{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,28]],"date-time":"2026-05-28T01:47:47Z","timestamp":1779932867791,"version":"3.53.1"},"reference-count":49,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2025,6,20]],"date-time":"2025-06-20T00:00:00Z","timestamp":1750377600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Brain Sciences"],"abstract":"<jats:p>Background\/Objectives: Sleep disorders are prevalent among aging populations and are often linked to cognitive decline, chronic conditions, and reduced quality of life. Traditional diagnostic methods, such as polysomnography, are resource-intensive and limited in accessibility. Meanwhile, individuals frequently describe their sleep experiences through unstructured narratives in clinical notes, online forums, and telehealth platforms. This study proposes a machine learning pipeline (sleepCare) that classifies sleep-related narratives into clinically meaningful categories, including stress-related, neurodegenerative, and breathing-related disorders. The proposed framework employs natural language processing (NLP) and machine learning techniques to support remote applications and real-time patient monitoring, offering a scalable solution for the early identification of sleep disturbances. Methods: The sleepCare consists of a three-tiered classification pipeline to analyze narrative sleep reports. First, a baseline model used a Multinomial Na\u00efve Bayes classifier with n-gram features from a Bag-of-Words representation. Next, a Support Vector Machine (SVM) was trained on GloVe-based word embeddings to capture semantic context. Finally, a transformer-based model (BERT) was fine-tuned to extract contextual embeddings, using the [CLS] token as input for SVM classification. Each model was evaluated using stratified train-test splits and 10-fold cross-validation. Hyperparameter tuning via GridSearchCV optimized performance. The dataset contained 475 labeled sleep narratives, classified into five etiological categories relevant for clinical interpretation. Results: The transformer-based model utilizing BERT embeddings and an optimized Support Vector Machine classifier achieved an overall accuracy of 81% on the test set. Class-wise F1-scores ranged from 0.72 to 0.91, with the highest performance observed in classifying normal or improved sleep (F1 = 0.91). The macro average F1-score was 0.78, indicating balanced performance across all categories. GridSearchCV identified the optimal SVM parameters (C = 4, kernel = \u2018rbf\u2019, gamma = 0.01, degree = 2, class_weight = \u2018balanced\u2019). The confusion matrix revealed robust classification with limited misclassifications, particularly between overlapping symptom categories such as stress-related and neurodegenerative sleep disturbances. Conclusions: Unlike generic large language model applications, our approach emphasizes the personalized identification of sleep symptomatology through targeted classification of the narrative input. By integrating structured learning with contextual embeddings, the framework offers a clinically meaningful, scalable solution for early detection and differentiation of sleep disorders in diverse, real-world, and remote settings.<\/jats:p>","DOI":"10.3390\/brainsci15070667","type":"journal-article","created":{"date-parts":[[2025,6,20]],"date-time":"2025-06-20T05:17:42Z","timestamp":1750396662000},"page":"667","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["From Narratives to Diagnosis: A Machine Learning Framework for Classifying Sleep Disorders in Aging Populations: The sleepCare Platform"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8372-8915","authenticated-orcid":false,"given":"Christos A.","family":"Frantzidis","sequence":"first","affiliation":[{"name":"School of Engineering and Physical Sciences, University of Lincoln, Lincoln LN6 7TS, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,6,20]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"6","DOI":"10.1016\/j.smrv.2015.12.006","article-title":"Sleep and emotion regulation: An organizing, integrative review","volume":"31","author":"Palmer","year":"2017","journal-title":"Sleep Med. Rev."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"S7","DOI":"10.1016\/j.sleep.2009.07.004","article-title":"Sleep and its disorders in aging populations","volume":"10","year":"2009","journal-title":"Sleep Med."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"eaax7331","DOI":"10.1126\/sciadv.aax7331","article-title":"Sleep fragmentation, microglial aging, and cognitive impairment in adults with and without Alzheimer\u2019s dementia","volume":"5","author":"Kaneshwaran","year":"2019","journal-title":"Sci. Adv."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Merrill, R.M. (2022). Mental health conditions according to stress and sleep disorders. Int. J. Environ. Res. Public Health, 19.","DOI":"10.3390\/ijerph19137957"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1016\/j.jsmc.2017.09.006","article-title":"Neurodegenerative disorders and sleep","volume":"13","author":"Malhotra","year":"2018","journal-title":"Sleep Med. Clin."