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Semi-Supervised Learning to Improve Generalizability of Cancer Associated-Venous Thromboembolism Risk Prediction

Shuai Jin¹, Chong Wang², Dan Qin³

¹Department of Adult Care, School of Nursing, Capital Medical University, Beijing, China.

Clinical and Applied Thrombosis/Hemostasis : Official Journal of the International Academy of Clinical and Applied Thrombosis/Hemostasis

|January 27, 2026

Summary

This study developed improved cancer-associated venous thromboembolism (CA-VTE) risk models using machine learning and semi-supervised learning (SSL), outperforming the Khorana score for better patient risk stratification.

Keywords:

cancer prediction model risk factors semi-supervised learning venous thromboembolism

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Area of Science:

Oncology
Data Science
Medical Informatics

Background:

Cancer patients face a high risk of venous thromboembolism (VTE), a serious complication.
Existing risk prediction models, like the Khorana score, have limitations in accuracy and generalizability.
Accurate prediction of cancer-associated VTE (CA-VTE) is crucial for timely intervention and preventive care.

Purpose of the Study:

To develop and validate an enhanced CA-VTE risk prediction model using a semi-supervised learning (SSL) algorithm.
To improve the generalizability and predictive accuracy of machine learning (ML) models for CA-VTE.
To compare the performance of developed ML models against the established Khorana score.

Main Methods:

A combined retrospective and prospective cohort study involving 2100 cancer patients.
Development of eight supervised ML models and one SSL model for CA-VTE risk prediction.
External validation of models using a prospective cohort and performance evaluation via Area Under the Curve (AUC) and Brier score.

Main Results:

Post-imputation ML models demonstrated superior performance (AUC: 0.816-0.868) compared to pre-imputation models (AUC: 0.798-0.841) on external validation.
The developed ML models significantly outperformed the Khorana score (AUC: 0.693), which showed no improvement.
SSL algorithm application enhanced the external validation performance and prediction accuracy of the ML models.

Conclusions:

The study successfully developed eight ML models that surpass the predictive capability of the Khorana score for CA-VTE.
The integration of SSL significantly improved the generalizability and accuracy of CA-VTE risk prediction models.
These findings offer a valuable tool for early identification of high-risk patients and implementation of stratified preventive strategies for CA-VTE.