Resources

An LLM Benchmark for Real-World Clinical Practice Using Discharge Summaries

The RadEvalX is a publicly available dataset developed similarly to the ReXVal dataset. RedEvalX focuses on radiologist evaluations of errors found in automatically generated radiology reports.

Open-access dataset of electromyogram (EMG) recordings collected from the wrist and forearm muscles of 43 people while they performed hand gestures.

Me-LLaMA is a family of large language models for medical applications trained using clinical text with LLaMA2 models as the base. We release model weights for the foundation models as well as the chat-enhanced models.

Data for the "Discharge Me!" Shared Task on Streamlining Discharge Documentation for BioNLP ACL'24

The PADS dataset contains smartwatch-based records from interactive neurological assessments of Parkinsons disease patients, differential diagnoses and healthy controls. The data is complemented with non-motor symptoms and medical history information

Multimodal (ECG, EMG, EDA, PPG, TEMP, ACC) biosignal dataset of everyday activities. Created with 3 wearable devices based on ScientISST Sense and Empatica E4.

Asclepius: Publicly Available Clinical Large Language Models with Synthetic Clinical Notes Asclepius-R: A instruction-finetuned large language model with MIMIC-III clinical notes

Respiratory and cardiovascular data collected from 20 subjects. Pressure, flow, aeration, and heart-rate data were collected during trials which included resting breathing, CPAP at varied PEEP settings, breath-holds, and forced expiratory manoeuvres.

Early detection of heart failure is vital for improving outcomes. The dataset contains 71,589 CXRs paired with gold standard labels from echocardiograms to enable the training of models to detect pathologies indicative of early stage heart failure.