[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-116907-en":3,"doc-seo-116907-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},116907,687197100911,"Himbo","https://ap-avatar.wpscdn.com/avatar/a000239b6f1da00475?x-image-process=image/resize,m_fixed,w_180,h_180&k=1785132997149421697",7,"Healthcare","Design of an Intelligent Decision Support System Applied to the Diagnosis of Obstructive Sleep Apnea","Obstructive sleep apnea (OSA), defined by recurrent partial or complete upper-airway obstruction during sleep, drives high global incidence and strains diagnostic services through prolonged waiting lists. This paper designs and develops an intelligent clinical decision support system for identifying patients suspected of OSA. Two heterogeneous information sets are integrated: objective health-profile data from electronic health records and subjective symptom data from patient interviews. Processing combines machine-learning classification with cascading fuzzy expert systems to produce two risk indicators, enabling severity interpretation and clinical alerts. Initial testing uses a dataset of 4400 patients and shows promising utility.","diagnostics  \nArticle  \nDesign of an Intelligent Decision Support System Applied to the Diagnosis of Obstructive Sleep Apnea  \nManuel Casal-Guisande 1,2, *,†, Laura Ceide-Sandoval 2, *,†, Mar Mosteiro-Añân 3,4, *, Mar½a Torres-Dur¡n 3,4, Jorge Cerqueiro-Pequeño 1,2, Jos²-Benito Bouza-Rodr½guez 1,2, Alberto Fern¡ndez-Villar 3,4  \nand Alberto Comesaña-Campos 1,2,†  \nCitation: Casal-Guisande, M.;  \nCeide-Sandoval, L.;  \nMosteiro-Añón, M.; Torres-Durán, M.; Cerqueiro-Pequeño, J.;  \nBouza-Rodríguez, J.-B.;  \nFernández-Villar, A.;  \nComesaña-Campos, A. Design of an Intelligent Decision Support System Applied to the Diagnosis of Obstructive Sleep Apnea. Diagnostics 2023, 13, 1854. [https://doi.org/](https://doi.org/)  \n[10.3390/diagnostics13111854](10.3390/diagnostics13111854)[ ](10.3390/diagnostics13111854)Academic Editors: Peter K. Panegyres and Koichi Nishimura  \nReceived: 7 February 2023  \nRevised: 7 May 2023  \nAccepted: 22 May 2023  \nPublished: 25 May 2023  \nCopyright: © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://](https://)[ ](https://)[creativecommons.org/licenses/by/](creativecommons.org/licenses/by/)[ ](creativecommons.org/licenses/by/)[4.0/](4.0/)) .  \n1 Department of Design in Engineering, University of Vigo, 36208 Vigo, Spain; jcerquei@uvigo.es (J.C.-P.); [jbouza@uvigo.es](jbouza@uvigo.es) (J.-B.B.-R.); acomesana@uvigo.es (A.C.-C.)  \n2 Design, Expert Systems and Artificial Intelligent Solutions Group (DESAINS), Galicia Sur Health Research Institute (IIS Galicia Sur), SERGAS-UVIGO, 36213 Vigo, Spain  \n3 Pulmonary Department, Hospital 􀂁lvaro Cunqueiro, 36213 Vigo, Spain;  \nmaria.luisa.torres.duran@sergas.es (M.T.-D.); [alberto.fernandez.villar@sergas.es](alberto.fernandez.villar@sergas.es) (A.F.-V.)  \n4 NeumoVigo I+i Research Group, Galicia Sur Health Research Institute (IIS Galicia Sur), SERGAS-UVIGO, 36213 Vigo, Spain  \n* Correspondence: manuel.casal.guisande@uvigo.es (M.C.-G.); [laura.ceide.sandoval@uvigo.es](laura.ceide.sandoval@uvigo.es) (L.C.-S.);  \n[mar.mosteiro.anon@sergas.es](mar.mosteiro.anon@sergas.es) (M.M.-A.)† These authors contributed equally to this work.  \nAbstract: Obstructive sleep apnea (OSA), characterized by recurrent episodes of partial or total obstruction of the upper airway during sleep, is currently one of the respiratory pathologies with the highest incidence worldwide. This situation has led to an increase in the demand for medical appointments and specific diagnostic studies, resulting in long waiting lists, with all the health consequences that this entails for the affected patients. In this context, this paper proposes the design and development of a novel intelligent decision support system applied to the diagnosis of OSA, aiming to identify patients suspected of suffering from the pathology. For this purpose, two sets of heterogeneous information are considered. The first one includes objective data related to the patient's health profile, with information usually available in electronic health records (anthropometric information, habits, diagnosed conditions and prescribed treatments) . The second type includes subjective data related to the specific OSA symptomatology reported by the patient in a specific interview. For the processing of this information, a machine-learning classification algorithm and a set of fuzzy expert systems arranged in cascade are used, obtaining, as a result, two indicators related to the risk of suffering from the disease. Subsequently, by interpreting both risk indicators, it will be possible to determine the severity of the patients' condition and to generate alerts. For the initial tests, a software artifact was built using a dataset with 4400 patients from the 􀂁lvaro Cunqueiro Hospital (Vigo, Galicia, Spain) . The preliminary results obtained are promising and demonstrate the potential usefulness of this type o","cbCaibcY4cXcBIJC","https://ap.wps.com/l/cbCaibcY4cXcBIJC","pdf",16600666,1,32,"English","en",105,"# Introduction\n## Obstructive sleep apnea overview\n## Diagnostic need and current standards\n# Materials and Methods\n## Information sources: objective and subjective data\n## Intelligent processing: machine learning and fuzzy expert systems\n## Dataset and preliminary testing setup\n# Results and Discussion\n## Initial performance and risk indicator interpretation\n## Potential clinical usefulness\n# Conclusion","[{\"question\":\"What problem does the proposed intelligent decision support system address?\",\"answer\":\"It targets the demand and delays in diagnosing obstructive sleep apnea, helping identify patients suspected of OSA earlier to reduce downstream health consequences.\"},{\"question\":\"What types of data does the system use for diagnosis?\",\"answer\":\"It uses objective patient data from electronic health records (e.g., anthropometrics, habits, conditions, treatments) and subjective symptom information collected during a patient interview.\"},{\"question\":\"How are the system’s risk indicators produced and used?\",\"answer\":\"A machine-learning classification algorithm and cascading fuzzy expert systems process the two data sets to generate two OSA risk indicators, which are then interpreted to assess severity and trigger alerts.\"}]","Design of an Intelligent Decision Support System Applied to the Diagnosis of Obstructive Sleep Apnea | 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