11 research outputs found

    On Expert-Machine Partnership to Predict Mortality of Congestive Heart Failure Patients

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    This study examines the combined use of machine learning (ML) and expert judgment in predicting 30-day mortality for congestive heart failure (CHF) patients. It compares models using either expert-selected, ML-selected, or integrated features. The integrated model, merging expert and ML insights, outperforms others in predicting mortality risk, underscoring the value of combining human expertise and ML in clinical decision-making.</p

    Outcomes of the study population during follow-up (crude data).

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    <p>Data are number of events/(%).</p><p>- The primary endpoint of this study was 30-day Major Adverse Coronary Events (MACE): all-cause mortality, recurrent MI, recurrent ischemia, stent thrombosis, ischemic stroke, urgent revascularization during follow-up.</p
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