{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,22]],"date-time":"2026-04-22T02:28:44Z","timestamp":1776824924991,"version":"3.51.2"},"reference-count":33,"publisher":"Wiley","issue":"3","license":[{"start":{"date-parts":[[2023,2,7]],"date-time":"2023-02-07T00:00:00Z","timestamp":1675728000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Ann Clin Transl Neurol"],"published-print":{"date-parts":[[2023,3]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Objective<\/jats:title><jats:p>Parkinson disease (PD) is a progressive neurodegenerative disorder with an annual incidence of approximately 0.1%. While primarily considered a motor disorder, increasing emphasis is being placed on its non\u2010motor features. Both manifestations of the disease affect quality of life (QoL), which is captured in part II of the Unified Parkinson's Disease Rating Scale (UPDRS\u2010II). While useful in the management of patients, it remains challenging to predict how QoL will change over time in PD. The goal of this work is to explore the feasibility of a machine learning algorithm to predict QoL changes in PD patients.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>In this retrospective cohort study, patients with at least 12\u2009months of follow\u2010up were identified from the Parkinson's Progression Markers Initiative database (<jats:italic>N<\/jats:italic>\u2009=\u2009630) and divided into two groups: those with and without clinically significant worsening in UPDRS\u2010II (<jats:italic>n<\/jats:italic>\u2009=\u2009404 and <jats:italic>n<\/jats:italic>\u2009=\u2009226, respectively). We developed an artificial neural network using only UPDRS\u2010II scores, to predict whether a patient would clinically worsen or not at 12\u2009months from follow\u2010up.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>Using UPDRS\u2010II at baseline, at 2\u2009months, and at 4\u2009months, the algorithm achieved 90% specificity and 56% sensitivity.<\/jats:p><\/jats:sec><jats:sec><jats:title>Interpretation<\/jats:title><jats:p>A learning model has the potential to rule in patients who may exhibit clinically significant worsening in QoL at 12\u2009months. These patients may require further testing and increased focus.<\/jats:p><\/jats:sec>","DOI":"10.1002\/acn3.51577","type":"journal-article","created":{"date-parts":[[2023,2,8]],"date-time":"2023-02-08T07:45:52Z","timestamp":1675842352000},"page":"312-320","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["A machine learning approach to predict quality of life changes in patients with Parkinson's Disease"],"prefix":"10.1002","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6886-9189","authenticated-orcid":false,"given":"Tyler D.","family":"Alexander","sequence":"first","affiliation":[{"name":"Department of Neurological Surgery Thomas Jefferson University Hospitals  Philadelphia Pennsylvania 19107 USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chandrasekhar","family":"Nataraj","sequence":"additional","affiliation":[{"name":"Villanova Center for Analytics of Dynamic Systems (VCADS) Villanova University  Villanova Pennsylvania 19085 USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chengyuan","family":"Wu","sequence":"additional","affiliation":[{"name":"Department of Neurological Surgery Thomas Jefferson University Hospitals  Philadelphia Pennsylvania 19107 USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2023,2,7]]},"reference":[{"key":"e_1_2_8_2_1","doi-asserted-by":"publisher","DOI":"10.1093\/aje\/kwg068"},{"key":"e_1_2_8_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.parkreldis.2015.09.004"},{"key":"e_1_2_8_4_1","doi-asserted-by":"publisher","DOI":"10.1038\/nrn.2017.62"},{"key":"e_1_2_8_5_1","doi-asserted-by":"publisher","DOI":"10.1016\/bs.irn.2017.05.023"},{"key":"e_1_2_8_6_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jns.2013.07.005"},{"key":"e_1_2_8_7_1","doi-asserted-by":"publisher","DOI":"10.1002\/mds.23368"},{"key":"e_1_2_8_8_1","doi-asserted-by":"publisher","DOI":"10.1111\/ene.12950"},{"key":"e_1_2_8_9_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.parkreldis.2015.10.006"},{"key":"e_1_2_8_10_1","doi-asserted-by":"publisher","DOI":"10.1002\/mds.27790"},{"key":"e_1_2_8_11_1","doi-asserted-by":"publisher","DOI":"10.1002\/mds.27492"},{"key":"e_1_2_8_12_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.artmed.2020.101807"},{"key":"e_1_2_8_13_1","doi-asserted-by":"publisher","DOI":"10.1109\/EMBC.2017.8037708"},{"key":"e_1_2_8_14_1","doi-asserted-by":"publisher","DOI":"10.1002\/ana.25459"},{"key":"e_1_2_8_15_1","doi-asserted-by":"publisher","DOI":"10.1136\/jnnp\u20102018\u2010318337"},{"key":"e_1_2_8_16_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.parkreldis.2021.04.016"},{"key":"e_1_2_8_17_1","doi-asserted-by":"publisher","DOI":"10.3389\/fneur.2020.601225"},{"key":"e_1_2_8_18_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41531\u2010019\u20100086\u20104"},{"key":"e_1_2_8_19_1","doi-asserted-by":"publisher","DOI":"10.3390\/s21103553"},{"key":"e_1_2_8_20_1","doi-asserted-by":"crossref","unstructured":"SalmanpourM SaberiA ShamsaeiM RahmimA.Optimal feature selection and machine learning for prediction of outcome in Parkinson's disease. 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