M
Michael D. Howell
Researcher at Google
Publications - 148
Citations - 11850
Michael D. Howell is an academic researcher from Google. The author has contributed to research in topics: Intensive care unit & Intensive care. The author has an hindex of 48, co-authored 139 publications receiving 10010 citations. Previous affiliations of Michael D. Howell include University of Chicago & Baylor College of Medicine.
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Journal ArticleDOI
Scalable and accurate deep learning with electronic health records
Alvin Rajkomar,Alvin Rajkomar,Eyal Oren,Kai Chen,Andrew M. Dai,Nissan Hajaj,Michaela Hardt,Peter J. Liu,Xiaobing Liu,Jake Marcus,Mimi Sun,Patrik Sundberg,Hector Yee,Kun Zhang,Yi Zhang,Gerardo Flores,Gavin E. Duggan,Jamie Irvine,Quoc V. Le,Kurt Litsch,Alexander Mossin,Justin Tansuwan,De Wang,James Wexler,Jimbo Wilson,Dana Ludwig,Samuel L. Volchenboum,Katherine Chou,Michael Pearson,Srinivasan Madabushi,Nigam H. Shah,Atul J. Butte,Michael D. Howell,Claire Cui,Greg S. Corrado,Jeffrey Dean +35 more
TL;DR: A representation of patients’ entire raw EHR records based on the Fast Healthcare Interoperability Resources (FHIR) format is proposed, and it is demonstrated that deep learning methods using this representation are capable of accurately predicting multiple medical events from multiple centers without site-specific data harmonization.
Journal ArticleDOI
Scalable and accurate deep learning for electronic health records
Alvin Rajkomar,Eyal Oren,Kai Chen,Andrew M. Dai,Nissan Hajaj,Peter J. Liu,Xiaobing Liu,Mimi Sun,Patrik Sundberg,Hector Yee,Kun Zhang,Gavin E. Duggan,Gerardo Flores,Michaela Hardt,Jamie Irvine,Quoc V. Le,Kurt Litsch,Jake Marcus,Alexander Mossin,Justin Tansuwan,De Wang,James Wexler,Jimbo Wilson,Dana Ludwig,Samuel L. Volchenboum,Katherine Chou,Michael Pearson,Srinivasan Madabushi,Nigam H. Shah,Atul J. Butte,Michael D. Howell,Claire Cui,Greg S. Corrado,Jeffrey Dean +33 more
TL;DR: In this paper, the authors proposed a representation of patients' entire, raw EHR records based on the Fast Healthcare Interoperability Resources (FHIR) format and demonstrated that deep learning methods using this representation are capable of accurately predicting multiple medical events from multiple centers without site-specific data harmonization.
Journal ArticleDOI
Serum Lactate as a Predictor of Mortality in Emergency Department Patients with Infection
Nathan I. Shapiro,Michael D. Howell,Daniel Talmor,Larry A. Nathanson,Alan Lisbon,Richard E. Wolfe,J. Woodrow Weiss +6 more
TL;DR: The results support serum venous lactate level as a promising risk-stratification tool in this cohort of ED patients with signs and symptoms suggestive of infection, and multi-enter validation needs to be done before widespread implementation.
Journal ArticleDOI
Quick Sepsis-related Organ Failure Assessment, Systemic Inflammatory Response Syndrome, and Early Warning Scores for Detecting Clinical Deterioration in Infected Patients outside the Intensive Care Unit.
Matthew M. Churpek,Ashley H. Snyder,Xuan Han,Sarah Sokol,Natasha N Pettit,Michael D. Howell,Dana P. Edelson +6 more
TL;DR: Commonly used early warning scores are more accurate than the qSOFA score for predicting death and ICU transfer in non‐ICU patients, and these results suggest that the qsoFA score should not replace general earlywarning scores when risk‐stratifying patients with suspected infection.
Journal ArticleDOI
Acid-suppressive medication use and the risk for hospital-acquired pneumonia.
TL;DR: In this large, hospital-based pharmacoepidemiologic cohort, acid-suppressive medication use was associated with 30% increased odds of hospital-acquired pneumonia.