Resources

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…

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…

Measurable progress in the development of medical decision support systems has been hindered by a lack of comprehensive datasets. Many available datasets focus on narrow prediction tasks and do not include a diverse range of data types, which limits…

The Brazilian Multilabel Ophthalmological Dataset (BRSET) is a multi-labeled ophthalmological dataset designed to improve scientific community development and validate machine learning models. In ophthalmology, ancillary exams support medical decisi…

We introduce Q-Pain, a dataset for assessing bias in medical QA in the context of pain management. We developed 55 medical question-answer pairs across five different types of pain management: each question includes a detailed patient-specific medic…

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 …

This study integrates the MIMIC-III and CORAL electronic health records into knowledge graphs to enhance their utility for advanced medical analysis and decision-making. MIMIC-III contains comprehensive data from over 40,000 patients, while CORAL fo…

Applying methods in natural language processing on electronic health records (EHR) data is a growing field. Existing corpus and annotation focus on modelling textual features and relation prediction [1] . However, there is a paucity of annotated cor…

Offline reinforcement learning (RL) holds promise for improving healthcare decision-making. However, most existing approaches rely on discretising patient trajectories into strictly regular time intervals. This is done to improve compatibility with …

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…