{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,19]],"date-time":"2026-10-19T16:35:55Z","timestamp":1792427755133,"version":"4.5.0"},"reference-count":44,"publisher":"Springer Science and Business Media LLC","issue":"S3","license":[{"start":{"date-parts":[[2025,9,15]],"date-time":"2025-09-15T00:00:00Z","timestamp":1757894400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2025,9,15]],"date-time":"2025-09-15T00:00:00Z","timestamp":1757894400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Med Inform Decis Mak"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>The need for transparency and interpretability is a fundamental theme to be addressed by Artificial Intelligence (AI) research, especially in high-risk applications such as healthcare. In this work, we propose Fuzzy Sets in Probability Trees (FPT), a novel method that combines probabilistic trees and fuzzy logic. This approach is fully interpretable, providing clinicians with a tool generate and verify the entire clinical decision process.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>FPT extends the existing framework of Probabilistic Decision Trees by incorporating the uncertainty in the data, allowing for a flexible description of vague variables. Thus, FPTs enable the incorporation of domain knowledge in the form of fuzzy membership functions within the framework of probabilistic trees. Furthermore, FPTs can represent circumstances or explanations that cannot be represented with other techniques (e.g., Bayesian networks), paving the way to a novel form of interpretable AI that allows clinicians to generate, control and verify the entire diagnosis procedure; one of the strengths of our methodology is the capability to decrease the frequency of misdiagnoses by providing an estimate of uncertainties and counterfactuals.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>We applied FPT to two real medical scenarios: classifying malignant thyroid nodules, and predicting the risk of progression in chronic kidney disease patients. Our results show that FPTs can provide interpretable support to clinicians. We also show that FPT and its predictions can assist clinical practice in an intuitive manner, with the use of a user-friendly interface specifically designed for this purpose.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>The integration of probabilistic trees and fuzzy reasoning preserves the nuances that are generally lost in (probabilistic) decision trees due to the adoption of crisp thresholds, leading to hybrid trees that provide an AI system better aligned with human reasoning processes and that can effectively support clinicians in the diagnosis decision process.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1186\/s12911-025-03183-5","type":"journal-article","created":{"date-parts":[[2025,9,15]],"date-time":"2025-09-15T13:15:13Z","timestamp":1757942113000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Assisting clinical diagnosis with interpretable fuzzy probabilistic modelling"],"prefix":"10.1186","volume":"25","author":[{"given":"Giulia","family":"Capitoli","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7692-7203","authenticated-orcid":false,"given":"Marco S.","family":"Nobile","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Emma L.","family":"Ambags","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Vincenzo","family":"L\u2019Imperio","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Michele","family":"Provenzano","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pietro","family":"Li\u00f2","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,9,15]]},"reference":[{"issue":"17","key":"3183_CR1","doi-asserted-by":"crossref","first-page":"1564","DOI":"10.1056\/NEJMp2404691","volume":"391","author":"IS Kohane","year":"2024","unstructured":"Kohane IS. Compared with what? measuring AI against the health care we have. N Engl J Med. 2024;391(17):1564\u201366.","journal-title":"N Engl J Med"},{"key":"3183_CR2","doi-asserted-by":"crossref","first-page":"118083","DOI":"10.1016\/j.eswa.2022.118083","volume":"207","author":"Y Yang","year":"2022","unstructured":"Yang Y, Truong ND, Maher C, Nikpour A, Kavehei O. Continental generalization of a human-in-the-loop AI system for clinical seizure recognition. Expert Syst With Applications. 