M
Michael J. Eisses
Researcher at University of Washington
Publications - 18
Citations - 1216
Michael J. Eisses is an academic researcher from University of Washington. The author has contributed to research in topics: Cardiopulmonary bypass & Perioperative. The author has an hindex of 9, co-authored 18 publications receiving 619 citations. Previous affiliations of Michael J. Eisses include Seattle Children's.
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Journal ArticleDOI
Explainable Machine-Learning Predictions for the Prevention of Hypoxaemia During Surgery
Scott M. Lundberg,Bala G. Nair,Monica S. Vavilala,Mayumi Horibe,Michael J. Eisses,Michael J. Eisses,Trevor Adams,Trevor Adams,David E. Liston,David E. Liston,Daniel King-Wai Low,Daniel King-Wai Low,Shu-Fang Newman,Jerry Kim,Jerry Kim,Su-In Lee +15 more
TL;DR: The results suggest that if anaesthesiologists currently anticipate 15% of hypoxaemia events, with the assistance of this system they could anticipate 30%, a large portion of which may benefit from early intervention because they are associated with modifiable factors.
Journal ArticleDOI
Preoperative malnutrition is associated with increased mortality and adverse outcomes after paediatric cardiac surgery.
Faith J. Ross,Gregory J. Latham,Denise C. Joffe,Michael Richards,Jeremy M. Geiduschek,Michael J. Eisses,Douglas Thompson,Monique Radman +7 more
TL;DR: This study is unique in demonstrating a significant association between malnutrition and 30-day mortality and other adverse outcomes after paediatric cardiac surgery in a mixed population of CHD patients.
Journal ArticleDOI
Perioperative morbidity in children with elastin arteriopathy.
Gregory J. Latham,Faith J. Ross,Michael J. Eisses,Michael Richards,Jeremy M. Geiduschek,Denise C. Joffe +5 more
TL;DR: Children with elastin arteriopathy, the majority of whom have Williams–Beuren syndrome, are at high risk for sudden death, and the frequency or risk factors for morbidity and mortality in an entire cohort of children undergoing anesthesia are reported.
Posted ContentDOI
Explainable machine learning predictions to help anesthesiologists prevent hypoxemia during surgery
Scott M. Lundberg,Bala G. Nair,Monica S. Vavilala,Mayumi Horibe,Michael J. Eisses,Trevor Adams,David E. Liston,Daniel King-Wai Low,Shu-Fang Newman,Jerry Kim,Su-In Lee +10 more
TL;DR: Using minute by minute EMR data from fifty thousand surgeries, a machine learning based system called Prescience is developed and tested that predicts real-time hypoxemia risk and presents an explanation of factors contributing to that risk during general anesthesia.
Journal ArticleDOI
Cardiopulmonary Bypass Parameters and Hemostatic Response to Cardiopulmonary Bypass in Infants Versus Children
TL;DR: Infants had greater chest tube output, longer CPB times, and a larger drop in platelet counts during CPB than children, and active tissue plasminogen activator (tPA) increased duringCPB in both groups, with infants showing lower levels than children (p < 0.001).