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Developing and Validating a Prediction Model For Death or Critical Illness in Hospitalized Adults, an Opportunity for Human-Computer Collaboration by Amol A. Verma; Chloe Pou-Prom; Liam G. McCoy; Joshua Murray; Bret Nestor; Shirley Bell; Ophyr Mourad; Michael Fralick; Jan Friedrich; Marzyeh Ghassemi; Muhammad Mamdani is a Medicine article available to read on EtoBox.

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## OBJECTIVES: Hospital early warning systems that use machine learning (ML) to predict clinical deterioration are increasingly being used to aid clinical decision-making. However, it is not known how ML predictions complement physician and nurse judgment. Our objective was to train and validate a ML model to predict patient deterioration and compare model predictions with real-world physician and nurse predictions. ## DESIGN: Retrospective and prospective cohort study. ## SETTING: Academic tertiary care hospital. ## PATIENTS: Adult general internal medicine hospitalizations. ## MEASUREMENTS AND MAIN RESULTS: We developed and validated a neural network model to predict in-hospital death and ICU admission in 23,528 hospitalizations between April 2011 and April 2019. We then compared model predictions with 3,374 prospectively collected predictions from nurses, residents, and attending physicians about their own patients in 960 hospitalizations between April 30, and August 28, 2019. ML model predictions achieved clinician-level accuracy for predicting ICU admission or death (ML median F1 score 0.32 [interquartile range (IQR) 0.30-0.34], AUC 0.77 [IQ 0.76-0.78]; clinicians median F1-sc

Who reads Developing and Validating a Prediction Model For Death or Critical Illness in Hospitalized Adults, an Opportunity for Human-Computer Collaboration?

Author

Amol A. Verma; Chloe Pou-Prom; Liam G. McCoy; Joshua Murray; Bret Nestor; Shirley Bell; Ophyr Mourad; Michael Fralick; Jan Friedrich; Marzyeh Ghassemi; Muhammad Mamdani

Publisher

Ovid Technologies (Wolters Kluwer Health)

Published

2023

Language

EN

Field

Medicine (Health Sciences)