Deep Learning Predicts the Malignant-Transformation-Free Survival of Oral Potentially Malignant Disorders.

ABSTRACT: Machine-intelligence platforms for the prediction of the probability of malignant transformation of oral potentially malignant disorders are required as adjunctive decision-making platforms in contemporary clinical practice. This study utilized time-to-event learning models to predict malignant transformation in oral leukoplakia and oral lichenoid lesions. A total of 1098 patients with oral white lesions from two institutions were included in this study. In all, 26 features available from electronic health records were used to train four learning algorithms-Cox-Time, DeepHit, DeepSurv, random survival forest (RSF)-and one standard statistical method-Cox proportional hazards model. Discriminatory performance, calibration of survival estimates, and model stability were assessed using a conco

SUBMITTER: Adeoye J 

PROVIDER: S-EPMC8657223 | biostudies-literature | 2021 Dec

REPOSITORIES: biostudies-literature