Oct 24, 2019 · The objective of this study was to apply ML algorithms at the visit level to impute self-harm events that were uncoded in claims data of ...
Results: Imputation identified 1 592 703 self-harm events vs 83 113 coded events, with areas under the curve >0.99 for the balanced and full datasets, and 83.5% ...
Visit level machine learning imputation of uncoded self-harm in major mental illness and characterization of incidence of self-harm. Praveen Kumar1,2 ...
Visit level machine learning imputation of uncoded self-harm in major mental illness and characterization of incidence of self-harm. Abstract: Inadequate ...
Jan 1, 2020 · Kumar P, Nestsiarovich A, Nelson SJ, Kerner B, Perkins DJ, Lambert CG. Imputation and characterization of uncoded self-harm in major mental ...
Abstract Objective We aimed to impute uncoded self-harm in administrative claims data of individuals with major mental illness (MMI), characterize self-harm ...
Apr 21, 2021 · The aim of this study was to compare all commonly used BD pharmacotherapies, as well as psychotherapy for the risk of self-harm, in a large population of ...
Mar 9, 2024 · Imputation and characterization of uncoded self-harm in major mental illness using machine learning. J Am Med Inform Assoc. 2020;27:136–46 ...
Imputation and characterization of uncoded self-harm in major mental illness using machine learning · Self-harm, Unintentional Injury, and Suicide in Bipolar ...
Aug 15, 2019 · ... , Kerner B, Perkins DJ, Lambert CG. Imputation and characterization of uncoded self-harm in major mental illness using machine learning,...