Development and validation of a novel bleeding risk prediction tool for aspirin users with a low body mass index.

Aspirin is commonly utilized in the management and prevention of various diseases. However, in specific individuals, particularly those with low body mass index (BMI), aspirin can elevate the risk of bleeding. Achieving a delicate equilibrium between the desirable antiplatelet effects and potential bleeding complications is a notable consideration.

Machine learning derived model for the prediction of bleeding in dual antiplatelet therapy patients.

Objective: This study aimed to develop a predictive model for assessing bleeding risk in dual antiplatelet therapy (DAPT) patients.

Methods: A total of 18,408 DAPT patients were included. Data on patients' demographics, clinical features, underlying diseases, past history, and laboratory examinations were collected from Affiliated Dongyang Hospital of Wenzhou Medical University.

Predictive Model of Internal Bleeding in Elderly Aspirin Users Using XGBoost Machine Learning.

Objective: This study aimed to develop a predictive model for assessing internal bleeding risk in elderly aspirin users using machine learning.

Methods: A total of 26,030 elderly aspirin users (aged over 65) were retrospective included in the study. Data on patient demographics, clinical features, underlying diseases, medical history, and laboratory examinations were collected from Affiliated Dongyang Hospital of Wenzhou Medical University.