{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T13:35:39Z","timestamp":1773840939774,"version":"3.50.1"},"reference-count":42,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2024,1,19]],"date-time":"2024-01-19T00:00:00Z","timestamp":1705622400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Israeli Ministry of Innovation, Science and Technology","award":["3-15608"],"award-info":[{"award-number":["3-15608"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Bioengineering"],"abstract":"<jats:p>This study primarily aimed at developing a novel multi-dimensional methodology to discover and validate the optimal number of clusters. The secondary objective was to deploy it for the task of clustering fibromyalgia patients. We present a comprehensive methodology that includes the use of several different clustering algorithms, quality assessment using several syntactic distance measures (the Silhouette Index (SI), Calinski\u2013Harabasz index (CHI), and Davies\u2013Bouldin index (DBI)), stability assessment using the adjusted Rand index (ARI), and the validation of the internal semantic consistency of each clustering option via the performance of multiple clustering iterations after the repeated bagging of the data to select multiple partial data sets. Then, we perform a statistical analysis of the (clinical) semantics of the most stable clustering options using the full data set. Finally, the results are validated through a supervised machine learning (ML) model that classifies the patients back into the discovered clusters and is interpreted by calculating the Shapley additive explanations (SHAP) values of the model. Thus, we refer to our methodology as the clustering, distance measures and iterative statistical and semantic validation (CDI-SSV) methodology. We applied our method to the analysis of a comprehensive data set acquired from 1370 fibromyalgia patients. The results demonstrate that the K-means was highly robust in the syntactic and the internal consistent semantics analysis phases and was therefore followed by a semantic assessment to determine the optimal number of clusters (k), which suggested k = 3 as a more clinically meaningful solution, representing three distinct severity levels. the random forest model validated the results by classification into the discovered clusters with high accuracy (AUC: 0.994; accuracy: 0.946). SHAP analysis emphasized the clinical relevance of \"functional problems\" in distinguishing the most severe condition. In conclusion, the CDI-SSV methodology offers significant potential for improving the classification of complex patients. Our findings suggest a classification system for different profiles of fibromyalgia patients, which has the potential to improve clinical care, by providing clinical markers for the evidence-based personalized diagnosis, management, and prognosis of fibromyalgia patients.<\/jats:p>","DOI":"10.3390\/bioengineering11010097","type":"journal-article","created":{"date-parts":[[2024,1,19]],"date-time":"2024-01-19T09:46:39Z","timestamp":1705657599000},"page":"97","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Multi-Dimensional Validation of the Integration of Syntactic and Semantic Distance Measures for Clustering Fibromyalgia Patients in the Rheumatic Monitor Big Data Study"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0794-3653","authenticated-orcid":false,"given":"Ayelet","family":"Goldstein","sequence":"first","affiliation":[{"name":"Computer Science Department, Hadassah Academic College, Jerusalem 9101001, Israel"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0328-2333","authenticated-orcid":false,"given":"Yuval","family":"Shahar","sequence":"additional","affiliation":[{"name":"Medical Informatics Research Center, Department of Software and Information Systems Engineering, Ben Gurion University of the Negev, Beer Sheva 8410501, Israel"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Michal","family":"Weisman Raymond","sequence":"additional","affiliation":[{"name":"Medical Informatics Research Center, Department of Software and Information Systems Engineering, Ben Gurion University of the Negev, Beer Sheva 8410501, Israel"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hagit","family":"Peleg","sequence":"additional","affiliation":[{"name":"Rheumatology Unit, Hadassah Medical Center, Jerusalem 9112102, Israel"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Eldad","family":"Ben-Chetrit","sequence":"additional","affiliation":[{"name":"Rheumatology Unit, Hadassah Medical Center, Jerusalem 9112102, Israel"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Arie","family":"Ben-Yehuda","sequence":"additional","affiliation":[{"name":"Division of Internal Medicine, Hadassah Medical Center, Jerusalem 9112102, Israel"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Erez","family":"Shalom","sequence":"additional","affiliation":[{"name":"Medical Informatics Research Center, Department of Software and Information Systems Engineering, Ben Gurion University of the Negev, Beer Sheva 8410501, Israel"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chen","family":"Goldstein","sequence":"additional","affiliation":[{"name":"Faculty of Dental Medicine, Hebrew University of Jerusalem, Israel; Big Biomedical Data Research Laboratory, Dean\u2019s Office, Hadassah Medical Center, Jerusalem 91120, Israel"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shmuel Shay","family":"Shiloh","sequence":"additional","affiliation":[{"name":"Faculty of Dental Medicine, Hebrew University of Jerusalem, Israel; Big Biomedical Data Research Laboratory, Dean\u2019s Office, Hadassah Medical Center, Jerusalem 91120, Israel"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9671-0047","authenticated-orcid":false,"given":"Galit","family":"Almoznino","sequence":"additional","affiliation":[{"name":"Faculty of Dental Medicine, Hebrew University of Jerusalem, Israel; Big Biomedical Data Research Laboratory, Dean\u2019s Office, Hadassah Medical Center, Jerusalem 91120, Israel"},{"name":"Department of Oral Medicine, Sedation & Maxillofacial Imaging, Hadassah Medical Center, Faculty of Dental Medicine, Hebrew University of Jerusalem, Jerusalem 91120, Israel"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2024,1,19]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"611","DOI":"10.1016\/j.jpain.2018.10.008","article-title":"AAPT Diagnostic Criteria for Fibromyalgia","volume":"20","author":"Arnold","year":"2019","journal-title":"J. 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