Development and external validation of a machine learning model for predicting drug-induced immune thrombocytopenia in a real-world hospital cohort.

ABSTRACT:

Background

Drug-induced immune thrombocytopenia (DITP) is a rare but potentially life-threatening adverse drug reaction, often underrecognized due to its nonspecific presentation and the lack of real-time diagnostic tools. Early identification of at-risk patients is critical to improving medication safety and preventing severe complications.

Objective

To develop and externally validate a machine learning model for predicting the risk of DITP using routinely collected hospital data, and to optimize its clinical applicability through threshold adjustment.

Methods

We conducted a retrospective cohort study using electronic medical records from Hai Phong International Hospital (2018-2024) for model development and internal validation. An independent cohort from Hai Phong I

SUBMITTER: Van Dung H 

PROVIDER: S-EPMC12261740 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature