{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,30]],"date-time":"2025-07-30T16:09:51Z","timestamp":1753891791563,"version":"3.41.2"},"reference-count":51,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2024,10,16]],"date-time":"2024-10-16T00:00:00Z","timestamp":1729036800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Neuroinform."],"abstract":"<jats:p>Machine learning (ML) methodologies for detecting Mild Cognitive Impairment (MCI) are progressively gaining prevalence to manage the vast volume of processed information. Nevertheless, the black-box nature of ML algorithms and the heterogeneity within the data may result in varied interpretations across distinct studies. To avoid this, in this proposal, we present the design of a decision support system that integrates a machine learning model represented using the Semantic Web Rule Language (SWRL) in an ontology with specialized knowledge in neuropsychological tests, the NIO ontology. The system\u2019s ability to detect MCI subjects was evaluated on a database of 520 neuropsychological assessments conducted in Spanish and compared with other well-established ML methods. Using the <jats:italic>F2<\/jats:italic> coefficient to minimize false negatives, results indicate that the system performs similarly to other well-established ML methods (<jats:italic>F2<jats:sub>TE2<\/jats:sub><\/jats:italic>\u2009=\u20090.830, only below bagging, <jats:italic>F2<jats:sub>BAG<\/jats:sub><\/jats:italic>\u2009=\u20090.832) while exhibiting other significant attributes such as explanation capability and data standardization to a common framework thanks to the ontological part. On the other hand, the system\u2019s versatility and ease of use were demonstrated with three additional use cases: evaluation of new cases even if the acquisition stage is incomplete (the case records have missing values), incorporation of a new database into the integrated system, and use of the ontology capabilities to relate different domains. This makes it a useful tool to support physicians and neuropsychologists in population-based screenings for early detection of MCI.<\/jats:p>","DOI":"10.3389\/fninf.2024.1378281","type":"journal-article","created":{"date-parts":[[2024,10,16]],"date-time":"2024-10-16T11:35:57Z","timestamp":1729078557000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Early detection of mild cognitive impairment through neuropsychological tests in population screenings: a decision support system integrating ontologies and machine learning"],"prefix":"10.3389","volume":"18","author":[{"given":"Alba","family":"G\u00f3mez-Valad\u00e9s","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rafael","family":"Mart\u00ednez-Tom\u00e1s","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sara","family":"Garc\u00eda-Herranz","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Atle","family":"Bj\u00f8rnerud","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mariano","family":"Rinc\u00f3n","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1965","published-online":{"date-parts":[[2024,10,16]]},"reference":[{"key":"ref1","doi-asserted-by":"publisher","first-page":"896","DOI":"10.1017\/S1355617706061078","article-title":"Role of frontal versus temporal cortex in verbal fluency as revealed by voxel-based lesion symptom mapping","volume":"12","author":"Baldo","year":"2006","journal-title":"J. Int. Neuropsychol. Soc."},{"key":"ref2","doi-asserted-by":"publisher","first-page":"113","DOI":"10.1016\/j.dadm.2016.02.001","article-title":"Novel verbal fluency scores and structural brain imaging for prediction of cognitive outcome in mild cognitive impairment","volume":"2","author":"Clark","year":"2016","journal-title":"Alzheimers Dement. Diagn. Assess. Dis. Monit."},{"key":"ref3","doi-asserted-by":"publisher","first-page":"433","DOI":"10.1016\/j.drudis.2013.10.012","article-title":"Big data in biomedicine","volume":"19","author":"Costa","year":"2014","journal-title":"Drug Discov. Today"},{"key":"ref4","doi-asserted-by":"publisher","first-page":"111","DOI":"10.3390\/brainsci7090111","article-title":"Problems in classifying mild cognitive impairment (MCI): one or multiple syndromes?","volume":"7","author":"D\u00edaz-Mardomingo","year":"2017","journal-title":"Brain Sci."