Parkinson's Disease Diagnosis via Joint Learning from Multiple Modalities and Relations

Haijun Lei, Zhongwei Huang, Feng Zhou, Ahmed Elazab, Ee Leng Tan, Hancong Li, Jing Qin, Baiying Lei

Research output: Journal article publicationJournal articleAcademic researchpeer-review

11 Citations (Scopus)

Abstract

Parkinson's disease (PD) is a neurodegenerative progressive disease that mainly affects the motor systems of patients. To slow this disease deterioration, early and accurate diagnosis of PD is an effective way, which alleviates mental and physical sufferings by clinical intervention. In this paper, we propose a joint regression and classification framework for PD diagnosis via magnetic resonance and diffusion tensor imaging data. Specifically, we devise a unified multitask feature selection model to explore multiple relationships among features, samples, and clinical scores. We regress four clinical variables of depression, sleep, olfaction, cognition scores, as well as perform the classification of PD disease from the multimodal data. The multitask model explores the relationships at the level of clinical scores, image features, and subjects, to select the most informative and diseased-related features for diagnosis. The proposed method is evaluated on the public Parkinson's progression markers initiative dataset. The extensive experimental results show that the multitask framework can effectively boost the performance of regression and classification and outperforms other state-of-the-art methods. The computerized predictions of clinical scores and label for PD diagnosis may offer quantitative reference for decision support as well.

Original languageEnglish
Article number8453792
Pages (from-to)1437-1449
Number of pages13
JournalIEEE Journal of Biomedical and Health Informatics
Volume23
Issue number4
DOIs
Publication statusPublished - Jul 2019

Keywords

  • classification
  • joint learning
  • multi-modal
  • multiple relations
  • Parkinson's disease diagnosis
  • prediction

ASJC Scopus subject areas

  • Biotechnology
  • Computer Science Applications
  • Electrical and Electronic Engineering
  • Health Information Management

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