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Database Credentialed Access

EHR-DS-QA: A Synthetic QA Dataset Derived from Medical Discharge Summaries for Enhanced Medical Information Retrieval Systems

Konstantin Kotschenreuther

Dataset consisting of question and answer pairs synthetically generated from medical discharge summaries, designed to facilitate the training and development of large language models specifically tailored for healthcare applications

mimic-iv clinical question-answering medical discharge summaries large language models

Published: Jan. 11, 2024. Version: 1.0.0

Database Credentialed Access

MedDec: Medical Decisions for Discharge Summaries in the MIMIC-III Database

Mohamed Elgaar, Jiali Cheng, Nidhi Vakil, et al.

Annotations of ten types of medical decisions from discharge summaries in the MIMIC-III database.

natural language processing medical decisions span classification discharge summary mimic

Published: Oct. 16, 2024. Version: 1.0.0

Database Credentialed Access

MedDec: Medical Decisions for Discharge Summaries in the MIMIC-III Database

Mohamed Elgaar, Jiali Cheng, Nidhi Vakil, et al.

Annotations of ten types of medical decisions from discharge summaries in the MIMIC-III database.

natural language processing medical decisions span classification discharge summary mimic

Published: Oct. 16, 2024. Version: 1.0.0

Database Credentialed Access

Learning to Ask Like a Physician: a Discharge Summary Clinical Questions (DiSCQ) Dataset

Eric Lehman

Dataset of questions asked by medical experts about patients. Medical experts will read a discharge summary line-by-line and (1) ask any question that they may have and (2) record what in the text "triggered" them to ask their question.

question generation question answering machine learning

Published: July 28, 2022. Version: 1.0

Database Credentialed Access

Annotation dataset of problematic opioid use and related contexts from MIMIC-III Critical Care Database discharge summaries

Melissa Poulsen, Vanessa Troiani, Philip Freda, et al.

The database contains a corpus of annotated data from the MIMIC-III Critical Care Database from a study that aimed to develop and apply an annotation schema to characterize opioid use disorder and related contextual factors.

opioid use disorder substance use natural language processing clinical notes

Published: Feb. 8, 2023. Version: 1.0.0

Model Credentialed Access

Asclepius-R : Clinical Large Language Model Built On MIMIC-III Discharge Summaries

Sunjun Kweon, Junu Kim, Jiyoun Kim, et al.

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

clinical notes synthetic clinical notes synthetic notes asclepius open-source llm clinical llm large language model

Published: March 25, 2024. Version: 1.1.0

Database Credentialed Access

National Institutes of Health Stroke Scale (NIHSS) Annotations for the MIMIC-III Database

Jiayang Wang, Xiaoshuo Huang, Lin Yang, et al.

A dataset of annotated NIHSS scale items and corresponding scores from stroke patients discharge summaries in MIMIC-III.

Published: Jan. 25, 2021. Version: 1.0.0

Database Credentialed Access

CLIP: A Dataset for Extracting Action Items for Physicians from Hospital Discharge Notes

James Mullenbach, Yada Pruksachatkun, Sean Adler, et al.

Clinical action items annotated over MIMIC-III. 718 discharge summaries are labeled at a sentence- and character-level with multiple action labels including Appointment, Lab, Procedure, Medication, Imaging, Patient Instructions, and Other.

Published: June 21, 2021. Version: 1.0.0

Model Credentialed Access

Shareable Artificial Intelligence to Extract Cancer Outcomes from Electronic Health Records for Precision Oncology Research

Kenneth Kehl, Pavel Trukhanov, Christopher Fong, et al.

The DFCI-imaging-student and DFCI-medonc-student AI models for extracting cancer outcomes from imaging reports and medical oncologist notes from electronic health records.

Published: Oct. 24, 2024. Version: 1.0.0

Database Credentialed Access

MeDiSumQA: Patient-Oriented Question-Answer Generation from Discharge Letters

Amin Dada, Osman Alperen Koras, Marie Bauer, et al.

MeDiSumQA is a dataset of patient-oriented QA pairs from MIMIC-IV discharge summaries, designed to evaluate LLMs in generating safe, patient-friendly medical responses for clinical QA and healthcare communication.

Published: May 5, 2025. Version: 1.0.0