Resources

This dataset was designed and created to enable advancements in healthcare-focused large language models, particularly in the context of retrieval-augmented clinical question-answering capabilities. Developed using a self-constructed pipeline based …

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. The MedDec dataset includes expert-annotated decisions of te…

EHRNote-ChatQA is a benchmark for evidence-grounded, multi-turn clinical question answering over longitudinal discharge summaries. Derived from de-identified MIMIC-IV data, it contains 967 patient-level samples covering one to five discharge summari…

Existing question answering (QA) datasets derived from electronic health records (EHR) are artificially generated and consequently fail to capture realistic physician information needs. We present Discharge Summary Clinical Questions (DiSCQ), a newl…

MIMIC is one of the most widely used public hospital datasets for machine learning on electronic health records [3], but its structured tables answer many clinical questions only through coded surrogates, or not at all. For example, a patient's past…

Opioid use disorder (OUD) is underdiagnosed in health system settings, limiting research on OUD using electronic health records (EHRs). Medical encounter notes can enrich structured EHR data with documented signs and symptoms of OUD and social risks…

The development of large language models tailored for handling patients’ clinical notes is often hindered by the limited accessibility and usability of these notes due to strict privacy regulations. To address these challenges, we first create synth…

The National Institutes of Health Stroke Scale (NIHSS) is a 15-item neurologic examination stroke scale. It quantifies the physical manifestations of neurological deficits and provides crucial support for clinical decision making and early-stage eme…

We created a dataset of clinical action items annotated over MIMIC-III. This dataset, which we call CLIP, is annotated by physicians and covers 718 discharge summaries, representing 107,494 sentences. Annotations were collected as character-level sp…

Databases that link molecular data to clinical outcomes can inform precision cancer research into novel prognostic and predictive biomarkers. However, outside of clinical trials, cancer outcomes are typically recorded only in text form within electr…