{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,8]],"date-time":"2026-07-08T05:18:56Z","timestamp":1783487936944,"version":"3.55.0"},"reference-count":73,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2026,5,16]],"date-time":"2026-05-16T00:00:00Z","timestamp":1778889600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2026,7,8]],"date-time":"2026-07-08T00:00:00Z","timestamp":1783468800000},"content-version":"vor","delay-in-days":53,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Med Inform Decis Mak"],"DOI":"10.1186\/s12911-026-03568-0","type":"journal-article","created":{"date-parts":[[2026,5,16]],"date-time":"2026-05-16T01:24:15Z","timestamp":1778894655000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Interpretable machine learning methods based on oscillometry and electric modeling for the diagnostic of respiratory dysfunction in silicosis"],"prefix":"10.1186","volume":"26","author":[{"given":"Jorge Lu\u00eds Machado","family":"do Amaral","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"C\u00edntia Moraes","family":"de S\u00e1 Sousa","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Caroline","family":"de Oliveira Ribeiro","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Paula Morisco","family":"de S\u00e1","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Agnaldo Jos\u00e9","family":"Lopes","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pedro Lopes","family":"de Melo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,5,16]]},"reference":[{"issue":"9830","key":"3568_CR1","doi-asserted-by":"publisher","first-page":"2008","DOI":"10.1016\/S0140-6736(12)60235-9","volume":"379","author":"CC Leung","year":"2012","unstructured":"Leung CC, Yu ITS, Chen W. Silicosis Lancet. 2012;379(9830):2008\u201318.","journal-title":"Silicosis Lancet"},{"issue":"4","key":"3568_CR2","first-page":"275","volume":"53","author":"G Minelli","year":"2017","unstructured":"Minelli G, Zona A, Cavariani F, Comba P, Pasetto R. Silicosis mortality in Italy: temporal trends 1990\u20132012 and spatial patterns 2000\u20132012. Annali dell\u2019Istituto Superiore di Sanit\u00e0. 2017;53(4):275\u201382.","journal-title":"Annali dell\u2019Istituto Superiore di Sanit\u00e0"},{"issue":"3","key":"3568_CR3","doi-asserted-by":"publisher","first-page":"681","DOI":"10.1148\/radiol.13121415","volume":"270","author":"CW Cox","year":"2014","unstructured":"Cox CW, Rose CS, Lynch DA. State of the Art: Imaging of Occupational Lung Disease. Radiology. 2014;270(3):681\u201396.","journal-title":"Radiology"},{"issue":"15","key":"3568_CR4","doi-asserted-by":"publisher","first-page":"8123","DOI":"10.3390\/ijerph18158123","volume":"18","author":"EK Austin","year":"2021","unstructured":"Austin EK, James C, Tessier J. Early Detection Methods for Silicosis in Australia and Internationally: A Review of the Literature. IJERPH. 2021;18(15):8123.","journal-title":"IJERPH"},{"issue":"3","key":"3568_CR5","doi-asserted-by":"publisher","first-page":"205","DOI":"10.1097\/00130832-200106000-00002","volume":"1","author":"DA Kaminsky","year":"2001","unstructured":"Kaminsky DA, Irvin CG. New insights from lung function. Curr Opin Allergy Clin Immunol. 