Joint regression and classification via relational regularization for Parkinson's disease diagnosis.
It is known that the symptoms of Parkinson's disease (PD) progress successively, early and accurate diagnosis of the disease is of great importance, which slows the disease deterioration further and alleviates mental and physical suffering. In this paper, we propose a joint regression and classifica...
| Publicado en: | Technology & Health Care Vol. 26; pp. 19 - 31 |
|---|---|
| Autores principales: | , , , , , , , , , |
| Formato: | Journal Article |
| Publicado: |
Sage Publications Inc.
2018 Supplement 1
|
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=129909164&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 129909164 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 09287329 3QT jtl: Technology & Health Care issn: 09287329 maglogo: N pubinfo: dt: 2018 Supplement 1 vid: 26 pid: 344 pub: Sage Publications Inc. place: Thousand Oaks, California artinfo: ui: 129909164 129909164 NLM29689760 10.3233/THC-174540 NLM29689760 129909164 ppf: 19 ppct: 12 formats: tig: atl: Joint regression and classification via relational regularization for Parkinson's disease diagnosis. aug: au: Lei, Haijun Huang, Zhongwei Han, Tao Luo, Qiuming Cai, Ye Liu, Gang Lei, Baiying Gómez Schwarzacher Zhou affil: Guangdong Province Key Laboratory of Popular High Performance Computers, Key Laboratory of Service Computing and Applications, College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, Guangdong, China sug: subj: Disease Progression Joints Tomography, Emission-Computed Methods Parkinson Disease Diagnosis Neuroradiography Methods Information Science Methods Image Interpretation, Computer Assisted Methods Middle Age Aged Female Male Scales Middle Aged: 45-64 years Aged: 65+ years Female Male ab: It is known that the symptoms of Parkinson's disease (PD) progress successively, early and accurate diagnosis of the disease is of great importance, which slows the disease deterioration further and alleviates mental and physical suffering. In this paper, we propose a joint regression and classification scheme for PD diagnosis using baseline multi-modal neuroimaging data. Specifically, we devise a new feature selection method via relational learning in a unified multi-task feature selection model. Three kinds of relationships (e.g., relationships among features, responses, and subjects) are integrated to represent the similarities among features, responses, and subjects. Our proposed method exploits five regression variables (depression, sleep, olfaction, cognition scores and a clinical label) to jointly select the most discriminative features for clinical scores prediction and class label identification. Extensive experiments are conducted to demonstrate the effectiveness of the proposed method on the Parkinson's Progression Markers Initiative (PPMI) dataset. Our experimental results demonstrate that multi-modal data can effectively enhance the performance in class label identification compared with single modal data. Our proposed method can greatly improve the performance in clinical scores prediction and outperforms the state-of-art methods as well. The identified brain regions can be recognized for further medical analysis and diagnosis. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|