with the inherent complexities in clinical text data, following the meticulously defined annotation guidelines, we achieved a high interannotator agreement, i.e., fleiss' kappa (κ) score of 0.72. building various network architectures on top of language models, we achieved a macro f-1 score of 0.56. an end-to-end nlp pipeline built with models for patients' data de-identification, biomedical entity extraction, and causal relations identification between various biomedical entities will be instrumental in narrative understanding from clinical notes. in the future, we are planning to extend our annotation guidelines to jointly annotate temporal and causal relations to capture the ordering of various causal interactions between biomedical entities over time.