2001;1(3):205\u20139.","journal-title":"Curr Opin Allergy Clin Immunol"},{"key":"3568_CR6","doi-asserted-by":"crossref","unstructured":"Kaminsky DA, Simpson SJ, Berger KI, Calverley P, De Melo PL, Dandurand R, Dellac\u00e0 RL, Farah CS, Farr\u00e9 R, Hall GL. Clinical significance and applications of oscillometry. Eur respiratory Rev 2022, 31(163).","DOI":"10.1183\/16000617.0208-2021"},{"key":"3568_CR7","doi-asserted-by":"crossref","unstructured":"King GG, Bates J, Berger KI, Calverley P, de Melo PL, Dellaca RL, Farre R, Hall GL, Ioan I, Irvin CG et al. Technical standards for respiratory oscillometry. Eur Respir J 2020, 55(2).","DOI":"10.1183\/13993003.00753-2019"},{"key":"3568_CR8","doi-asserted-by":"crossref","unstructured":"Bates JHT. Lung mechanics: an inverse modeling approach, 1st Edition edn. Cambridge: Cambridge University Press; 2009.","DOI":"10.1017\/CBO9780511627156"},{"key":"3568_CR9","doi-asserted-by":"publisher","first-page":"11","DOI":"10.1186\/s12938-015-0007-7","volume":"14","author":"AN Lima","year":"2015","unstructured":"Lima AN, Faria AC, Lopes AJ, Jansen JM, Melo PL. Forced oscillations and respiratory system modeling in adults with cystic fibrosis. Biomed Eng Online. 2015;14:11.","journal-title":"Biomed Eng Online"},{"key":"3568_CR10","doi-asserted-by":"publisher","first-page":"12","DOI":"10.1016\/j.cmpb.2016.02.010","volume":"128","author":"AC Faria","year":"2016","unstructured":"Faria AC, Veiga J, Lopes AJ, Melo PL. Forced oscillation, integer and fractional-order modeling in asthma. Comput Methods Programs Biomed. 2016;128:12\u201326.","journal-title":"Comput Methods Programs Biomed"},{"issue":"9","key":"3568_CR11","doi-asserted-by":"publisher","first-page":"e0161981","DOI":"10.1371\/journal.pone.0161981","volume":"11","author":"PM de Sa","year":"2016","unstructured":"de Sa PM, Castro HA, Lopes AJ, Melo PL. Early Diagnosis of Respiratory Abnormalities in Asbestos-Exposed Workers by the Forced Oscillation Technique. PLoS ONE. 2016;11(9):e0161981.","journal-title":"PLoS ONE"},{"key":"3568_CR12","unstructured":"Ribeiro CO, Faria ACD, Lopes AJ, Melo PL. Early Diagnosis of the effects of smoking and chronic obstructive pulmonary disease based on forced oscillations and fractional-order modelling In: XXVI Congresso Brasileiro de Engenharia Biom\u00e9dica - CBEB 2018. vol. Aceito para apresenta\u00e7\u00e3o. B\u00fazios, Rio de Janeiro: Springer, The International Federation for Medical and Biological Engineering (IFMBE) Proceedings book series.; 2018."},{"key":"3568_CR13","doi-asserted-by":"publisher","first-page":"3273","DOI":"10.2147\/COPD.S276690","volume":"15","author":"CO Ribeiro","year":"2020","unstructured":"Ribeiro CO, Lopes AJ, de Melo PL. Oscillation Mechanics, Integer and Fractional Respiratory Modeling in COPD: Effect of Obstruction Severity. Int J Chronic Obstr Pulm Dis. 2020;15:3273\u201389.","journal-title":"Int J Chronic Obstr Pulm Dis"},{"key":"3568_CR14","doi-asserted-by":"publisher","first-page":"104135","DOI":"10.1016\/j.resp.2023.104135","volume":"316","author":"S Kostorz-Nosal","year":"2023","unstructured":"Kostorz-Nosal S, Jastrz\u0119bski D, B\u0142ach A, Skoczy\u0144ski S. Window of opportunity for respiratory oscillometry: A review of recent research. Respir Physiol Neurobiol. 2023;316:104135.","journal-title":"Respir Physiol Neurobiol"},{"issue":"3","key":"3568_CR15","doi-asserted-by":"publisher","first-page":"441","DOI":"10.1016\/j.cmpb.2013.08.004","volume":"112","author":"JLM Amaral","year":"2013","unstructured":"Amaral JLM, Lopes AJ, Jansen JM, Faria ACD, Melo PL. An improved method of early diagnosis of smoking-induced respiratory changes using machine learning algorithms. Comput Methods Programs Biomed. 