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Michael Draugelis
Michael Draugelis
Verified email at pennmedicine.upenn.edu - Homepage
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Cited by
Cited by
Year
Locally informed simulation to predict hospital capacity needs during the COVID-19 pandemic
GE Weissman, A Crane-Droesch, C Chivers, TB Luong, A Hanish, ...
Annals of internal medicine 173 (1), 21-28, 2020
2552020
A reinforcement learning approach to weaning of mechanical ventilation in intensive care units
N Prasad, LF Cheng, C Chivers, M Draugelis, BE Engelhardt
arXiv preprint arXiv:1704.06300, 2017
1572017
A machine learning algorithm to predict severe sepsis and septic shock: Development, implementation and impact on clinical practice
HM Giannini, JC Ginestra, C Chivers, M Draugelis, A Hanish, ...
Critical care medicine 47 (11), 1485, 2019
1132019
Machine learning approaches to predict 6-month mortality among patients with cancer
RB Parikh, C Manz, C Chivers, SH Regli, J Braun, ME Draugelis, ...
JAMA network open 2 (10), e1915997-e1915997, 2019
1112019
Clinician perception of a machine learning-based early warning system designed to predict severe sepsis and septic shock
JC Ginestra, HM Giannini, WD Schweickert, L Meadows, MJ Lynch, ...
Critical care medicine 47 (11), 1477, 2019
792019
Validation of a machine learning algorithm to predict 180-day mortality for outpatients with cancer
CR Manz, J Chen, M Liu, C Chivers, SH Regli, J Braun, M Draugelis, ...
JAMA oncology 6 (11), 1723-1730, 2020
432020
Electronic health record mortality prediction model for targeted palliative care among hospitalized medical patients: a pilot quasi-experimental study
KR Courtright, C Chivers, M Becker, SH Regli, LC Pepper, ME Draugelis, ...
Journal of general internal medicine 34 (9), 1841-1847, 2019
362019
Sparse multi-output Gaussian processes for medical time series prediction
LF Cheng, G Darnell, B Dumitrascu, C Chivers, ME Draugelis, K Li, ...
arXiv preprint arXiv:1703.09112, 2017
352017
Sparse multi-output Gaussian processes for online medical time series prediction
LF Cheng, B Dumitrascu, G Darnell, C Chivers, M Draugelis, K Li, ...
BMC medical informatics and decision making 20 (1), 1-23, 2020
312020
Methods and systems for prioritizing network assets
M Draugelis
US Patent 8,533,319, 2013
232013
Development and implementation of a machine-learning algorithm for early identification of sepsis in a multi-hospital academic healthcare system
HM Giannini, C Chivers, M Draugelis, A Hanish, B Fuchs, P Donnelly, ...
D15. CRITICAL CARE: DO WE HAVE A CRYSTAL BALL? PREDICTING CLINICAL …, 2017
102017
Clinical impact of an electronic dashboard and alert system for sedation minimization and ventilator liberation: a before-after study
BJ Anderson, D Do, C Chivers, K Choi, Y Gitelman, SJ Mehta, ...
Critical care explorations 1 (10), 2019
92019
Patient-specific effects of medication using latent force models with gaussian processes
LF Cheng, B Dumitrascu, M Zhang, C Chivers, M Draugelis, K Li, ...
International Conference on Artificial Intelligence and Statistics, 4045-4055, 2020
62020
Remote monitoring of critically-ill post-surgical patients: Lessons from a biosensor implementation trial
M Restrepo, AM Huffenberger, CW Hanson III, M Draugelis, K Laudanski
Healthcare 9 (3), 343, 2021
52021
A reinforcement learning approach to weaning of mechanical ventilation in intensive care units. arXiv. 2017
N Prasad, LF Cheng, C Chivers, M Draugelis, BE Engelhardt
arXiv preprint arXiv:1704.06300, 2019
42019
Application of machine learning approaches to administrative claims data to predict clinical outcomes in medical and surgical patient populations
EJ MacKay, MD Stubna, C Chivers, ME Draugelis, WJ Hanson, ND Desai, ...
PloS one 16 (6), e0252585, 2021
32021
Why Is the Electronic Health Record So Challenging for Research and Clinical Care?
JH Holmes, J Beinlich, MR Boland, KH Bowles, Y Chen, TS Cook, ...
Methods of Information in Medicine 60 (01/02), 032-048, 2021
22021
Forecasting PPE consumption during a pandemic: the case of Covid-19
K Lum, J Johndrow, A Cardone, B Fuchs, CE Cotner, O Jew, RB Parikh, ...
medRxiv, 2020
22020
Prospective validation of a machine learning algorithm to predict short-term mortality among outpatients with cancer.
C Manz, C Chivers, M Liu, SB Regli, S Changolkar, CN Evans, ...
Journal of Clinical Oncology 38 (15_suppl), 2009-2009, 2020
12020
Derivation and implementation of a machine learning approach to prompt serious illness conversations among outpatients with cancer.
RB Parikh, C Manz, C Chivers, SB Regli, J Braun, JA Jones, R Mamtani, ...
Journal of Clinical Oncology 37 (31_suppl), 131-131, 2019
12019
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