Impact of System and Diagnostic Errors on Medical Litigation Outcomes: Machine Learning-Based Prediction Models.
Healthcare (Basel)
; 10(5)2022 May 12.
Article
en En
| MEDLINE
| ID: mdl-35628029
ABSTRACT
No prediction models using use conventional logistic models and machine learning exist for medical litigation outcomes involving medical doctors. Using a logistic model and three machine learning models, such as decision tree, random forest, and light-gradient boosting machine (LightGBM), we evaluated the prediction ability for litigation outcomes among medical litigation in Japan. The prediction model with LightGBM had a good predictive ability, with an area under the curve of 0.894 (95% CI; 0.893-0.895) in all patients' data. When evaluating the feature importance using the SHApley Additive exPlanation (SHAP) value, the system error was the most significant predictive factor in all clinical settings for medical doctors' loss in lawsuits. The other predictive factors were diagnostic error in outpatient settings, facility size in inpatients, and procedures or surgery settings. Our prediction model is useful for estimating medical litigation outcomes.
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Tipo de estudio:
Diagnostic_studies
/
Prognostic_studies
/
Risk_factors_studies
Idioma:
En
Revista:
Healthcare (Basel)
Año:
2022
Tipo del documento:
Article
País de afiliación:
Japón