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Machine Learning
Machine Learning
Mining patient-specific and contextual data with machine learning technologies to predict cancellation of children's surgery.
Citation:
Liu L, Ni Y, Zhang N, Nick Pratap J. Mining patient-specific and contextual data with machine learning technologies to predict cancellation of children's surgery. Int J Med Inform. 2019 Sep;129:234-241. doi:
10.1016/j.ijmedinf.2019.06.007. Epub 2019 Jun 8. PMID: 31445261.
Principal Investigator:
Project Name:
Semi-Automated Identification of Biomedical Literature
Description:
This research will develop and evaluate a semi-automatic approach to conducting literature searches for systematic reviews.
Principal Investigator:
Project Dates:
September 30, 2019 to September 29, 2020
Exploration and initial development of text classification models to identify health information technology usability-related patient safety event reports.
Citation:
Fong A, Komolafe T, Adams KT, Cohen A, Howe JL, Ratwani RM. Exploration and initial development of text classification models to identify health information technology usability-related patient safety event reports. Appl Clin Inform. 2019 May;10(3):521-527. doi: 10.1055/s-0039-1693427. Epub 2019 Jul 17. PMID: 31315139.
Principal Investigator:
Optimal Methods for Notifying Clinicians About Epilepsy Surgery Patients - Final Report
Citation:
Dexheimer J. Optimal Methods for Notifying Clinicians About Epilepsy Surgery Patients - Final Report. (Prepared by Cincinnati Children's Hospital Medical Center under Grant No. R21 HS024977). Rockville, MD: Agency for Healthcare Research and Quality, 2018.
Principal Investigator:
Health Information Technology in Heart Failure Care - Final Report
Citation:
Blecker S. Health Information Technology in Heart Failure Care - Final Report. (Prepared by the New York University School of Medicine under Grant No. K08 HS023683). Rockville, MD: Agency for Healthcare Research and Quality, 2018.
Principal Investigator:
Project Name:
Enhancing an EMR-Based Real-Time Sepsis Alert System Performance Through Machine Learning - Final Report
Citation:
Sherwin R. Enhancing an EMR-Based Real-Time Sepsis Alert System Performance Through Machine Learning - Final Report. (Prepared by Wayne State University under Grant No. R21 HS024750). Rockville, MD: Agency for Healthcare Research and Quality, 2019.
Principal Investigator:
Project Name:
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:
Project Name:
HopScore: an Electronic Outcomes-Based Emergency Triage System - Final Report
Citation:
Levin, S. HopScore: an Electronic Outcomes-Based Emergency Triage System - Final Report. (Prepared by Johns Hopkins University under Grant No. R21 HS023641). Rockville, MD: Agency for Healthcare Research and Quality, 2018.
Principal Investigator:
Improving Missing Data Analysis in Distributed Research Networks
Description:
This project aims to refine and develop methods to address missing electronic health record data to improve data quality and research validity.
Principal Investigator:
Project Dates:
September 30, 2018 to September 29, 2021
TREAT ECARDS: Translating Evidence into Action: Electronic Clinical Decision Support in ARDS
Description:
This project will develop and evaluate an electronic clinical decision support tool for care of patients with Acute Respiratory Distress Syndrome.
Principal Investigator:
Project Dates:
September 1, 2018 to June 30, 2021
