Multi-Task Learning-Based Binary Evaluation and Prediction for Commercialization Value of Chronic Disease Research Achievements — A Case Study of Diabetes Patents

Corresponding author: Shan zhiguang, calfqingqing@163.com

DOI: 10.12201/bmr.202610.00040

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  Abstract: Purpose/Significance: Patent transformation is an important indicator for measuring the technological transformation of chronic disease prevention and treatment. This paper conducts a comprehensive prediction study on the value and time of patent transformation, providing decision support for the technological transformation in this field.Method/Process. Based on 13,373 diabetes-related patent records from 2000 to 2024, this paper designs a patent transformation feature system and builds a multi-task learning prediction model that takes into account both patent transfer and patent transformation time. Results/Conclusion. The analysis of feature importance indicates that the application-to-publication time (days), the number of citations of the family, and the type of patent are the key factors affecting patent transfer.Multi-task learning prediction model can provide decision-making references for all parties involved in the patent transfer process. However, for the prediction results of the model, they should be used with caution in light of actual conditions.

  Key words: Chronic diseases; Diabetes; Patent transfer; Transfer duration; Machine Learning

  Submit time: 19 October 2026

  Copyright: The copyright holder for this preprint is the author/funder, who has granted biomedRxiv a license to display the preprint in perpetuity.
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