Tensor Factorization-based Prediction with an Application to Estimate the Risk of Chronic Diseases

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

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  • Eman Yee Man Leung
  • Eliza Lai Yi Wong
  • Eng Kiong Yeoh


Original languageEnglish
Number of pages10
Journal / PublicationIEEE Intelligent Systems
Online published5 Apr 2021
Publication statusOnline published - 5 Apr 2021


Tensor factorization has emerged as a powerful method to address the challenges of high dimensionality and sparsity regarding disease development and comorbidity. Chronic diseases have a high likelihood to co-occur, making patients suffering from one chronic disease to have an elevated risk for other diseases in the course of aging. Despite rich results of risk assessment models for chronic diseases, risk prediction considering the complex mechanisms of disease development and comorbidity remains to be under-researched. This research aims to develop tensor factorization-based methods to predict the onset of new chronic diseases through incorporating the comorbidity patterns with the clinical and sequential factors revealed in the electronic health records (EHRs). The efficacy of the proposed methods was validated through predicting the onset of new chronic diseases using the EHRs data for 23 years from a major hospital in Hong Kong. The proposed methods could inform proactive health management programs for at-risk patients with different chronic conditions.

Research Area(s)

  • data mining, Data mining, Data models, Diseases, health, health care, Intelligent systems, Medical information systems, Medical services, Predictive models, Tensors