2013;112(3):441\u201354.","journal-title":"Comput Methods Programs Biomed"},{"key":"3568_CR16","doi-asserted-by":"crossref","unstructured":"do Amaral JLM, de Melo PL. Clinical decision support systems to improve the diagnosis and management of respiratory diseases. In: Artificial intelligence in precision health. Edited by Barh D: Academic Press; 2020: 359\u2013391.","DOI":"10.1016\/B978-0-12-817133-2.00015-X"},{"key":"3568_CR17","first-page":"1394","volume":"2010","author":"JL Amaral","year":"2010","unstructured":"Amaral JL, Faria AC, Lopes AJ, Jansen JM, Melo PL. Automatic identification of Chronic Obstructive Pulmonary Disease Based on forced oscillation measurements and artificial neural networks. Conf Proc: Annual Int Conf IEEE Eng Med Biology Soc IEEE Eng Med Biology Soc Conf. 2010;2010:1394\u20137.","journal-title":"Conf Proc: Annual Int Conf IEEE Eng Med Biology Soc IEEE Eng Med Biology Soc Conf"},{"key":"3568_CR18","doi-asserted-by":"crossref","unstructured":"Zhao D, Mou X, Li Y, Yao Y, Du L, Li Z, Wang P, Li X, Chen X, Li X et al. The application of impulse oscillometry system based on machine learning algorithm in the diagnosis of chronic obstructive pulmonary disease. Physiol Meas 2024;45(5).","DOI":"10.1088\/1361-6579\/ad3d24"},{"key":"3568_CR19","doi-asserted-by":"publisher","first-page":"113","DOI":"10.1016\/j.cmpb.2017.03.023","volume":"144","author":"JL Amaral","year":"2017","unstructured":"Amaral JL, Lopes AJ, Veiga J, Faria AC, Melo PL. High-accuracy Detection of Airway Obstruction in Asthma Using Machine Learning Algorithms and Forced Oscillation Measurements. Comput Methods Programs Biomed. 2017;144:113\u201325.","journal-title":"Comput Methods Programs Biomed"},{"issue":"1","key":"3568_CR20","doi-asserted-by":"publisher","first-page":"336","DOI":"10.1186\/s12931-024-02960-6","volume":"25","author":"X Shen","year":"2024","unstructured":"Shen X, Liu H. Using machine learning for early detection of chronic obstructive pulmonary disease: a narrative review. Respir Res. 2024;25(1):336.","journal-title":"Respir Res"},{"issue":"12","key":"3568_CR21","doi-asserted-by":"publisher","first-page":"6924","DOI":"10.21037\/jtd-21-1379","volume":"13","author":"F Wu","year":"2021","unstructured":"Wu F, Zhou Y, Peng J, Deng Z, Wen X, Wang Z, Zheng Y, Tian H, Yang H, Huang P, et al. Rationale and design of the Early Chronic Obstructive Pulmonary Disease (ECOPD) study in Guangdong, China: a prospective observational cohort study. J Thorac Dis. 2021;13(12):6924\u201335.","journal-title":"J Thorac Dis"},{"issue":"1","key":"3568_CR22","doi-asserted-by":"publisher","first-page":"286","DOI":"10.1186\/s12931-024-02911-1","volume":"25","author":"WJ Xu","year":"2024","unstructured":"Xu WJ, Shang WY, Feng JM, Song XY, Li LY, Xie XP, Wang YM, Liang BM. Machine learning for accurate detection of small airway dysfunction-related respiratory changes: an observational study. Respir Res. 2024;25(1):286.","journal-title":"Respir Res"},{"key":"3568_CR23","unstructured":"Lima AD, Lopes AJ, Amaral JL, Melo PL. Explainable machine learning and respiratory oscillometry for the diagnosis of respiratory abnormalities in sarcoidosis. PLoS ONE 2022."},{"issue":"1","key":"3568_CR24","doi-asserted-by":"publisher","first-page":"31","DOI":"10.1186\/s12938-021-00865-9","volume":"20","author":"DSM Andrade","year":"2021","unstructured":"Andrade DSM, Ribeiro LM, Lopes AJ, Amaral JLM, Melo PL. Machine learning associated with respiratory oscillometry: a computer-aided diagnosis system for the detection of respiratory abnormalities in systemic sclerosis. Biomed Eng Online. 