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Explainable machine learning for real-time deterioration alert prediction to guide pre-emptive treatment.

ABSTRACT: The Electronic Medical Record (EMR) provides an opportunity to manage patient care efficiently and accurately. This includes clinical decision support tools for the timely identification of adverse events or acute illnesses preceded by deterioration. This paper presents a machine learning-driven tool developed using real-time EMR data for identifying patients at high risk of reaching critical conditions that may demand immediate interventions. This tool provides a pre-emptive solution that can help busy clinicians to prioritize their efforts while evaluating the individual patient risk of deterioration. The tool also provides visualized explanation of the main contributing factors to its decisions, which can guide the choice of intervention. When applied to a test cohort of 18,648 patient records, the tool achieved 100% sensitivity for prediction windows 2-8 h in advance for patients that were identified at 95%, 85% and 70% risk of deterioration.

SUBMITTER: Brankovic A 

PROVIDER: S-EPMC9273762 | biostudies-literature | 2022 Jul

REPOSITORIES: biostudies-literature

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Explainable machine learning for real-time deterioration alert prediction to guide pre-emptive treatment.

Brankovic Aida A   Hassanzadeh Hamed H   Good Norm N   Mann Kay K   Khanna Sankalp S   Abdel-Hafez Ahmad A   Cook David D  

Scientific reports 20220711 1

The Electronic Medical Record (EMR) provides an opportunity to manage patient care efficiently and accurately. This includes clinical decision support tools for the timely identification of adverse events or acute illnesses preceded by deterioration. This paper presents a machine learning-driven tool developed using real-time EMR data for identifying patients at high risk of reaching critical conditions that may demand immediate interventions. This tool provides a pre-emptive solution that can h  ...[more]

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