Resources
27 results for "patient language information"
Challenge
Credentialed Access
ShAReCLEF eHealth 2013: Natural Language Processing and Information Retrieval for Clinical Care
This is the pilot year of the ShARe/CLEF eHealth Evaluation Lab, a shared task focused on natural language processing (NLP) and information retrieval (IR) for clinical care. The task is co-organized by the Shared Annotated Resources (ShARe) project …
Database
Contributor Review
BRATECA (Brazilian Tertiary Care Dataset): a Clinical Information Dataset for the Portuguese Language
Computational medicine research requires clinical data for training and testing purposes, so the development of datasets composed of real hospital data is of utmost importance in this field. Most such data collections are in the English language, we…
Database
Credentialed Access
Medical Expert Annotations of Unsupported Facts in Doctor-Written and LLM-Generated Patient Summaries
Large language models in healthcare can generate informative patient summaries while reducing the documentation workload of healthcare professionals. However, these models are prone to producing hallucinations, that is, generating unsupported inform…
Challenge
Credentialed Access
ShAReCLEF eHealth Evaluation Lab 2014 (Task 2): Disorder Attributes in Clinical Reports
This project focuses on Task 2 of the 2014 ShARe/CLEF eHealth evaluation lab, which extended Task 1 of the 2013 ShARe/CLEF eHealth evaluation lab by focusing on identification and normalization of disorder attributes. The task comprises two subtasks…
Database
Credentialed Access
MIMIC-IV-Ext clinical decision support for referral, triage and diagnosis
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…
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
brief hospital course
long-form text
logical atomism
retrieval-augmented generation
text reranking
clinical medicine
atomic claim
fact verification
hybrid retrieval
llm-as-a-judge
llm evaluation
clinical notes
large language models
electronic health records
artificial intelligence
Database
Credentialed Access
C-REACT: Contextualized Race and Ethnicity Annotations for Clinical Text
The Contextualized Race and Ethnicity Annotations for Clinical Text (C-REACT) dataset is a large publicly available corpus of sentences from clinical notes manually annotated for information related to race and ethnicity (RE). The corpus presented h…
Database
Credentialed Access
AIPatient KG: MIMIC-III and CORAL Electronic Health Records based Patient Knowledge Graph
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…
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
Phenotype Annotations for Patient Notes in the MIMIC-III Database
A crucial step within secondary analysis of electronic health records (EHRs) is to identify the patient cohort under investigation. While EHRs contain medical billing codes that aim to represent the conditions and treatments patients may have, much …