A Comorbidity Knowledge-Aware Model for Disease Prognostic Prediction

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

8 Scopus Citations
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Author(s)

  • Zhongzhi Xu
  • Jian Zhang
  • Qingpeng Zhang
  • Qi Xuan
  • Paul Siu Fai Yip

Related Research Unit(s)

Detail(s)

Original languageEnglish
Pages (from-to)9809-9819
Journal / PublicationIEEE Transactions on Cybernetics
Volume52
Issue number9
Online published7 May 2021
Publication statusPublished - Sept 2022

Abstract

Prognostic prediction is the task of estimating a patient's risk of disease development based on various predictors. Such prediction is important for healthcare practitioners and patients because it reduces preventable harm and costs. As such, a prognostic prediction model is preferred if: 1) it exhibits encouraging performance and 2) it can generate intelligible rules, which enable experts to understand the logic of the model's decision process. However, current studies usually concentrated on only one of the two features. Toward filling this gap, in the present study, we develop a novel knowledge-aware Bayesian model taking into consideration accuracy and transparency simultaneously. Real-world case studies based on four years' territory-wide electronic health records are conducted to test the model. The results show that the proposed model surpasses state-of-the-art prognostic prediction models in accuracy and c-statistic. In addition, the proposed model can generate explainable rules.

Research Area(s)

  • Comorbidity networks, Data models, disease risk prediction, Diseases, explainable learning, graph representation, History, Knowledge engineering, Medical diagnostic imaging, Predictive models, Task analysis

Citation Format(s)

A Comorbidity Knowledge-Aware Model for Disease Prognostic Prediction. / Xu, Zhongzhi; Zhang, Jian; Zhang, Qingpeng et al.
In: IEEE Transactions on Cybernetics, Vol. 52, No. 9, 09.2022, p. 9809-9819.

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review