2021;20(1):31.","journal-title":"Biomed Eng Online"},{"issue":"1","key":"3568_CR25","doi-asserted-by":"publisher","first-page":"112","DOI":"10.1007\/s40846-023-00777-0","volume":"43","author":"NP Pinto","year":"2023","unstructured":"Pinto NP, Amaral JLM, Lopes AJ, Melo PL. Diagnosis of Respiratory Changes in Cystic Fibrosis Using a Soft Voting Ensemble with Bayesian Networks and Machine Learning Algorithms. J Med Biol Eng. 2023;43(1):112\u201323.","journal-title":"J Med Biol Eng"},{"issue":"6","key":"3568_CR26","doi-asserted-by":"publisher","first-page":"84","DOI":"10.1145\/3065386","volume":"60","author":"A Krizhevsky","year":"2017","unstructured":"Krizhevsky A, Sutskever I, Hinton GE. ImageNet classification with deep convolutional neural networks. Commun ACM. 2017;60(6):84\u201390.","journal-title":"Commun ACM"},{"key":"3568_CR27","doi-asserted-by":"crossref","unstructured":"Young T, Hazarika D, Poria S, Cambria E. Recent trends in deep learning based natural language processing. In.: arXiv. 2018.","DOI":"10.1109\/MCI.2018.2840738"},{"key":"3568_CR28","doi-asserted-by":"crossref","unstructured":"Michelsanti D, Tan Z-H, Zhang S-X, Xu Y, Yu M, Yu D, Jensen J. An overview of deep-learning-based audio-visual speech enhancement and separation. arXiv; 2020.","DOI":"10.1109\/TASLP.2021.3066303"},{"key":"3568_CR29","unstructured":"Mnih V, Kavukcuoglu K, Silver D, Graves A, Antonoglou I, Wierstra D, Riedmiller M. Playing atari with deep reinforcement learning. In.: arXiv 2013."},{"key":"3568_CR30","doi-asserted-by":"publisher","first-page":"84","DOI":"10.1016\/j.inffus.2021.11.011","volume":"81","author":"R Shwartz-Ziv","year":"2022","unstructured":"Shwartz-Ziv R, Armon A. Tabular data: Deep learning is not all you need. Inform Fusion. 2022;81:84\u201390.","journal-title":"Inform Fusion"},{"key":"3568_CR31","doi-asserted-by":"crossref","unstructured":"Grinsztajn L, Oyallon E, Varoquaux G. Why do tree-based models still outperform deep learning on tabular data? arXiv. 2022.","DOI":"10.52202\/068431-0037"},{"key":"3568_CR32","doi-asserted-by":"publisher","first-page":"110153","DOI":"10.1016\/j.compbiomed.2025.110153","volume":"191","author":"B Priego-Torres","year":"2025","unstructured":"Priego-Torres B, Sanchez-Morillo D, Khalili E, Conde-S\u00e1nchez M\u00c1, Garc\u00eda-G\u00e1mez A, Le\u00f3n-Jim\u00e9nez A. Automated engineered-stone silicosis screening and staging using Deep Learning with X-rays. Comput Biol Med. 2025;191:110153.","journal-title":"Comput Biol Med"},{"key":"3568_CR33","doi-asserted-by":"publisher","first-page":"100166","DOI":"10.1016\/j.cmpbup.2024.100166","volume":"6","author":"H Aksoy","year":"2024","unstructured":"Aksoy H, Atila \u00dc, Arslan S. Deep learning based detection of silicosis from computed tomography images. Comput Methods Programs Biomed Update. 2024;6:100166.","journal-title":"Comput Methods Programs Biomed Update"},{"key":"3568_CR34","doi-asserted-by":"crossref","unstructured":"Shivaanivarsha N, Kavipriya P. Identification of silicosis using deep learning model on computed tomography images. In: 2023 IEEE 3rd mysore sub section international conference (MysuruCon): 2023\/12\/01\/ 2023: IEEE. 2023: 1\u20136.","DOI":"10.1109\/MysuruCon59703.2023.10396879"},{"key":"3568_CR35","unstructured":"Sharma GK, Harjule P, Agarwal B, Kumar R. Silicosis detection using extended transfer learning model. In: Recent trends in image processing and pattern recognition. Edited by Santosh K, Makkar A, Conway M, Singh AK, Vacavant A, Abou El Kalam A, Bouguelia M-R, Hegadi R, vol. 2027. Cham: Springer Nature Switzerland; 2024: 111\u2013126."