2022;207:118083.","journal-title":"Expert Syst With Applications"},{"key":"3183_CR3","doi-asserted-by":"crossref","unstructured":"Ahmad MA, Eckert C, Teredesai A. Interpretable machine learning in healthcare. Proceedings of the 2018 ACM international conference on bioinformatics, computational biology, and health informatics. 2018; pp. 559\u201360.","DOI":"10.1145\/3233547.3233667"},{"issue":"21","key":"3183_CR4","doi-asserted-by":"crossref","first-page":"2199","DOI":"10.1001\/jama.2018.17163","volume":"320","author":"EH Shortliffe","year":"2018","unstructured":"Shortliffe EH, Sep\u00falveda MJ. Clinical decision support in the era of artificial intelligence. JAMA. 2018;320(21):2199\u2013200.","journal-title":"JAMA"},{"issue":"6","key":"3183_CR5","doi-asserted-by":"crossref","first-page":"517","DOI":"10.1001\/jama.2017.7797","volume":"318","author":"F Cabitza","year":"2017","unstructured":"Cabitza F, Rasoini R, Gensini GF. Unintended consequences of machine learning in medicine. JAMA. 2017;318(6):517\u201318.","journal-title":"JAMA"},{"key":"3183_CR6","doi-asserted-by":"crossref","first-page":"117099","DOI":"10.1016\/j.eswa.2022.117099","volume":"201","author":"T-L Nguyen","year":"2022","unstructured":"Nguyen T-L, Kavuri S, Park S-Y, Lee M. Attentive hierarchical ANFIS with interpretability for cancer diagnostic. Expert Syst With Applications. 2022;201:117099.","journal-title":"Expert Syst With Applications"},{"key":"3183_CR7","unstructured":"European Commission: General Data Protection Regulation. 2016). Available from: https:\/\/eur-lex.europa.eu\/legal-content\/EN\/TXT\/?uri=celex%3A32016R0679"},{"key":"3183_CR8","unstructured":"European commission: laying down harmonised rules on artificial intelligence (artificial intelligence Act) and amending certain union legislative acts. Office for Official Publications of the European CommunitiesLuxembourg 2021. https:\/\/eur-lex.europa.eu\/legal-content\/EN\/TXT\/?qid=1623335154975&uri=CELEX&3A52021PC0206."},{"issue":"5","key":"3183_CR9","doi-asserted-by":"publisher","first-page":"206","DOI":"10.1038\/s42256-019-0048-x","volume":"1","author":"C Rudin","year":"2019","unstructured":"Rudin C. Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat Mach Intell. 2019;1(5):206\u201315.","journal-title":"Nat Mach Intell"},{"issue":"8","key":"3183_CR10","doi-asserted-by":"crossref","first-page":"1328","DOI":"10.1038\/s41591-021-01461-z","volume":"27","author":"S Kundu","year":"2021","unstructured":"Kundu S. AI in medicine must be explainable. Nat Med. 2021;27(8):1328\u20131328.","journal-title":"Nat Med"},{"key":"3183_CR11","doi-asserted-by":"crossref","unstructured":"Hakkoum H, Abnane I, Idri A. Interpretability in the medical field: a systematic mapping and review study. Appl Soft Comput. 2021;108391.","DOI":"10.1016\/j.asoc.2021.108391"},{"key":"3183_CR12","doi-asserted-by":"crossref","unstructured":"Burrell J. How the machine \u2018thinks\u2019: understanding opacity in machine learning algorithms. Big Data Soc. 2016;3(1).","DOI":"10.1177\/2053951715622512"},{"key":"3183_CR13","doi-asserted-by":"crossref","unstructured":"Ba\u015fa\u011fao\u011flu H, Chakraborty D, Lago CD, Gutierrez L, \u015eahinli MA, Giacomoni M, Furl C, Mirchi A, Moriasi D, \u015eeng\u00f6r S.S.: a review on interpretable and explainable artificial intelligence in hydroclimatic applications. Water. 2022;14(8):1230.","DOI":"10.3390\/w14081230"},{"key":"3183_CR14","doi-asserted-by":"crossref","unstructured":"Fuchs C, Spolaor S, Nobile MS, Kaymak. U.: pyFUME: a python package for fuzzy model estimation. 2020 IEEE international conference on fuzzy systems (FUZZ-IEEE). 2020, pp. 1\u20138). IEEE.","DOI":"10.1109\/FUZZ48607.2020.9177565"},{"key":"3183_CR15","unstructured":"Doran D, Schulz S, Besold. T.R.: What does explainable AI really mean? A new conceptualization of perspectives. arXiv preprint arXiv:1710.00794 (2017."},{"key":"3183_CR16","unstructured":"Genewein T, McGrath T, Del\u00e9tang G, Mikulik V, Martic M, Legg S, Ortega PA:. Algorithms for causal reasoning in probability trees. arXiv preprint arXiv:2010.12237 (2020."