Zobrazeno 1 - 10
of 24
pro vyhledávání: '"Michael Draugelis"'
Autor:
Li-Fang Cheng, Bianca Dumitrascu, Gregory Darnell, Corey Chivers, Michael Draugelis, Kai Li, Barbara E Engelhardt
Publikováno v:
BMC Medical Informatics and Decision Making, Vol 20, Iss 1, Pp 1-23 (2020)
Abstract Background For real-time monitoring of hospital patients, high-quality inference of patients’ health status using all information available from clinical covariates and lab test results is essential to enable successful medical interventio
Externí odkaz:
https://doaj.org/article/8d5f947de380430196f387e4535f136c
Autor:
Niranjani Prasad, Aishwarya Mandyam, Corey Chivers, Michael Draugelis, C. William Hanson, Barbara E. Engelhardt, Krzysztof Laudanski
Publikováno v:
Journal of Personalized Medicine, Vol 12, Iss 5, p 661 (2022)
Both provider- and protocol-driven electrolyte replacement have been linked to the over-prescription of ubiquitous electrolytes. Here, we describe the development and retrospective validation of a data-driven clinical decision support tool that uses
Externí odkaz:
https://doaj.org/article/ebb0ec4494624cd38e24127976aec914
Autor:
Mariana Restrepo, Ann Marie Huffenberger, C William Hanson, Michael Draugelis, Krzysztof Laudanski
Publikováno v:
Healthcare, Vol 9, Iss 3, p 343 (2021)
Biosensors represent one of the numerous promising technologies envisioned to extend healthcare delivery. In perioperative care, the healthcare delivery system can use biosensors to remotely supervise patients who would otherwise be admitted to a hos
Externí odkaz:
https://doaj.org/article/9095b8f56bcc4c17946269d52e9e45bd
Autor:
Charles E. Kahn, Qi Long, Rebecca A. Hubbard, Laura Fluharty, James Beinlich, Daniel S. Herman, Danielle L. Mowery, Mary Regina Boland, Yong Chen, George Demiris, Ross Koppel, Tessa S. Cook, Dokyoon Kim, C. William Hanson, Ryan J. Urbanowicz, Nebojsa Mirkovic, Kathryn H. Bowles, Blanca E. Himes, Marylyn D. Ritchie, John H. Holmes, Peter Gabriel, Robert W. Grundmeier, Jeffrey S. Morris, Michael Draugelis, Jason H. Moore
Publikováno v:
Methods Inf Med
Background The electronic health record (EHR) has become increasingly ubiquitous. At the same time, health professionals have been turning to this resource for access to data that is needed for the delivery of health care and for clinical research. T
Autor:
Stephanie Teeple, Corey Chivers, Kristin A Linn, Scott D Halpern, Nwamaka Eneanya, Michael Draugelis, Katherine Courtright
Publikováno v:
BMJ Quality & Safety. :bmjqs-2022
ObjectiveEvaluate predictive performance of an electronic health record (EHR)-based, inpatient 6-month mortality risk model developed to trigger palliative care consultation among patient groups stratified by age, race, ethnicity, insurance and socio
Autor:
C. William Hanson, Patrick J. Brennan, Michael J. Levy, Michael Draugelis, Asaf Hanish, Jason D. Christie, Corey Chivers, George L. Anesi, Jason Lubken, ThaiBinh Luong, Mark E. Mikkelsen, Scott D. Halpern, Michael Becker, Gary E. Weissman, Andrew Crane-Droesch
Publikováno v:
Annals of Internal Medicine
Background: The coronavirus disease 2019 (COVID-19) pandemic challenges hospital leaders to make time-sensitive, critical decisions about clinical operations and resource allocations. Objective: To estimate the timing of surges in clinical demand and
Autor:
Jennifer C. Ginestra, Kimberly Pavan, Laurie Meadows, Craig A Umscheid, Barry D. Fuchs, Corey Chivers, Michael Draugelis, Michael J Lynch, William D. Schweickert, Patrick J. Donnelly, Heather M. Giannini
Publikováno v:
Crit Care Med
OBJECTIVE: Assess clinician perceptions of a machine learning-based early warning system to predict severe sepsis and septic shock (EWS 2.0) DESIGN: Prospective observational study SETTING: Tertiary teaching hospital in Philadelphia, PA PATIENTS: Non
Autor:
Neil O. Fishman, Kimberly Pavan, Michael Draugelis, Craig A Umscheid, Jennifer C. Ginestra, William D. Schweickert, Barry D. Fuchs, Patrick J. Donnelly, Heather M. Giannini, Corey Chivers, Laurie Meadows, Asaf Hanish, C. William Hanson, Michael J Lynch
Publikováno v:
Critical Care Medicine. 47:1485-1492
Objectives:Develop and implement a machine learning algorithm to predict severe sepsis and septic shock and evaluate the impact on clinical practice and patient outcomes.Design:Retrospective cohort for algorithm derivation and validation, pre-post im
Autor:
Emily J. MacKay, Peter W. Groeneveld, Nimesh D. Desai, Michael Draugelis, Corey Chivers, Michael D. Stubna, William Hanson
Publikováno v:
PLoS ONE, Vol 16, Iss 6, p e0252585 (2021)
PLoS ONE
PLoS ONE
Objective This study aimed to develop and validate a claims-based, machine learning algorithm to predict clinical outcomes across both medical and surgical patient populations. Methods This retrospective, observational cohort study, used a random 5%
Autor:
Christopher R Manz, Manqing Liu, Mitesh S. Patel, C. William Hanson, Corey Chivers, Nina O'Connor, Justin E. Bekelman, Jennifer Braun, Lynn M. Schuchter, Lawrence N. Shulman, Ravi B. Parikh, Jinbo Chen, Michael Draugelis, Susan Harkness Regli
Publikováno v:
JAMA oncology. 6(11)
Importance Machine learning (ML) algorithms can identify patients with cancer at risk of short-term mortality to inform treatment and advance care planning. However, no ML mortality risk prediction algorithm has been prospectively validated in oncolo