Article

Automated and interoperable methods for generalizable development of clinical machine-learning models for predicting neuromorbidity in critically ill children

2025-08-05

Abstract excerpt

<h4>Objectives</h4> To streamline the development of clinical machine learning (ML) models for predicting acute neurological morbidity in critically ill children by extending our prior work to create a standardized, reproducible, and scalable workflow leveraging Fast Healthcare Interoperability Resources (FHIR), cloud infrastructure, and automated ML tools. <h4>Methods</h4> We developed workflow for extracting,...

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Literature Corpus work: b6723e33-030b-5dc0-b781-4fa59218d315
DOI: 10.1101/2025.08.01.25332805

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Automated and interoperable methods for generalizable development of clinical machine-learning models for predicting neuromorbidity in critically ill childrenDOI 10.1101/2025.08.01.25332805Read

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