Goss, Foster R. | Digital Healthcare Research

Unsupervised clinical relevancy ranking of structured medical records to retrieve condition-specific information in the emergency department.

Citation

Korach ZT, Gradwohl S, Messinger A, Bookman K, Cohen K, Zhou L, Goss F. Unsupervised clinical relevancy ranking of structured medical records to retrieve condition-specific information in the emergency department. Int J Med Inform. 2021 May;149:104410. doi: 10.1016/j.ijmedinf.2021.104410. Epub 2021 Feb 5. PMID: 33621793.

Principal Investigator

Goss, Foster R.

Project Name

Natural Language Processing To Identify and Rank Clinically Relevant Information for EHRs in the Emergency Department

Natural Language Processing to Identify and Rank Clinically Relevant Information for EHRs in the Emergency Department - Final Report

Citation

Goss F. Natural Language Processing to Identify and Rank Clinically Relevant Information for EHRs in the Emergency Department - Final Report. (Prepared by the University of Colorado under Grant No. R21 HS024541). Rockville, MD: Agency for Healthcare Research and Quality, 2018.

Principal Investigator

Goss, Foster R.

Project Name

Natural Language Processing To Identify and Rank Clinically Relevant Information for EHRs in the Emergency Department

Description

This project developed a natural language processing electronic health record search tool that automatically identifies and ranks relevant clinical information based on a patient’s presenting complaint within the emergency department setting.

Grant Number

R21 HS024541

Principal Investigator(s)

Goss, Foster R.