{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,4]],"date-time":"2026-07-04T11:21:11Z","timestamp":1783164071010,"version":"3.54.6"},"reference-count":25,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100008366","name":"Guangdong Pharmaceutical University","doi-asserted-by":"publisher","award":["A2024090"],"award-info":[{"award-number":["A2024090"]}],"id":[{"id":"10.13039\/501100008366","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100008366","name":"Guangdong Pharmaceutical University","doi-asserted-by":"publisher","award":["B2025055"],"award-info":[{"award-number":["B2025055"]}],"id":[{"id":"10.13039\/501100008366","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["clinicalkey.com","clinicalkey.com.au","clinicalkey.es","clinicalkey.fr","clinicalkey.jp","elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Computer Methods and Programs in Biomedicine"],"published-print":{"date-parts":[[2026,8]]},"DOI":"10.1016\/j.cmpb.2026.109414","type":"journal-article","created":{"date-parts":[[2026,4,27]],"date-time":"2026-04-27T16:03:29Z","timestamp":1777305809000},"page":"109414","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["Unveiling the causal pathway of Parkinson\u2019s disease dysphonia: A voice causal generative model (VCGM) approach"],"prefix":"10.1016","volume":"282","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-6184-2550","authenticated-orcid":false,"given":"Guo","family":"Leiyong","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Li","family":"Yu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiang","family":"Zheng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"issue":"4","key":"10.1016\/j.cmpb.2026.109414_bib0001","article-title":"Causability and explainability of artificial intelligence in medicine","volume":"9","author":"Holzinger","year":"2019","journal-title":"Wiley Interdiscip. Rev.: Data Min. Knowl. Discov."},{"issue":"24","key":"10.1016\/j.cmpb.2026.109414_bib0002","doi-asserted-by":"crossref","first-page":"18069","DOI":"10.1007\/s00521-019-04051-w","article-title":"The importance of interpretability and visualization in machine learning for applications in medicine and health care","volume":"32","author":"Vellido","year":"2020","journal-title":"Neural Comput. Appl."},{"key":"10.1016\/j.cmpb.2026.109414_bib0003","article-title":"A novel and robust deep learning-based framework for Parkinson\u2019s disease detection using voice recordings","volume":"99","author":"Gunduz","year":"2021","journal-title":"Appl. Soft Comput."},{"issue":"3","key":"10.1016\/j.cmpb.2026.109414_bib0004","first-page":"806","article-title":"Detecting Parkinson\u2019s disease from speech using audio transformers","volume":"22","author":"Vaiciukynas","year":"2022","journal-title":"Sensors"},{"key":"10.1016\/j.cmpb.2026.109414_bib0005","series-title":"Advances in Neural Information Processing Systems","first-page":"4765","article-title":"A unified approach to interpreting model predictions","volume":"30","author":"Lundberg","year":"2017"},{"issue":"1","key":"10.1016\/j.cmpb.2026.109414_bib0006","first-page":"3691","article-title":"Causality matters in medical imaging","volume":"12","author":"Castro","year":"2021","journal-title":"Nat. Commun."},{"key":"10.1016\/j.cmpb.2026.109414_bib0007","series-title":"Causality: Models, reasoning, and inference","author":"Pearl","year":"2009"},{"key":"10.1016\/j.cmpb.2026.109414_bib0008","series-title":"Causation, prediction, and search","author":"Spirtes","year":"2000"},{"key":"10.1016\/j.cmpb.2026.109414_bib0009","first-page":"2003","article-title":"A linear non-gaussian acyclic model for causal discovery","volume":"7","author":"Shimizu","year":"2006","journal-title":"J. Mach. Learn. Res."},{"issue":"1","key":"10.1016\/j.cmpb.2026.109414_bib0010","doi-asserted-by":"crossref","first-page":"3923","DOI":"10.1038\/s41467-020-17419-7","article-title":"Improving the accuracy of medical diagnosis with causal machine learning","volume":"11","author":"Richens","year":"2020","journal-title":"Nat. Commun."},{"issue":"1","key":"10.1016\/j.cmpb.2026.109414_bib0011","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1186\/1475-925X-6-23","article-title":"Exploiting nonlinear recurrence and fractal scaling properties for voice disorder detection","volume":"6","author":"Little","year":"2007","journal-title":"Biomed. Eng. Online"},{"issue":"10","key":"10.1016\/j.cmpb.2026.109414_bib0012","doi-asserted-by":"crossref","first-page":"1943","DOI":"10.1109\/TBME.2006.871883","article-title":"Dimensionality reduction of a pathological voice quality assessment system based on gaussian mixture models and short-term cepstral parameters","volume":"53","author":"Godino-Llorente","year":"2006","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"10.1016\/j.cmpb.2026.109414_bib0013","article-title":"A multi-task learning framework for Parkinson\u2019s disease detection and severity rating from speech","volume":"205","author":"Zhang","year":"2021","journal-title":"Comput. Methods Programs Biomed."