A multinomial logistic regression model predicted three-class drug-induced Torsades de Pointes risk with 80% accuracy (95% CI 77%-94%) using data from rabbit ventricular wedge assays.
Does a multinomial logistic regression model accurately predict drug-induced Torsades de pointes (TdP) risk based on rabbit ventricular wedge assay data?
A machine learning approach using multinomial logistic regression on rabbit ventricular wedge assay data provides an interpretable method for predicting drug-induced Torsades de pointes risk.
Tasa de eventos absoluta: 80% vs 33%
Torsades de pointes (TdP) is an irregular heart rhythm as a side effect of drugs and may cause sudden cardiac death. A machine learning model that can accurately identify drug TdP risk is necessary. This study uses multinomial logistic regression models to predict three-class drug TdP risks based on datasets generated from rabbit ventricular wedge assay experiments. The training-test split and five-fold cross-validation provide unbiased measurements for prediction accuracy. We utilize bootstrap to construct a 95% confidence interval for prediction accuracy. The model interpretation is further demonstrated by permutation predictor importance. Our study offers an interpretable modeling method suitable for drug TdP risk prediction. Our method can be easily generalized to broader applications of drug side effect assessment.
Jaela Foster-Burns (Thu,) conducted a other in Drug-induced Torsades de Pointes (TdP) risk (n=112). Multinomial logistic regression model vs. Random guess baseline was evaluated on Prediction accuracy of three-class drug TdP risks (95% CI 77%-94%). A multinomial logistic regression model predicted three-class drug-induced Torsades de Pointes risk with 80% accuracy (95% CI 77%-94%) using data from rabbit ventricular wedge assays.
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