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Healthcare predictive analytics for disease progression: a longitudinal data fusion approach

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Abstract

Healthcare predictive analytics using electronic health records (EHR) offers a promising direction to address the challenging tasks of health assessment. It is highly important to precisely predict the potential disease progression based on the knowledge in the EHR data for chronic disease care. In this paper, we utilize a novel longitudinal data fusion approach to model the disease progression for chronic disease care. Different from the conventional method using only initial or static clinical data to model the disease progression for current time prediction, we design a temporal regularization term to maintain the temporal successivity of data from different time points and simultaneously analyze data from data source level and feature level based on a sparse regularization regression approach. We examine our approach through extensive experiments on the medical data provided by the Alzheimer’s Disease Neuroimaging Initiative (ADNI). The results show that the proposed approach is more useful to simulate and predict the disease progression compared with the existing methods.

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Acknowledgements

This work was partially supported by the Science Fund for Creative Research Groups of the National Natural Science Foundation of China[grant number 71421001]. We are grateful for this support. We also would like to thank the anonymous reviewers for their insightful and constructive comments, which greatly improved this paper.

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  1. Institute of Systems Engineering, Dalian University of Technology, Dalian, 116024, People’s Republic of China

    Yi Zheng & Xiangpei Hu

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  1. Yi Zheng
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  2. Xiangpei Hu
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Correspondence to Yi Zheng.

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Zheng, Y., Hu, X. Healthcare predictive analytics for disease progression: a longitudinal data fusion approach. J Intell Inf Syst 55, 351–369 (2020). https://doi.org/10.1007/s10844-020-00606-9

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  • DOI: https://doi.org/10.1007/s10844-020-00606-9

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