Resources

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…

Accurate medical decision-making is critical for both patients and clinicians. Patients often find it difficult to interpret their symptoms, determine their severity, and select the right specialist to see. At the same time, clinicians face challeng…

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…

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…

Unstructured clinical documentation, such as progress notes, contains rich contextual information critical for clinical decision-making but remains underutilized in computational research due to the limited availability of annotated datasets. The MI…

The ER-Reason dataset is a benchmark designed to evaluate LLM-based clinical reasoning and decision-making in the emergency room (ER), a high-stakes setting where clinicians make rapid, consequential decisions across diverse patient presentations an…

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…

Electronic Health Records (EHRs) combine structured data, such as medications and laboratory measurements, with unstructured clinical notes describing patient conditions and clinical events. Although these two data types provide complementary inform…

The Antibiotic Resistance Microbiology Dataset – MGB (ARMD-MGB) is a de-identified resource derived from electronic health records (EHR) that facilitates research in antimicrobial resistance (AMR). ARMD-MGB encompasses data collected from over 225,0…

We present a novel dataset of 12-lead electrocardiogram (ECG) recordings developed to support the study and classification of Brugada syndrome, a rare but potentially fatal cardiac arrhythmia. The data were collected retrospectively from patients ev…