},{"key":"3183_CR17","volume-title":"Decision analysis: introductory lectures on choices under uncertainty","author":"H Raiffa","year":"1968","unstructured":"Raiffa H. Decision analysis: Introductory lectures on choices under uncertainty. Addison-Wesley; ???; 1968."},{"key":"3183_CR18","volume-title":"Models, reasoning and inference","author":"J Pearl","year":"2000","unstructured":"Pearl J, et al. Models, reasoning and inference. Vol. 19. Cambridge, UK: CambridgeUniversityPress; 2000."},{"issue":"3","key":"3183_CR19","doi-asserted-by":"crossref","first-page":"338","DOI":"10.1016\/S0019-9958(65)90241-X","volume":"8","author":"LA Zadeh","year":"1965","unstructured":"Zadeh LA. Fuzzy sets. Inf And Control. 1965;8(3):338\u201353.","journal-title":"Inf And Control"},{"key":"3183_CR20","unstructured":"World Health Organization: International classification of diseases (icd) 10th revision. WHO [Internet]. 2019."},{"key":"3183_CR21","doi-asserted-by":"crossref","unstructured":"Frates MC, Benson CB, Charboneau JW, Cibas ES, Clark OH, Coleman BG, Cronan JJ, Doubilet PM, Evans DB, Goellner JR, et al. Management of thyroid nodules detected at US: society of radiologists in ultrasound consensus conference statement. Radiology. 2005;237(3):794\u2013800.","DOI":"10.1148\/radiol.2373050220"},{"issue":"172","key":"3183_CR22","doi-asserted-by":"crossref","first-page":"188","DOI":"10.1016\/j.jss.2011.11.040","volume":"2","author":"L Bessey","year":"2012","unstructured":"Bessey L, Lai N, Coorough N, Chen H, Sippel R. The incidence of thyroid cancer by FNA varies by age and gender. J Surg Res. 2012;2(172):188.","journal-title":"J Surg Res"},{"issue":"2","key":"3183_CR23","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1159\/000494720","volume":"8","author":"B \u00d6cal","year":"2019","unstructured":"\u00d6cal B, Korkmaz MH, Y\u0131lmazer D, T\u00fcrkmeno\u011flu TT, Bay\u0131r \u00d6, Saylam G, Tatar E\u00c7, Karahan S, Cakal E:. The malignancy risk assessment of cytologically indeterminate thyroid nodules improves markedly by using a predictive model. Eur Thyroid J. 2019;8(2):83\u201389.","journal-title":"Eur Thyroid J"},{"issue":"19","key":"3183_CR24","doi-asserted-by":"crossref","first-page":"1788","DOI":"10.1001\/jamainternmed.2013.9245","volume":"173","author":"R Smith-Bindman","year":"2013","unstructured":"Smith-Bindman R, Lebda P, Feldstein VA, Sellami D, Goldstein RB, Brasic N, Jin C, Kornak J. Risk of thyroid cancer based on thyroid ultrasound imaging characteristics: results of a population-based study. JAMA Intern Med. 2013;173(19):1788\u201395.","journal-title":"JAMA Intern Med"},{"key":"3183_CR25","doi-asserted-by":"crossref","unstructured":"Leni D, Seminati D, Fior D, Vacirca F, Capitoli G, Cazzaniga L, Di Bella C, L\u2019Imperio V, Galimberti S, Pagni F. Diagnostic performances of the ACR-TIRADS system in thyroid nodules triage: a prospective single center study. Cancers. 2021) Available from: 13(9). https:\/\/www.mdpi.com\/2072-6694\/13\/9\/2230.","DOI":"10.3390\/cancers13092230"},{"issue":"21","key":"3183_CR26","doi-asserted-by":"crossref","first-page":"5439","DOI":"10.3390\/cancers13215439","volume":"13","author":"D Seminati","year":"2021","unstructured":"Seminati D, Capitoli G, Leni D, Fior D, Vacirca F, Di Bella C, Galimberti S, L\u2019Imperio V, Pagni F. Use of diagnostic criteria from ACR and EU-TIRADS systems to improve the performance of cytology in thyroid nodule triage. Cancers. 2021) Available from: 13(21):5439. https:\/\/europepmc.org\/articles\/PMC8582424.","journal-title":"Cancers"},{"issue":"3","key":"3183_CR27","doi-asserted-by":"crossref","first-page":"567","DOI":"10.1016\/j.kint.2018.04.011","volume":"94","author":"Y Xie","year":"2018","unstructured":"Xie Y, Bowe B, Mokdad AH, Xian H, Yan Y, Li T, Maddukuri G, Tsai C-Y, Floyd T, Al-Aly Z. Analysis of the global burden of disease study highlights the global, regional, and national trends of chronic kidney disease epidemiology from 1990 to 2016. Kidney Int. 2018;94(3):567\u201381.","journal-title":"Kidney Int"},{"issue":"1","key":"3183_CR28","doi-asserted-by":"publisher","first-page":"138","DOI":"10.1093\/ndt\/gfy217","volume":"35","author":"M Provenzano","year":"2018","unstructured":"Provenzano M, Chiodini P, Minutolo R, Zoccali C, Bellizzi V, Conte G, Locatelli F, Tripepi G, Del Vecchio L, Mallamaci F, Di Micco L, Russo D, Heerspink HJL, De Nicola L. Reclassification of chronic kidney disease patients for end-stage renal disease risk by proteinuria indexed to estimated glomerular filtration rate: Multicentre prospective study in nephrology clinics. Nephrol Dialysis Transplant. 