},{"key":"3568_CR36","doi-asserted-by":"publisher","first-page":"1450439","DOI":"10.3389\/fpubh.2024.1450439","volume":"12","author":"G-k Sun","year":"2025","unstructured":"Sun G-k, Xiang Y-h, Wang L, Xiang P-p, Wang Z-x, Zhang J, Wu L. Development of a multi-laboratory integrated predictive model for silicosis utilizing machine learning: a retrospective case-control study. Front Public Health. 2025;12:1450439.","journal-title":"Front Public Health"},{"key":"3568_CR37","doi-asserted-by":"crossref","unstructured":"Baker MJ, Gordon J, Thiruvarudchelvan A, Yates D, Donald WA. Rapid, non-invasive breath analysis for enhancing detection of silicosis using mass spectrometry and interpretable machine learning. J Breath Res 2025;19(2).","DOI":"10.1088\/1752-7163\/adbc11"},{"issue":"5","key":"3568_CR38","doi-asserted-by":"publisher","first-page":"e6486","DOI":"10.1590\/1414-431x20186486","volume":"51","author":"TP Chao","year":"2018","unstructured":"Chao TP, Sperandio EF, Ostolin TLVP, Almeida VR, Romiti M, Gagliardi ART, Arantes RL, Dourado VZ. Use of cardiopulmonary exercise testing to assess early ventilatory changes related to occupational particulate matter. Braz J Med Biol Res. 2018;51(5):e6486.","journal-title":"Braz J Med Biol Res"},{"key":"3568_CR39","unstructured":"ILO ILO. Guidelines for the use of the ILO international classification of radiographs of pneumoconioses In. Geneva international labour organization. 2022."},{"issue":"7","key":"3568_CR40","doi-asserted-by":"publisher","first-page":"2867","DOI":"10.1063\/1.1150705","volume":"71","author":"PL de Melo","year":"2000","unstructured":"de Melo PL, Werneck MM, Giannella-Neto A. New impedance spectrometer for scientific and clinical studies of the respiratory system. Rev Sci Instrum. 2000;71(7):2867\u201372.","journal-title":"Rev Sci Instrum"},{"issue":"3","key":"3568_CR41","doi-asserted-by":"publisher","first-page":"1233","DOI":"10.1002\/j.2040-4603.2011.tb00366.x","volume":"1","author":"JH Bates","year":"2011","unstructured":"Bates JH, Irvin CG, Farre R, Hantos Z. Oscillation mechanics of the respiratory system. Compr Physiol. 2011;1(3):1233\u201372.","journal-title":"Compr Physiol"},{"key":"3568_CR42","doi-asserted-by":"publisher","first-page":"667","DOI":"10.2147\/COPD.S446085","volume":"19","author":"EM Teixeira","year":"2024","unstructured":"Teixeira EM, Ribeiro CO, Lopes AJ, de Melo PL. Respiratory Oscillometry and Functional Performance in Different COPD Phenotypes. Int J Chronic Obstr Pulm Dis. 2024;19:667\u201382.","journal-title":"Int J Chronic Obstr Pulm Dis"},{"key":"3568_CR43","doi-asserted-by":"crossref","unstructured":"Patki N, Wedge R, Veeramachaneni K. The synthetic data vault. IEEE Int Conf Data Sci Adv Anal (DSAA). 2016: 399\u2013410.","DOI":"10.1109\/DSAA.2016.49"},{"key":"3568_CR44","unstructured":"Goodfellow IJ, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, Courville AC, Bengio Y. Generative adversarial nets. in: neural information processing systems: 2014; 2014."},{"key":"3568_CR45","doi-asserted-by":"crossref","unstructured":"El Naqa I, Murphy MJ. What is machine learning? Springer; 2015.","DOI":"10.1007\/978-3-319-18305-3_1"},{"issue":"3","key":"3568_CR46","doi-asserted-by":"publisher","first-page":"183","DOI":"10.1016\/j.cmpb.2011.09.009","volume":"105","author":"JLM Amaral","year":"2012","unstructured":"Amaral JLM, Lopes AJ, Jansen JM, Faria ACD, Melo PL. Machine learning algorithms and forced oscillation measurements applied to the automatic identification of chronic obstructive pulmonary disease. Comput Methods Programs Biomed. 