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Treatment Recommendations for Clinical Deterioration on the Wards: Development and Validation of Machine Learning Models
Clinical deterioration in general ward patients is associated with increased morbidity and mortality. Early and appropriate treatments can improve outcomes for such patients. While machine learning (ML) tools have proven successful in the early identification of clinical deterioration risk, little work has explored their effectiveness in providing data-driven treatment recommendations to clinicians for high-risk patients.
Medigy Insights
Machine learning models paired with early warning systems can not only detect patients at high risk of clinical deterioration on hospital wards but also suggest specific, data-driven treatment recommendations to support clinician decision-making. These AI-based treatment predictions demonstrated strong performance across multiple intervention types and have the potential to reduce delays in lifesaving care.
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