Retrospective validation of a machine learning clinical decision support tool for myocardial infarction risk stratification.

ABSTRACT: Diagnosis and appropriate intervention for myocardial infarction (MI) are time-sensitive but rely on clinical measures that can be progressive and initially inconclusive, underscoring the need for an accurate and early predictor of MI to support diagnostic and clinical management decisions. The objective of this study was to develop a machine learning algorithm (MLA) to predict MI diagnosis based on electronic health record data (EHR) readily available during Emergency Department assessment. An MLA was developed using retrospective patient data. The MLA used patient data as they became available in the first 3 h of care to predict MI diagnosis (defined by International Classification of Diseases, 10th revision code) at any time during the encounter. The MLA obtained an area under the recei

SUBMITTER: Panchavati S 

PROVIDER: S-EPMC8667565 | biostudies-literature | 2021 Dec

REPOSITORIES: biostudies-literature