Modeling disease progression via multisource multitask learners: a case study with Alzheimer's disease

Liqiang Nie, Luming Zhang, Lei Meng, Xuemeng Song, Xiaojun Chang, Xuelong Li

Research output: Contribution to journalArticleResearchpeer-review

88 Citations (Scopus)


Understanding the progression of chronic diseases can empower the sufferers in taking proactive care. To predict the disease status in the future time points, various machine learning approaches have been proposed. However, a few of them jointly consider the dual heterogeneities of chronic disease progression. In particular, the predicting task at each time point has features from multiple sources, and multiple tasks are related to each other in chronological order. To tackle this problem, we propose a novel and unified scheme to coregularize the prior knowledge of source consistency and temporal smoothness. We theoretically prove that our proposed model is a linear model. Before training our model, we adopt the matrix factorization approach to address the data missing problem. Extensive evaluations on real-world Alzheimer's disease data set have demonstrated the effectiveness and efficiency of our model. It is worth mentioning that our model is generally applicable to a rich range of chronic diseases.

Original languageEnglish
Article number7419264
Pages (from-to)1508-1519
Number of pages12
JournalIEEE Transactions on Neural Networks and Learning Systems
Issue number7
Publication statusPublished - Jul 2017
Externally publishedYes


  • Disease progression modeling
  • future health prediction
  • multisource analysis
  • source consistency
  • temporal regularization

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