},{"key":"ref5","first-page":"438","article-title":"Detecci\u00f3n precoz del deterioro cognitivo ligero de la tercera edad","volume":"20","author":"D\u00edaz-Mardomingo","year":"2008","journal-title":"Psicothema"},{"key":"ref6","doi-asserted-by":"publisher","first-page":"97","DOI":"10.3233\/JAD-170231","article-title":"Detecting at-risk Alzheimer\u2019s disease cases","volume":"60","author":"Fladby","year":"2017","journal-title":"J. Alzheimers Dis."},{"key":"ref17","first-page":"70","volume-title":"Incremental reduced error pruning","author":"F\u00fcrnkranz","year":"1994"},{"key":"ref7","doi-asserted-by":"publisher","first-page":"239","DOI":"10.1111\/jnp.12067","article-title":"Neuropsychological predictors of conversion to probable Alzheimer disease in ederly with mild cognitive impairment","volume":"10","author":"Garc\u00eda-Herranz","year":"2016","journal-title":"J. Neuropsychol."},{"key":"ref8","doi-asserted-by":"publisher","first-page":"826","DOI":"10.1080\/13825585.2019.1698710","article-title":"Accuracy of verbal fluency tests in the discrimination of mild cognitive impairment and probable Alzheimer\u2019s disease in older Spanish monolingual individuals","volume":"27","author":"Garc\u00eda-Herranz","year":"2019","journal-title":"Neuropsychol. Dev. Cogn. B Aging Neuropsychol."},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.3389\/fninf.2021.561691","article-title":"Integrative Base ontology for the research analysis of Alzheimer\u2019s disease-related mild cognitive impairment","volume":"15","author":"Gomez-Valades","year":"2021","journal-title":"Front. Neuroinform."},{"key":"ref10","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1007\/978-3-030-19591-5_5","article-title":"Ontologies for early detection of the Alzheimer disease and other neurodegenerative diseases","volume-title":"Understanding the brain function and emotions, lecture notes in computer science","author":"Gomez-Valad\u00e9s","year":"2019"},{"year":"2021","author":"Gupta","key":"ref11"},{"year":"1995","author":"Ho","key":"ref12"},{"key":"ref13","doi-asserted-by":"publisher","first-page":"1069","DOI":"10.1093\/bib\/bbv011","article-title":"The role of ontologies in biological and biomedical research: a functional perspective","volume":"16","author":"Hoehndorf","year":"2015","journal-title":"Brief. Bioinform."},{"year":"2015","author":"Ivascu","key":"ref14"},{"key":"ref15","doi-asserted-by":"publisher","first-page":"42","DOI":"10.1186\/2041-1480-4-42","article-title":"The neurological disease ontology","volume":"4","author":"Jensen","year":"2013","journal-title":"J. Biomed. Semant."},{"key":"ref16","doi-asserted-by":"publisher","first-page":"4284","DOI":"10.1038\/s41598-022-08231-y","article-title":"Searching for optimal machine learning model to classify mild cognitive impairment (MCI) subtypes using multimodal MRI data","volume":"12","author":"Jitsuishi","year":"2022","journal-title":"Sci. Rep."},{"key":"ref18","doi-asserted-by":"publisher","first-page":"231","DOI":"10.1186\/s12911-019-0974-x","article-title":"Prediction of cognitive impairment via deep learning trained with multi-center neuropsychological test data","volume":"19","author":"Kang","year":"2019","journal-title":"BMC Med. Inform. Decis. Mak."},{"key":"ref19","doi-asserted-by":"publisher","first-page":"198","DOI":"10.1159\/000487852","article-title":"Fully automatic speech-based analysis of the semantic verbal fluency task","volume":"45","author":"K\u00f6nig","year":"2018","journal-title":"Dement. Geriatr. Cogn. Disord."},{"key":"ref20","doi-asserted-by":"publisher","first-page":"032068","DOI":"10.1088\/1742-6596\/1015\/3\/032068","article-title":"Comparison of rule induction, decision trees and formal concept analysis approaches for classification","volume":"1015","author":"Kotelnikov","year":"2018","journal-title":"J. Phys. Conf. Ser."