{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,16]],"date-time":"2025-07-16T13:56:32Z","timestamp":1752674192265},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"value":"9781643684567","type":"print"},{"value":"9781643684574","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,1,25]],"date-time":"2024-01-25T00:00:00Z","timestamp":1706140800000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,1,25]]},"abstract":"<jats:p>Parkinson\u2019s disease is a chronic progressive neurodegenerative disease with highly heterogeneous symptoms and progression. It is helpful for patient management to establish a personalized model that integrates heterogeneous interpretation methods to predict disease progression. In the study, we propose a novel approach based on a multi-task learning framework to divide Parkinson\u2019s disease progression modeling into an unsupervised clustering task and a disease progression prediction task. On the one hand, the method can cluster patients with different progression trajectories and discover new progression patterns of Parkinson\u2019s disease. On the other hand, the discovery of new progression patterns helps to predict the future progression of Parkinson\u2019s disease markers more accurately through parameter sharing among multiple tasks. We discovered three different Parkinson\u2019s disease progression patterns and achieved better prediction performance (MAE=5.015, RMSE=7.284, r2=0.727) than previously proposed methods on Parkinson\u2019s Progression Markers Initiative datasets, which is a longitudinal cohort study with newly diagnosed Parkinson\u2019s disease.<\/jats:p>","DOI":"10.3233\/shti231068","type":"book-chapter","created":{"date-parts":[[2024,1,25]],"date-time":"2024-01-25T10:25:07Z","timestamp":1706178307000},"source":"Crossref","is-referenced-by-count":1,"title":["Personalized Prediction of Parkinson\u2019s Disease Progression Based on Deep Gaussian Processes"],"prefix":"10.3233","author":[{"given":"Changrong","family":"Pan","sequence":"first","affiliation":[{"name":"Engineering Research Center of EMR and Intelligent Expert System, Ministry of Education, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yu","family":"Tian","sequence":"additional","affiliation":[{"name":"Engineering Research Center of EMR and Intelligent Expert System, Ministry of Education, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tianshu","family":"Zhou","sequence":"additional","affiliation":[{"name":"Research Center for Healthcare Data Science, Zhejiang Laboratory, Hangzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jingsong","family":"Li","sequence":"additional","affiliation":[{"name":"Engineering Research Center of EMR and Intelligent Expert System, Ministry of Education, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, China"},{"name":"Research Center for Healthcare Data Science, Zhejiang Laboratory, Hangzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"7437","container-title":["Studies in Health Technology and Informatics","MEDINFO 2023 \u2014 The Future Is Accessible"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/SHTI231068","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,1,25]],"date-time":"2024-01-25T10:25:09Z","timestamp":1706178309000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/SHTI231068"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,1,25]]},"ISBN":["9781643684567","9781643684574"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/shti231068","relation":{},"ISSN":["0926-9630","1879-8365"],"issn-type":[{"value":"0926-9630","type":"print"},{"value":"1879-8365","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,1,25]]}}}