A predictive analytics framework for identifying patients at risk of developing multiple medical complications caused by chronic diseases
作者：
Highlights：
• Patients with chronic diseases are often at risk for multiple correlated complications.
• Single-task learning predicts these complications but ignores their correlations.
• We use single- and multi-task learning with different predictive models.
• We compare prediction performance of hypertrophic cardiomyopathy complications.
• We show multi-task learning implemented by logistic regression has the best performance.
摘要
•Patients with chronic diseases are often at risk for multiple correlated complications.•Single-task learning predicts these complications but ignores their correlations.•We use single- and multi-task learning with different predictive models.•We compare prediction performance of hypertrophic cardiomyopathy complications.•We show multi-task learning implemented by logistic regression has the best performance.
论文关键词：Predictive analytics,Chronic disease,Artificial neural networks,Multi-Task learning,Regression.
论文评审过程：Received 14 July 2018, Revised 7 July 2019, Accepted 30 October 2019, Available online 9 November 2019, Version of Record 18 November 2019.
论文官网地址：https://doi.org/10.1016/j.artmed.2019.101750
