M ed D ec: A Dataset for Extracting Medical Decisions from Discharge Summaries (2024)

Abstract: Medical decisions directly impact individuals’ health and well-being. Extracting decision spans from clinical notes plays a crucial role in understanding medical decision-making processes. In this paper, we develop a new dataset called “MedDec,” which contains clinical notes of eleven different phenotypes (diseases) annotated by ten types of medical decisions. We introduce the task of medical decision extraction, aiming to jointly extract and classify different types of medical decisions within clinical notes. We provide a comprehensive analysis of the dataset, develop a span detection model as a baseline for this task, evaluate recent span detection approaches, and employ a few metrics to measure the complexity of data samples. Our findings shed light on the complexities inherent in clinical decision extraction and enable future work in this area of research. The dataset and code are available through https://github.com/CLU-UML/MedDec.

## introduction clinical notes contain rich information about medical decision-making. such notes document patient conditions, medications, laboratory and diagnostic results, assessments and plans, prognoses, and follow-up information, among other crucial data points. however, automatic knowledge extraction from clinical notes has been challenged by imprecise clinical descriptions, heterogeneous data, and the need for data annotation. in particular, although there exist comprehensive and manually verified taxonomies of medical decisions @xcite , and successful information extraction techniques in medical @xcite @xcite @xcite , and general @xcite @xcite @xcite domains, mr. [..] is a 61 y/om with hiv and hcv and hemophilia [defining problem] ... with suspicion for diffuse neoplastic process of the liver [defining problem] ... admitted for biopsy [therapeutic procedure related] he was treated with interferon [drug related]... he has had multiple imaging that showed multliple focal lesions that were not previously seen [evaluating test result] ... past medical history: hemophilia [defining problem] -followed by dr [..], drs [..] and [..] ** [contact related] figure 1 : an example excerpt from a de-identified clinical note in meddec, where text spans are annotated into 10 medical decision categories defined by the decision identification and classification taxonomy for use in medicine (dictum) @xcite . color-coded texts represent medical decisions and their annotated decision categories are in @xcite . automatic extraction of medical decisions from clinical notes has the potential to transform clinical practice. it can inform the development of evidence-based decision-making guidelines and stewardship programs, identify potential deviations from best decision-making practices, and determine potential risks to patient based on prior medical decisions and their outcomes. in addition, beyond clinical applications, understanding clinical decision patterns can inform health policy development and refinement, especially when evaluating the impact of particular interventions or policies. this paper develops the first expert-annotated dataset for medical decision extraction and classification within discharge summaries (meddec). it is developed using patient data sourced from the medical information mart for intensive care (mimic-iii) @xcite , which is a publicly available dataset of de-identified clinical data of patients who were treated in intensive care units (icus). meddec contains annotated decisions in 451 discharge summaries, covering more than 54k sentences and containing diverse patient groups based on sex, race, and english proficiency. in addition, 187 out of the 451 discharge summaries were previously classified into eleven phenotypic (main disease) categories through manual annotation by @xcite . we extend the dataset as follows: all medical decisions in the discharge summaries are annotated by domain experts according to the decision identification and classification taxonomy for use in medicine (dic-tum) @xcite . dictum covers ten (10) medical decision categories listed in in addition, we introduce the new task of clinical decision extraction, which involves identifying and classifying spans of medical decisions within relatively long clinical notes. this focused information extraction task contributes to the advancement of bionlp techniques and has the potential to improve healthcare. we develop several baselines including span detection and named entity recognition models, and evaluate and analyze their performance on meddec. in addition, we introduce a framework for medical decision extraction to set a baseline for future research. the contributions of this paper are: ## learning to extract medical decisions we present an overview of our approach and describe its two key components in subsequent sections. figure 2 shows our approach for extracting and classifying medical decisions. first, a long note is chunked into segments of acceptable length to the model. each segment is fed into the model to generate hidden representations and token classification probabilities (this step can be batched for efficiency). then, the resulting labeled text sequences are concatenated to obtain classification results for the entire clinical note. finally, we postprocess the results to convert them into spans, defined by a start position, end position, and category. ## conclusion we developed meddec for the extraction and classification of ten types of documented medical decisions in discharge summaries of eleven different phenotypes (diseases). we demonstrate several baseline models to tackle this task. through extensive experiments and analysis, we find that the task is challenging, the performance of the bestperforming model significantly varies across phenotypes and the spectrum of sample complexity. the dataset can be useful in studying population statistics, biases in medical treatment, analysis of medical decisions for different phenotypes, and understanding medical decision-making processes.