},{"key":"ref21","doi-asserted-by":"publisher","first-page":"422","DOI":"10.1093\/bioinformatics\/btz595","article-title":"Machine learning with biomedical ontologies","volume":"36","author":"Kulmanov","year":"2020","journal-title":"Bioinformatics"},{"key":"ref22","doi-asserted-by":"publisher","first-page":"bbaa199","DOI":"10.1093\/bib\/bbaa199","article-title":"Semantic similarity and machine learning with ontologies","volume":"22","author":"Kulmanov","year":"2021","journal-title":"Brief. Bioinform."},{"year":"2019","author":"Lakshmi","key":"ref23"},{"key":"ref24","first-page":"7","volume-title":"Using neural word embeddings in the analysis of the clinical semantic verbal fluency task","author":"Linz","year":"2017"},{"key":"ref25","first-page":"189","article-title":"Cognocitive mini-test (a simple practical test to detect intellectual changes in medical patients)","volume":"7","author":"Lobo","year":"1979","journal-title":"Actas Luso Esp. Neurol. Psiquiatr. Cienc. Afines"},{"key":"ref26","doi-asserted-by":"publisher","first-page":"15761","DOI":"10.1007\/s00521-018-3494-1","article-title":"On the analysis of speech and disfluencies for automatic detection of mild cognitive impairment","volume":"32","author":"L\u00f3pez-de-Ipi\u00f1a","year":"2018","journal-title":"Neural Comput. & Applic."},{"key":"ref27","doi-asserted-by":"publisher","first-page":"715","DOI":"10.14569\/IJACSA.2022.0130715","article-title":"An ontological model based on machine learning for predicting breast cancer","volume":"13","author":"Massari","year":"","journal-title":"Int. J. Adv. Comput. Sci. Appl."},{"key":"ref28","doi-asserted-by":"publisher","first-page":"2805","DOI":"10.11591\/eei.v11i5.4392","article-title":"Integration of ontology with machine learning to predict the presence of covid-19 based on symptoms","volume":"11","author":"Massari","year":"","journal-title":"Bull. Electr. Eng. Inform."},{"key":"ref29","doi-asserted-by":"publisher","first-page":"319","DOI":"10.13052\/jicts2245-800X.10212","article-title":"Diabetes prediction using machine learning algorithms and ontology","volume":"10","author":"Massari","year":"","journal-title":"J. ICT Stand."},{"key":"ref30","doi-asserted-by":"publisher","first-page":"1107","DOI":"10.3390\/make4040056","article-title":"Ontology completion with graph-based machine learning: a comprehensive evaluation","volume":"4","author":"Me\u017enar","year":"2022","journal-title":"Mach. Learn. Knowl. Extr."},{"key":"ref31","doi-asserted-by":"publisher","first-page":"4","DOI":"10.1145\/2757001.2757003","article-title":"The prot\u00e9g\u00e9 project: a look back and a look forward","volume":"1","author":"Musen","year":"2015","journal-title":"AI Matters"},{"volume-title":"Writing rules for the semantic web using SWRL and Jess","year":"2005","author":"O\u2019Connor","key":"ref32"},{"key":"ref33","doi-asserted-by":"publisher","first-page":"74","DOI":"10.3991\/ijoe.v17i08.23643","article-title":"A systematic review of clinical decision support systems in Alzheimer\u2019s disease domain","volume":"17","author":"Sherimon","year":"2021","journal-title":"Int. J. Onl. Eng."},{"key":"ref34","doi-asserted-by":"publisher","first-page":"633","DOI":"10.1097\/00019442-200508000-00002","article-title":"Current epidemiology of mild cognitive impairment and other predementia syndromes","volume":"13","author":"Panza","year":"2005","journal-title":"Am. J. Geriatr. Psychiatry"},{"key":"ref35","doi-asserted-by":"publisher","first-page":"292","DOI":"10.1016\/j.jbi.2011.11.008","article-title":"An ontology for clinical questions about the contents of patient notes","volume":"45","author":"Patrick","year":"2012","journal-title":"J. Biomed. Inform."},{"key":"ref36","doi-asserted-by":"publisher","first-page":"2825","DOI":"10.48550\/arXiv.1201.0490","article-title":"Scikit-learn: machine learning in Python","volume":"12","author":"Pedregosa","year":"2011","journal-title":"J. Mach. Learn. Res."},{"key":"ref37","doi-asserted-by":"publisher","first-page":"171","DOI":"10.2174\/1874609811104020171","article-title":"Evolution of specific cognitive subprofiles of mild cognitive impairment in a three-year longitudinal study","volume":"4","author":"Peraita","year":"2011","journal-title":"Curr. Aging Sci."