2012;105(3):183\u201393.","journal-title":"Comput Methods Programs Biomed"},{"key":"3568_CR47","doi-asserted-by":"crossref","unstructured":"Hastie T, Tibshirani R, Friedman JH, Friedman JH. The elements of statistical learning: data mining, inference, and prediction. Springer. 2009;2.","DOI":"10.1007\/978-0-387-84858-7"},{"key":"3568_CR48","unstructured":"Dorogush AV, Ershov V, Gulin A. CatBoost: gradient boosting with categorical features support. arXiv. 2018."},{"issue":"4","key":"3568_CR49","first-page":"1","volume":"1","author":"T Chen","year":"2015","unstructured":"Chen T, He T, Benesty M, Khotilovich V, Tang Y, Cho H, Chen K, Mitchell R, Cano I, Zhou T, et al. Xgboost: extreme gradient boosting. R package version 04 \u2013 2. 2015;1(4):1\u20134.","journal-title":"R package version 04 \u2013 2"},{"key":"3568_CR50","unstructured":"Ke G, Meng Q, Finley T, Wang T, Chen W, Ma W, Ye Q, Liu T-Y. Lightgbm: a highly efficient gradient boosting decision tree. Adv Neural Inf Process Syst. 2017;30."},{"key":"3568_CR51","unstructured":"Nori H, Jenkins S, Koch P, Caruana R. InterpretML: A unified framework for machine learning interpretability. arXiv. 2019."},{"key":"3568_CR52","doi-asserted-by":"crossref","unstructured":"Hastie T, Tibshirani R. Generalized Additive Models. Statist Sci. 1986;1(3).","DOI":"10.1214\/ss\/1177013604"},{"key":"3568_CR53","doi-asserted-by":"crossref","unstructured":"Lou Y, Caruana R, Gehrke J, Hooker G. Accurate intelligible models with pairwise interactions. In: KDD\u2019 13: The 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining: 2013\/08\/11\/ 2013: ACM; 2013: 623\u2013631.","DOI":"10.1145\/2487575.2487579"},{"key":"3568_CR54","doi-asserted-by":"crossref","unstructured":"Wydma\u0144ski W, Bulenok O, \u015amieja M. HyperTab: hypernetwork approach for deep learning on small tabular datasets. arXiv. 2023.","DOI":"10.1109\/DSAA60987.2023.10302504"},{"issue":"1","key":"3568_CR55","doi-asserted-by":"publisher","first-page":"25","DOI":"10.4097\/kja.21209","volume":"75","author":"FS Nahm","year":"2022","unstructured":"Nahm FS. Receiver operating characteristic curve: overview and practical use for clinicians. Korean J Anesthesiol. 2022;75(1):25\u201336.","journal-title":"Korean J Anesthesiol"},{"key":"3568_CR56","doi-asserted-by":"crossref","unstructured":"Japkowicz N, Shah M. Evaluating learning algorithms: a classification perspective. Cambridge University Press; 2011.","DOI":"10.1017\/CBO9780511921803"},{"key":"3568_CR57","doi-asserted-by":"crossref","unstructured":"Greiner M, Pfeiffer D, Smith RD. Principles and practical application of the receiver-operating characteristic analysis for diagnostic tests. 2000, 45(1\u20132):23\u201341.","DOI":"10.1016\/S0167-5877(00)00115-X"},{"key":"3568_CR58","doi-asserted-by":"crossref","unstructured":"Akiba T, Sano S, Yanase T, Ohta T, Koyama M. Optuna: A next-generation hyperparameter optimization framework. 