Resources

This dataset comprises 9,606 Synthetic Discharge Summaries generated by GPT-3.5 based on combinations of ICD-10-code descriptions associated with real discharge summaries in MIMIC-IV. As part of the generation process, GPT-3.5 was also tasked to cod…

Discharge summaries in Electronic Health Records (EHRs) are crucial for clinical decision-making, but their length and complexity make information extraction challenging, especially when dealing with accumulated summaries across multiple patient adm…

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…

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…

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…

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…

We introduce a dataset of people’s names and clinical note templates to support research on the demographic bias of de-identification systems. The dataset contains 16 name sets that vary along four demographic dimensions: gender, race, name populari…