Title

A predictive analytics framework for identifying patients at risk of developing multiple medical complications caused by chronic diseases

Document Type

Article

Publication Date

11-9-2019

DOI

https://doi.org/10.1016/j.artmed.2019.101750

Abstract

Chronic diseases often cause several medical complications. This paper aims to predict multiple complications among patients with a chronic disease. The literature uses single-task learning algorithms to predict complications independently and assumes no correlation among complications of chronic diseases. We propose two methods (independent prediction of complications with single-task learning and concurrent prediction of complications with multi-task learning) and show that medical complications of chronic diseases can be correlated. We use a case study and compare the performance of these two methods by predicting complications of hypertrophic cardiomyopathy on 106 predictors in 1078 electronic medical records from April 2009-April 2017, inclusive. The methods are implemented using logistic regression, artificial neural networks, decision trees, and support vector machines. The results show multi-task learning with logistic regression improves the performance of predictions in terms of both discrimination and calibration.

Language

English

Comments

Talaei-Khoei, A., Tavana, M. and Wilson, J.M. (2019) ‘A Predictive Analytics Framework for Identifying Patients at Risk of Developing Multiple Medical Complications Caused by Chronic Diseases,’ Artificial Intelligence in Medicine, Vol. 101.

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