{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,20]],"date-time":"2026-08-20T07:24:05Z","timestamp":1787210645741,"version":"build-2736575974"},"reference-count":43,"publisher":"IOP Publishing","issue":"8","license":[{"start":{"date-parts":[[2026,8,20]],"date-time":"2026-08-20T00:00:00Z","timestamp":1787184000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/publishingsupport.iopscience.iop.org\/iop-standard\/v1"},{"start":{"date-parts":[[2026,8,20]],"date-time":"2026-08-20T00:00:00Z","timestamp":1787184000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/iopscience.iop.org\/info\/page\/text-and-data-mining"}],"funder":[{"name":"The Key Research and Development Projects of Zhejiang Province","award":["No.2023C03107"],"award-info":[{"award-number":["No.2023C03107"]}]},{"name":"Ningbo Major Research and Development Plan Project","award":["No.2025Z164"],"award-info":[{"award-number":["No.2025Z164"]}]}],"content-domain":{"domain":["iopscience.iop.org"],"crossmark-restriction":false},"short-container-title":["Physiol. Meas."],"published-print":{"date-parts":[[2026,8,31]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    <jats:italic>Objective.<\/jats:italic>\n                    Pulmonary function tests are central to assessing ventilatory impairment but are often limited by accessibility, repeatability, and patient compliance, particularly in community and longitudinal monitoring settings. This study investigates whether acoustic features of voluntary coughs can serve as low burden surrogate signals reflecting trends in spirometry related indices of ventilatory impairment.\n                    <jats:italic>Approach.<\/jats:italic>\n                    We propose a dual output ensemble learning framework that integrates deterministic regression and probabilistic interval estimation to jointly model spirometry related parameters from cough acoustics and basic demographic information. Fifty eight cough derived acoustic features and four biometric variables were analysed using an optimized extreme gradient boosting (XGBoost) model for point estimation, coupled with natural gradient boosting (NGBoost) for uncertainty quantification. A hybrid Bayesian-Whale Optimization strategy was employed for robust hyperparameter tuning.\n                    <jats:italic>Main results.<\/jats:italic>\n                    In a cohort of 278 subjects with heterogeneous respiratory conditions, the proposed framework achieved root mean square errors of 0.20 l for forced vital capacity (FVC) and 0.27 l for forced expiratory volume in one second (FEV1), with coefficients of determination up to 0.94 and 0.87, respectively. Interval estimates provided uncertainty bounds consistent with model reliability, with the actual prediction interval coverage probabilities reaching 84.0% for FVC and 77.8% for FEV1. Performance for the composite FEV1\/FVC ratio was comparatively lower, reflecting the increased variability of ratio based metrics.\n                    <jats:italic>Significance.<\/jats:italic>\n                    These findings suggest that cough acoustics encode physiologically relevant information associated with ventilatory impairment and can support proxy trend modelling of spirometry related indices, rather than direct spirometry replacement. The proposed framework demonstrates the feasibility of cough based physiological assessment for low cost respiratory screening and longitudinal monitoring, particularly where conventional spirometry is impractical. Larger multi centre studies are warranted to further evaluate generalizability and clinical integration.\n                  <\/jats:p>","DOI":"10.1088\/1361-6579\/ae968d","type":"journal-article","created":{"date-parts":[[2026,8,6]],"date-time":"2026-08-06T22:57:21Z","timestamp":1786057041000},"page":"085026","update-policy":"https:\/\/doi.org\/10.1088\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Pulmonary function estimation and reliability assessment using hybrid-optimized ensemble learning"],"prefix":"10.1088","volume":"47","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-7372-3882","authenticated-orcid":false,"given":"Jiangli","family":"Zhu","sequence":"first","affiliation":[{"name":"China Jiliang University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-3696-2346","authenticated-orcid":false,"given":"Dandan","family":"Yan","sequence":"additional","affiliation":[{"name":"China Jiliang University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chenlei","family":"Sun","sequence":"additional","affiliation":[{"name":"First Affiliated Hospital of Ningbo University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9467-4481","authenticated-orcid":false,"given":"Chen","family":"Pan","sequence":"additional","affiliation":[{"name":"China Jiliang University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-3868-5727","authenticated-orcid":false,"given":"Dan","family":"Shen","sequence":"additional","affiliation":[{"name":"Zhejiang University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1074-2601","authenticated-orcid":true,"given":"Feng","family":"Liu","sequence":"additional","affiliation":[{"name":"University of Queensland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7485-8638","authenticated-orcid":true,"given":"Wenlong","family":"Xu","sequence":"additional","affiliation":[{"name":"China Jiliang University"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"266","published-online":{"date-parts":[[2026,8,20]]},"reference":[{"key":"pmeaae968dbib1","first-page":"58","type":"journal-article","article-title":"Global initiative for chronic obstructive lung disease 2023 report: GOLD executive summary","volume":"4","author":"Agust\u00ed","year":"2022","journal-title":"J. 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All rights, including for text and data mining, AI training, and similar technologies, are reserved.","name":"copyright_information","label":"Copyright Information"},{"value":"2026-05-08","name":"date_received","label":"Date Received","group":{"name":"publication_dates","label":"Publication dates"}},{"value":"2026-08-06","name":"date_accepted","label":"Date Accepted","group":{"name":"publication_dates","label":"Publication dates"}},{"value":"2026-08-20","name":"date_epub","label":"Online publication date","group":{"name":"publication_dates","label":"Publication dates"}}]}}