2019; 2019.","DOI":"10.1145\/3292500.3330701"},{"key":"3568_CR59","first-page":"2825","volume":"12","author":"F Pedregosa","year":"2011","unstructured":"Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, Blondel M, Prettenhofer P, Weiss R, Dubourg V, et al. Scikit-learn: Machine Learning in Python. J Mach Learn Res. 2011;12:2825\u201330.","journal-title":"J Mach Learn Res"},{"key":"3568_CR60","doi-asserted-by":"crossref","unstructured":"de Sa PM, Lopes AJ, Jansen JM, de Melo PL. Oscillation mechanics of the respiratory system in never-smoking patients with silicosis: pathophysiological study and evaluation of diagnostic accuracy. Clinics 2013;68(5).","DOI":"10.6061\/clinics\/2013(05)11"},{"issue":"1049","key":"3568_CR61","doi-asserted-by":"publisher","first-page":"20150028","DOI":"10.1259\/bjr.20150028","volume":"88","author":"AJ Lopes","year":"2015","unstructured":"Lopes AJ, Mogami R, Camilo GB, Machado DC, Melo PL, Carvalho AR. Relationships between the pulmonary densitometry values obtained by CT and the forced oscillation technique parameters in patients with silicosis. Br J Radiol. 2015;88(1049):20150028.","journal-title":"Br J Radiol"},{"key":"3568_CR62","doi-asserted-by":"publisher","first-page":"53","DOI":"10.1016\/j.cmpb.2019.02.003","volume":"172","author":"ACD Faria","year":"2019","unstructured":"Faria ACD, Carvalho ARS, Guimar\u00e3es ARM, Lopes AJ, Melo PL. Association of respiratory integer and fractional-order models with structural abnormalities in silicosis. Comput Methods Programs Biomed. 2019;172:53\u201363.","journal-title":"Comput Methods Programs Biomed"},{"issue":"12","key":"3568_CR63","doi-asserted-by":"publisher","first-page":"1295","DOI":"10.1590\/S1807-59322010001200012","volume":"65","author":"AC Faria","year":"2010","unstructured":"Faria AC, Costa AA, Lopes AJ, Jansen JM, Melo PL. Forced oscillation technique in the detection of smoking-induced respiratory alterations: diagnostic accuracy and comparison with spirometry. Clinics. 2010;65(12):1295\u2013304.","journal-title":"Clinics"},{"issue":"1","key":"3568_CR64","doi-asserted-by":"publisher","first-page":"124","DOI":"10.1038\/s41746-022-00648-z","volume":"5","author":"C Wang","year":"2022","unstructured":"Wang C, Ma J, Zhang S, Shao J, Wang Y, Zhou HY, Song L, Zheng J, Yu Y, Li W. Development and validation of an abnormality-derived deep-learning diagnostic system for major respiratory diseases. NPJ Digit Med. 2022;5(1):124.","journal-title":"NPJ Digit Med"},{"key":"3568_CR65","doi-asserted-by":"publisher","first-page":"102772","DOI":"10.1016\/j.eclinm.2024.102772","volume":"75","author":"Y Zhou","year":"2024","unstructured":"Zhou Y, Mei S, Wang J, Xu Q, Zhang Z, Qin S, Feng J, Li C, Xing S, Wang W, et al. Development and validation of a deep learning-based framework for automated lung CT segmentation and acute respiratory distress syndrome prediction: a multicenter cohort study. EClinicalMedicine. 2024;75:102772.","journal-title":"EClinicalMedicine"},{"key":"3568_CR66","doi-asserted-by":"crossref","unstructured":"Topalovic M, Das N, Burgel PR, Daenen M, Derom E, Haenebalcke C, Janssen R, Kerstjens HAM, Liistro G, Louis R, et al. Artificial intelligence outperforms pulmonologists in the interpretation of pulmonary function tests. Eur Respir J 2019;53(4).","DOI":"10.1183\/13993003.01660-2018"},{"key":"3568_CR67","volume-title":"Interpretation of pulmonary function tests","author":"RE Hyatt","year":"1997","unstructured":"Hyatt RE, Scandon PD, Nakamura M. Interpretation of pulmonary function tests. Phyladelphia: Lippincott-Raven; 1997."},{"key":"3568_CR68","unstructured":"Ljung L. System identification: theory for the user. Prentice-Hall, Inc. 1986."