Resources

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…

While increasing patients' access to medical documents improves medical care, this benefit is limited by varying health literacy levels and complex medical terminology. Large language models (LLMs) offer solutions by simplifying medical information.…

Immunosuppression due to underlying conditions or immunosuppressive medication use increases the risk of morbidity and mortality in the context of infectious disease. Identifying patients with immunosuppression is important for better studying and u…

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…

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…

Clinical decision making is one of the most impactful parts of a physician's responsibilities and stands to benefit greatly from AI solutions such as large language models (LLMs). However, while many datasets exist to test the performance of AI mode…

This project provides MIMIC-Patient, a structured, per-patient representation of the MIMIC-III database designed for large language model (LLM)–based clinical question answering. For each of 500 admissions, we reconstruct the patient’s episode into …

Large-language models (LLMs) show promise for extracting information from clinical notes. Deploying these models at scale can be challenging due to high computational costs, regulatory constraints, and privacy concerns. To address these challenges, …

The advent of large, open access text databases has driven advances in state-of-the-art model performance in natural language processing (NLP). The relatively limited amount of clinical data available for NLP has been cited as a significant barrier …

State of the art models using deep neural networks have become very good in learning an accurate mapping from inputs to outputs. However, they still lack generalization capabilities in conditions that differ from the ones encountered during training…