Intraoperative Features Improve Model Risk Predictions After Coronary Artery Bypass Grafting

Autor: Willa Potosnak, BS, Keith A. Dufendach, MD, Chirag Nagpal, PhD, David J. Kaczorowski, MD, Pyongsoo Yoon, MD, Johannes Bonatti, MD, James K. Miller, PhD, Artur W. Dubrawski, PhD

Publikováno v: Annals of Thoracic Surgery Short Reports, Vol 2, Iss 3, Pp 336-340 (2024)

Background: Intraoperative physiologic parameters could offer predictive utility in evaluating risk of adverse postoperative events yet are not included in current standard risk models. This study examined whether the inclusion of continuous intraope

Externí odkaz: https://doaj.org/article/c2415c225b6e4d60b354aabcb327962e

Supplementing Existing Societal Risk Models for Surgical Aortic Valve Replacement With Machine Learning for Improved Prediction

Autor: Arman Kilic, Robert H. Habib, James K. Miller, David M. Shahian, Joseph A. Dearani, Artur W. Dubrawski

Publikováno v: Journal of the American Heart Association: Cardiovascular and Cerebrovascular Disease, Vol 10, Iss 22 (2021)

Background This study evaluated the role of supplementing Society of Thoracic Surgeons (STS) risk models for surgical aortic valve replacement with machine learning (ML). Methods and Results Adults undergoing isolated surgical aortic valve replacemen

Externí odkaz: https://doaj.org/article/3861eabb8530449181e695aa601676a0

Supplementing Existing Societal Risk Models for Surgical Aortic Valve Replacement With Machine Learning for Improved Prediction

Autor: David M. Shahian, Joseph A. Dearani, Artur Dubrawski, Robert H. Habib, James K. Miller, Arman Kilic

Publikováno v: Journal of the American Heart Association: Cardiovascular and Cerebrovascular Disease, Vol 10, Iss 22 (2021)
Journal of the American Heart Association: Cardiovascular and Cerebrovascular Disease

Background This study evaluated the role of supplementing Society of Thoracic Surgeons (STS) risk models for surgical aortic valve replacement with machine learning (ML). Methods and Results Adults undergoing isolated surgical aortic valve replacemen

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::8bb25221bf7f57256a32b67b8b8b1180
https://www.ahajournals.org/doi/10.1161/JAHA.120.019697

Outbreak of Pseudomonas aeruginosa Infections from a Contaminated Gastroscope Detected by Whole Genome Sequencing Surveillance

Autor: Graham M. Snyder, Alexander J. Sundermann, Vaughn S. Cooper, Artur Dubrawski, Daria Van Tyne, Vatsala R. Srinivasa, Kady Waggle, James K. Miller, Marissa P. Griffith, Ashley M Ayres, Kathleen A. Shutt, A. William Pasculle, Praveen Kumar, Mustapha M. Mustapha, Jieshi Chen, Jane W. Marsh, Chinelo Ezeonwuka, Lee H. Harrison, Melissa Saul

Publikováno v: Clin Infect Dis

Background Traditional methods of outbreak investigations utilize reactive whole genome sequencing (WGS) to confirm or refute the outbreak. We have implemented WGS surveillance and a machine learning (ML) algorithm for the electronic health record (E

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::cabadea744560079ba8e20cc52083c9d
https://pubmed.ncbi.nlm.nih.gov/33367518

Automated data mining of the electronic health record for investigation of healthcare-associated outbreaks

Autor: Lee H. Harrison, Graham M. Snyder, Marissa P Pacey, Melissa Saul, Jieshi Chen, Jane W. Marsh, Alexander J. Sundermann, Ashley M Ayres, Mustapha M. Mustapha, Kathleen A. Shutt, Artur Dubrawski, James K. Miller, A. William Pasculle

Publikováno v: Infect Control Hosp Epidemiol

Background:Identifying routes of transmission among hospitalized patients during a healthcare-associated outbreak can be tedious, particularly among patients with complex hospital stays and multiple exposures. Data mining of the electronic health rec

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::aeb9ce72f4841737bac5aa0933fca414
https://europepmc.org/articles/PMC8189294/