2018) Available from: 35(1):138\u201347. https:\/\/doi.org\/10.1093\/ndt\/gfy217.","journal-title":"Nephrol Dialysis Transplant"},{"issue":"1","key":"3183_CR29","doi-asserted-by":"crossref","first-page":"18","DOI":"10.3390\/e23010018","volume":"23","author":"P Linardatos","year":"2020","unstructured":"Linardatos P, Papastefanopoulos V, Kotsiantis S. Explainable AI: a review of machine learning interpretability methods. Entropy. 2020;23(1):18.","journal-title":"Entropy"},{"issue":"3","key":"3183_CR30","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1145\/3236386.3241340","volume":"16","author":"ZC Lipton","year":"2018","unstructured":"Lipton ZC. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue. 2018;16(3):31\u201357.","journal-title":"Queue"},{"issue":"9","key":"3183_CR31","doi-asserted-by":"crossref","first-page":"4310","DOI":"10.1109\/TAI.2024.3439048","volume":"5","author":"H Abbass","year":"2024","unstructured":"Abbass H, Crockett K, Garibaldi J, Gegov A, Kaymak U, Sousa JMC. From explainable artificial intelligence (xAI) to understandable artificial intelligence (uAI). IEEE Trans Artif Intell. 2024;5(9):4310\u201314.","journal-title":"IEEE Trans Artif Intell"},{"key":"3183_CR32","doi-asserted-by":"crossref","unstructured":"Gallese C. Legal aspects of AI in the biomedical field. The role of interpretable models. Big Data Analysis And Artificial Intelligence For Medical Sciences. 2024.","DOI":"10.1002\/9781119846567.ch15"},{"key":"3183_CR33","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/978-1-4842-6603-8","volume-title":"Modern PyQt","author":"J Willman","year":"2021","unstructured":"Willman J. Overview of pyqt5. In: Modern PyQt. Springer, ???; 2021. p. 1\u201342."},{"key":"3183_CR34","unstructured":"McKinney W, et al. Pandas: a foundational python library for data analysis and statistics. Python for high performance and scientific computing. 2011;14(9):1\u20139."},{"issue":"3","key":"3183_CR35","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1109\/MCSE.2007.58","volume":"9","author":"TE Oliphant","year":"2007","unstructured":"Oliphant TE. Python for scientific computing. Comput. Sci. Eng. 2007;9(3):10\u201320.","journal-title":"Comput. Sci. Eng"},{"issue":"1","key":"3183_CR36","doi-asserted-by":"crossref","first-page":"1687","DOI":"10.2991\/ijcis.d.201012.002","volume":"13","author":"S Spolaor","year":"2020","unstructured":"Spolaor S, Fuchs C, Cazzaniga P, Kaymak U, Besozzi D, Nobile MS. Simpful: fuzzy logic made simple. Int J Comput Int Sys. 2020;13(1):1687\u201398.","journal-title":"Int J Comput Int Sys"},{"key":"3183_CR37","doi-asserted-by":"crossref","unstructured":"Papetti DM, Van Abeelen K, Davies R, Men\u00e8 R, Heilbron F, Perelli FP, Artico J, Seraphim A, Moon JC, Parati G, et al. An accurate and time-efficient deep learning-based system for automated segmentation and reporting of cardiac magnetic resonance-detected ischemic scar. Computer methods and programs in biomedicine. 2023;229:107321.","DOI":"10.1016\/j.cmpb.2022.107321"},{"key":"3183_CR38","doi-asserted-by":"crossref","first-page":"115927","DOI":"10.1016\/j.eswa.2021.115927","volume":"188","author":"NT Duc","year":"2022","unstructured":"Duc NT, Lee Y-M, Park JH, Lee B. An ensemble deep learning for automatic prediction of papillary thyroid carcinoma using fine needle aspiration cytology. Expert Syst With Applications. 2022;188:115927.","journal-title":"Expert Syst With Applications"},{"key":"3183_CR39","doi-asserted-by":"crossref","first-page":"118398","DOI":"10.1016\/j.eswa.2022.118398","volume":"209","author":"C Han","year":"2022","unstructured":"Han C, Pan S, Que W, Wang Z, Zhai Y, Shi L. Automated localization and severity period prediction of myocardial infarction with clinical interpretability based on deep learning and knowledge graph. Expert Syst With Applications. 