},{"key":"10.1016\/j.cmpb.2026.109414_bib0014","series-title":"Proc. 22nd ACM SIGKDD Int. Conf. Knowl. Discov. Data Min.","first-page":"1135","article-title":"Why should I trust you?\u201d: explaining the predictions of any classifier","author":"Ribeiro","year":"2016"},{"key":"10.1016\/j.cmpb.2026.109414_bib0015","series-title":"Proc. 4th Mach. Learn. Healthc. Conf.","first-page":"337","article-title":"What clinicians want: a survey of clinicians\u2019 desired insights from machine learning models","author":"Tonekaboni","year":"2019"},{"key":"10.1016\/j.cmpb.2026.109414_bib0016","series-title":"DAGs with NO TEARS: Continuous optimization for structure learning","first-page":"9492","author":"Zheng","year":"2018"},{"key":"10.1016\/j.cmpb.2026.109414_bib0017","article-title":"Causal discovery from neuroimaging data in Alzheimer\u2019s disease","volume":"130","author":"Kim","year":"2022","journal-title":"J. Biomed. Inform."},{"issue":"12","key":"10.1016\/j.cmpb.2026.109414_bib0018","first-page":"2731","article-title":"Causal inference in electronic health records: a review","volume":"28","author":"Cowie","year":"2021","journal-title":"J. Am. Med. Inform. Assoc."},{"key":"10.1016\/j.cmpb.2026.109414_bib0019","article-title":"Synthetic vowels of speakers with Parkinson's disease and parkinsonism","author":"Hlavni\u010dka","year":"2019","journal-title":"Figshare"},{"issue":"4","key":"10.1016\/j.cmpb.2026.109414_bib0020","first-page":"1225","article-title":"DirectLiNGAM: a direct method for learning a linear non-Gaussian structural equation model","volume":"12","author":"Shimizu","year":"2011","journal-title":"J. Mach. Learn. Res."},{"issue":"2","key":"10.1016\/j.cmpb.2026.109414_bib0021","doi-asserted-by":"crossref","first-page":"297","DOI":"10.1586\/14737175.8.2.297","article-title":"Speech treatment for Parkinson\u2019s disease","volume":"8","author":"Ramig","year":"2008","journal-title":"Expert Rev. Neurother."},{"key":"10.1016\/j.cmpb.2026.109414_bib0022","first-page":"689","article-title":"Nonlinear causal discovery with additive noise models","volume":"21","author":"Hoyer","year":"2009","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.cmpb.2026.109414_bib0023","series-title":"In Proceedings of the 36th Conference on Uncertainty in Artificial Intelligence (UAI 2020)","first-page":"186","article-title":"Causal discovery with general non-linear relationships using non-linear ICA","author":"Monti","year":"2020"},{"key":"10.1016\/j.cmpb.2026.109414_bib0024","doi-asserted-by":"crossref","unstructured":"M. Giuliano D. Adamec M.I. Debas Construcci\u00f3n de una base de voz de personas con y sin enfermedad de Parkinson Revista Digital del Departamento de Ingenier\u00eda e Investigaciones Tecnol\u00f3gicas (REDDI) 6(1): 2021,1-19 https:\/\/reddi.unlam.edu.ar\/index.php\/ReDDi\/article\/view\/141.","DOI":"10.54789\/reddi.6.1.1"},{"key":"10.1016\/j.cmpb.2026.109414_bib0025","series-title":"Selecci\u00f3n de medidas de disfon\u00eda para la identificaci\u00f3n de enfermos de Parkinson","first-page":"1","author":"Giuliano","year":"2020"}],"container-title":["Computer Methods and Programs in Biomedicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0169260726001690?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0169260726001690?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,7,4]],"date-time":"2026-07-04T10:57:14Z","timestamp":1783162634000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0169260726001690"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,8]]},"references-count":25,"alternative-id":["S0169260726001690"],"URL":"https:\/\/doi.org\/10.1016\/j.cmpb.2026.109414","relation":{},"ISSN":["0169-2607"],"issn-type":[{"value":"0169-2607","type":"print"}],"subject":[],"published":{"date-parts":[[2026,8]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Unveiling the causal pathway of Parkinson\u2019s disease dysphonia: A voice causal generative model (VCGM) approach","name":"articletitle","label":"Article Title"},{"value":"Computer Methods and Programs in Biomedicine","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.cmpb.2026.109414","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"109414"}}