Resources
22 results for "medical information extraction"
Model
Credentialed Access
Shareable Artificial Intelligence to Extract Cancer Outcomes from Electronic Health Records for Precision Oncology Research
Databases that link molecular data to clinical outcomes can inform precision cancer research into novel prognostic and predictive biomarkers. However, outside of clinical trials, cancer outcomes are typically recorded only in text form within electr…
Database
Credentialed Access
CAD-Chest: Comprehensive Annotation of Diseases based on MIMIC-CXR Radiology Report
Several extant chest X-ray (CXR) datasets predominantly comprise binary disease labels and exhibit a deficiency in providing comprehensive disease-related information. Crucial facets of disease management, including disease severity, diagnostic unce…
Database
Credentialed Access
Curated Data for Describing Blood Glucose Management in the Intensive Care Unit
Analysis of real-world glucose and insulin clinical data recorded in electronic health records can provide insights into tailored approaches to clinical care, but still present many analytic challenges. The present data subsets are the result of a d…
Database
Open Access
Synthetic Mention Corpora for Disease Entity Recognition and Normalization
Named Entity Recognition (NER) and Entity Normalization (EN) are fundamental tasks in information extraction, particularly in the biomedical and clinical domains. NER identifies textual mentions of entities, while EN maps these mentions to unique id…
Database
Restricted Access
Upper body thermal images and associated clinical data from a pilot cohort study of COVID-19
The prospective upper body thermal images SARS-CoV2 association study was designed to test the hypothesis that thermal videos may aid in the early diagnosis of COVID-19. The study recorded a set of measurements from 252 participants regarding PCR re…
Model
Credentialed Access
Clinical BERT Models Trained on Pseudo Re-identified MIMIC-III Notes
This project contains the weights of 7 different BERT models trained over a surrogate re-identified set of MIMIC-III (v1.4) notes. Each model is suitable for direct compatibility with the HuggingFace framework; users can easily load and apply the mo…
Database
Credentialed Access
PIFIR: PET-CT Invasive Fungal Infection Reports
Surveillance of invasive fungal infection (IFI) in clinical settings is a laborious process requiring a detailed review of patient medical history. One of the key sources of clinical information is imaging reports: radiologist-produced free-text rep…
Database
Credentialed Access
CHIFIR: Cytology and Histopathology Invasive Fungal Infection Reports
Surveillance of invasive fungal infection (IFI) in clinical settings is a laborious process requiring a detailed review of patient medical history. One of the key sources of clinical information is cytology and histopathology reports: pathologist-pr…
Database
Credentialed Access
MIMIC-III-Ext-VeriFact-BHC: Labeled Propositions From Brief Hospital Course Summaries for Long-form Clinical Text Evaluation
The VeriFact-BHC dataset is designed to verify the factuality of long-form text written about a patient against their own electronic health record. There is increasing interest in using large language models (LLMs) to generate clinical text in patie…
natural language processing
clinical informatics
formal logic
chart review
text embedding
artificial intelligence
brief hospital course
long-form text
llm evaluation
logical atomism
retrieval-augmented generation
text reranking
clinical medicine
atomic claim
fact verification
hybrid retrieval
llm-as-a-judge
large language models
electronic health records
clinical notes
Database
Credentialed Access
Medical-CXR-VQA dataset: A Large-Scale LLM-Enhanced Medical Dataset for Visual Question Answering on Chest X-Ray Images
Medical Visual Question Answering (VQA) is an important task in medical multi-modal Large Language Models (LLMs), aiming to answer clinically relevant questions regarding input medical images. This technique has the potential to improve the efficien…