Making the Improbable Possible: Generalizing Models Designed

Critical care clinics · Oct 2023

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Making the Improbable Possible: Generalizing Models Designed for a Syndrome-Based, Heterogeneous Patient Landscape.

Syndromic conditions, such as sepsis, are commonly encountered in the intensive care unit. Although these conditions are easy for clinicians to grasp, these conditions may limit the performance of machine-learning algorithms. ⋯ Recent advances in data science, such as transfer learning, conformal prediction, and continual learning, may improve generalizability of machine-learning algorithms in critically ill patients. Randomized trials with these approaches are indicated to demonstrate improvements in patient-centered outcomes at this point.

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- Joshua Pei Le, Supreeth Prajwal Shashikumar, Atul Malhotra, Shamim Nemati, and Gabriel Wardi.
- School of Medicine, University of Limerick, Castletroy, Co, Limerick V94 T9PX, Ireland.
- Crit Care Clin. 2023 Oct 1; 39 (4): 751768751-768.
AbstractSyndromic conditions, such as sepsis, are commonly encountered in the intensive care unit. Although these conditions are easy for clinicians to grasp, these conditions may limit the performance of machine-learning algorithms. Individual hospital practice patterns may limit external generalizability. Data missingness is another barrier to optimal algorithm performance and various strategies exist to mitigate this. Recent advances in data science, such as transfer learning, conformal prediction, and continual learning, may improve generalizability of machine-learning algorithms in critically ill patients. Randomized trials with these approaches are indicated to demonstrate improvements in patient-centered outcomes at this point.Copyright © 2023 Elsevier Inc. All rights reserved.

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Making the Improbable Possible: Generalizing Models Designed for a Syndrome-Based, Heterogeneous Patient Landscape.

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