2022;209:118398.","journal-title":"Expert Syst With Applications"},{"key":"3183_CR40","doi-asserted-by":"crossref","first-page":"119087","DOI":"10.1016\/j.eswa.2022.119087","volume":"213","author":"H Mehnatkesh","year":"2023","unstructured":"Mehnatkesh H, Jalali SMJ, Khosravi A, Nahavandi S. An intelligent driven deep residual learning framework for brain tumor classification using MRI images. Expert Syst With Applications. 2023;213:119087.","journal-title":"Expert Syst With Applications"},{"issue":"12","key":"3183_CR41","doi-asserted-by":"crossref","first-page":"100608","DOI":"10.1016\/j.modpat.2024.100608","volume":"37","author":"V L\u2019Imperio","year":"2024","unstructured":"L\u2019Imperio V, Coelho V, Cazzaniga G, Papetti DM, Del Carro F, Capitoli G, Marino M, Ceku J, Fusco N, Ivanova M, et al. Machine learning streamlines the morphometric characterization and multiclass segmentation of nuclei in different follicular thyroid lesions: Everything in a NUTSHELL. Mod Pathol. 2024;37(12):100608.","journal-title":"Mod Pathol"},{"key":"3183_CR42","doi-asserted-by":"crossref","unstructured":"Gallese C, Scantamburlo T, Manzoni L, Nobile MS. Investigating semi-automatic assessment of Data sets fairness by means of fuzzy logic. 2023 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB). 2023, pp. 1\u201310. IEEE.","DOI":"10.1109\/CIBCB56990.2023.10264913"},{"issue":"12","key":"3183_CR43","doi-asserted-by":"crossref","first-page":"2398","DOI":"10.1097\/HJH.0000000000001825","volume":"36","author":"S Borrelli","year":"2018","unstructured":"Borrelli S, Garofalo C, Mallamaci F, Tripepi G, Stanzione G, Provenzano M, Conte G, De Nicola L, Zoccali C, Minutolo R. Short-term blood pressure variability in nondialysis chronic kidney disease patients: correlates and prognostic role on the progression of renal disease. J Educ Chang Hypertension. 2018;36(12):2398\u2013405.","journal-title":"J Educ Chang Hypertension"},{"key":"3183_CR44","doi-asserted-by":"crossref","unstructured":"Tangherloni A, Cazzaniga P, Stranieri N, Buffa FM, Nobile. M.S.: A fast feature selection for interpretable modeling based on fuzzy inference systems. 2024 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB). 2024, pp. 1\u20138. IEEE.","DOI":"10.1109\/CIBCB58642.2024.10702107"}],"container-title":["BMC Medical Informatics and Decision Making"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12911-025-03183-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s12911-025-03183-5","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12911-025-03183-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,16]],"date-time":"2026-06-16T11:48:13Z","timestamp":1781610493000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1186\/s12911-025-03183-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,15]]},"references-count":44,"journal-issue":{"issue":"S3","published-online":{"date-parts":[[2025,9]]}},"alternative-id":["3183"],"URL":"https:\/\/doi.org\/10.1186\/s12911-025-03183-5","relation":{},"ISSN":["1472-6947"],"issn-type":[{"value":"1472-6947","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,9,15]]},"assertion":[{"value":"15 March 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"29 July 2025","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 August 2025","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 September 2025","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Project approved by the local ethical committee (FINAL-TIR PU 3581\/21).","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 that they have no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}},{"value":"This article has been published as part of BMC Medical informatics and Decision Making, Volume 25 Supplement 3, 2025: IEEE CIBCB 2023 - 20th IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology. The full contents of the supplement are available at\n                      \n                      .","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"About this supplement"}}],"article-number":"330"}}