},{"key":"3568_CR69","doi-asserted-by":"publisher","first-page":"867883","DOI":"10.3389\/fped.2022.867883","volume":"10","author":"BL Radics","year":"2022","unstructured":"Radics BL, Gyurkovits Z, Makan G, Gingl Z, Cz\u00f6vek D, Hantos Z. Respiratory Oscillometry in Newborn Infants: Conventional and Intra-Breath Approaches. Front Pediatr. 2022;10:867883.","journal-title":"Front Pediatr"},{"issue":"1","key":"3568_CR70","doi-asserted-by":"publisher","first-page":"11","DOI":"10.1088\/0967-3334\/24\/1\/302","volume":"24","author":"LN Lemes","year":"2003","unstructured":"Lemes LN, Melo PL. Forced oscillation technique in the sleep apnoea\/hypopnoea syndrome: identification of respiratory events and nasal continuous positive airway pressure titration. Physiol Meas. 2003;24(1):11\u201325.","journal-title":"Physiol Meas"},{"issue":"6","key":"3568_CR71","doi-asserted-by":"publisher","first-page":"828","DOI":"10.1093\/bja\/aep301","volume":"103","author":"J Sellares","year":"2009","unstructured":"Sellares J, Acerbi I, Loureiro H, Dellaca RL, Ferrer M, Torres A, Navajas D, Farre R. Respiratory impedance during weaning from mechanical ventilation in a mixed population of critically ill patients. Br J Anaesth. 2009;103(6):828\u201332.","journal-title":"Br J Anaesth"},{"key":"3568_CR72","doi-asserted-by":"crossref","unstructured":"Agust\u00ed A, Celli BR, Criner GJ, Halpin D, Anzueto A, Barnes P, Bourbeau J, Han MK, Martinez FJ, Montes, de Oca M, et al. Global initiative for chronic obstructive lung Disease 2023 Report: GOLD executive summary. Eur Res J. 2023;61(4).","DOI":"10.1183\/13993003.00239-2023"},{"issue":"2","key":"3568_CR73","doi-asserted-by":"publisher","first-page":"1601270","DOI":"10.1183\/13993003.01270-2016","volume":"49","author":"L Andr\u00e1s","year":"2017","unstructured":"Andr\u00e1s L, Dorottya C, Zolt\u00e1n G, Gergely M, Bence R, D\u00f3ra B, Szabolcs S, J\u00e1nos G, Gy\u00f6rgy L, Peter DS, et al. Airway dynamics in COPD patients by within-breath impedance tracking: effects of continuous positive airway pressure. Eur Respir J. 2017;49(2):1601270.","journal-title":"Eur Respir J"}],"container-title":["BMC Medical Informatics and Decision Making"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s12911-026-03568-0","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12911-026-03568-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12911-026-03568-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,8]],"date-time":"2026-07-08T05:03:41Z","timestamp":1783487021000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1186\/s12911-026-03568-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,5,16]]},"references-count":73,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,12]]}},"alternative-id":["3568"],"URL":"https:\/\/doi.org\/10.1186\/s12911-026-03568-0","relation":{},"ISSN":["1472-6947"],"issn-type":[{"value":"1472-6947","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,5,16]]},"assertion":[{"value":"24 November 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 May 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 May 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The local Medical Research Ethics Committee approved this study (Ethics Committee at Pedro Ernesto University Hospital under number 1117-CEP\/HUPE). For inclusion in this study, all the volunteers had to sign informed consent forms. The study was conducted following the Declaration of Helsinki and Resolution 466\/12 of the National Health Council \u2013 Brazil.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not Applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"249"}}