},{"key":"ref38","doi-asserted-by":"publisher","first-page":"201","DOI":"10.1212\/WNL.0b013e3181cb3e25","article-title":"Alzheimer\u2019s disease neuroimaging initiative (ADNI)","volume":"74","author":"Petersen","year":"2010","journal-title":"Neurology"},{"key":"ref39","doi-asserted-by":"publisher","first-page":"214","DOI":"10.1111\/joim.12190","article-title":"Mild cognitive impairment: a concept in evolution","volume":"275","author":"Petersen","year":"2014","journal-title":"J. Intern. Med."},{"key":"ref40","doi-asserted-by":"publisher","first-page":"685","DOI":"10.1037\/0894-4105.20.6.685","article-title":"A new dissimilarity measure for finding semantic structure in category fluency data with implications for understanding memory organization in schizophrenia","volume":"20","author":"Prescott","year":"2006","journal-title":"Neuropsychology"},{"key":"ref41","doi-asserted-by":"publisher","first-page":"159","DOI":"10.1055\/s-0040-1701991","article-title":"Ontologies, knowledge representation, and machine learning for translational research: recent contributions","volume":"29","author":"Robinson","year":"2020","journal-title":"Yearb. Med. Inform."},{"key":"ref42","doi-asserted-by":"publisher","first-page":"19430","DOI":"10.1038\/s41598-022-23101-3","article-title":"Ontology-based feature engineering in machine learning workflows for heterogeneous epilepsy patient records","volume":"12","author":"Sahoo","year":"2022","journal-title":"Sci. Rep."},{"year":"2019","author":"Shoaip","key":"ref43"},{"key":"ref44","doi-asserted-by":"publisher","first-page":"3531","DOI":"10.32604\/cmc.2021.019069","article-title":"Alzheimer\u2019s disease diagnosis based on a semantic rule-based modeling and reasoning approach","volume":"69","author":"Shoaip","year":"2021","journal-title":"Comput. Mater. Contin."},{"key":"ref45","doi-asserted-by":"publisher","first-page":"31350","DOI":"10.1109\/ACCESS.2020.3048435","article-title":"A comprehensive fuzzy ontology-based decision support system for Alzheimer\u2019s disease diagnosis","volume":"9","author":"Shoaip","year":"2020","journal-title":"IEEE Access"},{"key":"ref46","doi-asserted-by":"publisher","first-page":"51","DOI":"10.1016\/j.websem.2007.03.004","article-title":"Pellet: a practical OWL-DL reasoner. Web Semant","volume":"5","author":"Sirin","year":"2007","journal-title":"Sci. Serv. Agents World Wide Web"},{"key":"ref47","doi-asserted-by":"crossref","first-page":"156","DOI":"10.1007\/978-3-540-73255-6_14","article-title":"Ontology \u2013 supported machine learning and decision support in biomedicine","volume-title":"Data integration in the life sciences, lecture notes in computer science","author":"Tsymbal","year":"2007"},{"key":"ref48","doi-asserted-by":"publisher","first-page":"899","DOI":"10.1080\/13803395.2015.1067290","article-title":"Neuropsychological test selection for cognitive impairment classification: a machine learning approach","volume":"37","author":"Weakley","year":"2015","journal-title":"J. Clin. Exp. Neuropsychol."},{"key":"ref49","doi-asserted-by":"publisher","first-page":"37","DOI":"10.1016\/0022-3956(82)90033-4","article-title":"Development and validation of a geriatric depression screening scale: a preliminary report","volume":"17","author":"Yesavage","year":"1982","journal-title":"J. Psychiatr. Res."},{"year":"2015","author":"Zekri","key":"ref50"},{"key":"ref51","doi-asserted-by":"publisher","first-page":"781","DOI":"10.1016\/j.cmpb.2013.12.023","article-title":"Ontology driven decision support for the diagnosis of mild cognitive impairment","volume":"113","author":"Zhang","year":"2014","journal-title":"Comput. Methods Prog. Biomed."}],"container-title":["Frontiers in Neuroinformatics"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fninf.2024.1378281\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,16]],"date-time":"2024-10-16T11:36:02Z","timestamp":1729078562000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fninf.2024.1378281\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10,16]]},"references-count":51,"alternative-id":["10.3389\/fninf.2024.1378281"],"URL":"https:\/\/doi.org\/10.3389\/fninf.2024.1378281","relation":{},"ISSN":["1662-5196"],"issn-type":[{"type":"electronic","value":"1662-5196"}],"subject":[],"published":{"date-parts":[[2024,10,16]]},"article-number":"1378281"}}