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Koyanagi, A., Garin, N., Olaya, B., Ayuso-Mateos, J.L., Chatterji, S., Leonardi, M., Koskinen, S., Tobiasz-Adamczyk, B., and Haro, J.M. (2014). Chronic conditions and sleep problems among adults aged 50 years or over in nine countries: A multi-country study. PLoS ONE, 9.","DOI":"10.1371\/journal.pone.0114742"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1007\/s11325-009-0281-3","article-title":"Prevalence and impact of sleep disorders and sleep habits in the United States","volume":"14","author":"Ram","year":"2010","journal-title":"Sleep Breath."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1299","DOI":"10.1503\/cmaj.060792","article-title":"Sleep and aging: 1. Sleep disorders commonly found in older people","volume":"176","author":"Wolkove","year":"2007","journal-title":"CMAJ"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"e2013573","DOI":"10.1001\/jamanetworkopen.2020.13573","article-title":"Association between sleep duration and cognitive decline","volume":"3","author":"Ma","year":"2020","journal-title":"JAMA Netw. Open"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1016\/j.neuron.2017.02.004","article-title":"Sleep and human aging","volume":"94","author":"Mander","year":"2017","journal-title":"Neuron"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"343","DOI":"10.1016\/S1389-9457(03)00059-5","article-title":"Sleep disorders and depressive feelings: A global survey with the Beck depression scale","volume":"4","author":"Vandeputte","year":"2003","journal-title":"Sleep Med."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"481","DOI":"10.1007\/s12529-016-9609-0","article-title":"Sedentary behavior and sleep problems: A systematic review and meta-analysis","volume":"24","author":"Yang","year":"2017","journal-title":"Int. J. Behav. Med."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"104","DOI":"10.1038\/s41386-019-0478-5","article-title":"Bidirectional relationship between sleep and Alzheimer\u2019s disease: Role of amyloid, tau, and other factors","volume":"45","author":"Wang","year":"2020","journal-title":"Neuropsychopharmacology"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"238","DOI":"10.1136\/oemed-2012-101091","article-title":"Shift-related sleep problems vary according to work schedule","volume":"70","author":"Flo","year":"2013","journal-title":"Occup. Environ. Med."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"233","DOI":"10.1111\/j.1744-1609.2009.00142.x","article-title":"Review of systematic reviews about the efficacy of non-pharmacological interventions to improve sleep quality in insomnia","volume":"7","author":"Tiemens","year":"2009","journal-title":"Int. J. Evid.-Based Healthc."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Lam, C., Lai, F.C., Wang, C.H., Lai, M.H., Hsu, N., and Chung, M.H. (2016). Text mining of journal articles for sleep disorder terminologies. PLoS ONE, 11.","DOI":"10.1371\/journal.pone.0156031"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"364","DOI":"10.1093\/jamia\/ocy173","article-title":"Natural language processing of symptoms documented in free-text narratives of electronic health records: A systematic review","volume":"26","author":"Koleck","year":"2019","journal-title":"J. Am. Med. Inform. Assoc."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"342","DOI":"10.1038\/s41746-024-01321-3","article-title":"Natural language processing in mixed-methods evaluation of a digital sleep-alcohol intervention for young adults","volume":"7","author":"Griffith","year":"2024","journal-title":"NPJ Digit. Med."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"665","DOI":"10.1016\/j.sleh.2024.08.011","article-title":"ChatGPT vs. sleep disorder specialist responses to common sleep queries: Ratings by experts and laypeople","volume":"10","author":"Kim","year":"2024","journal-title":"Sleep Health"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"579","DOI":"10.1016\/j.actaastro.2018.11.043","article-title":"Cortical connectivity analysis for assessing the impact of microgravity and the efficacy of reactive sledge jumps countermeasure to NREM 2 sleep","volume":"166","author":"Frantzidis","year":"2020","journal-title":"Acta Astronaut."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Chriskos, P., Frantzidis, C.A., Plomariti, C.S., Papanastasiou, E., Pataka, A., Kourtidou-Papadeli, C., and Bamidis, P.D. (2025). SmartHypnos: An Android application for low-cost sleep self-monitoring and personalized recommendation generation. Comput. Biol. Med., 184.","DOI":"10.1016\/j.compbiomed.2024.109306"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"113","DOI":"10.1109\/TNNLS.2019.2899781","article-title":"Automatic sleep staging employing convolutional neural networks and cortical connectivity images","volume":"31","author":"Chriskos","year":"2019","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"e55762","DOI":"10.2196\/55762","article-title":"Assessing the accuracy of generative conversational artificial intelligence in debunking sleep health myths: Mixed methods comparative study with expert analysis","volume":"8","author":"Bragazzi","year":"2024","journal-title":"JMIR Form. Res."},{"key":"ref_24","unstructured":"(2025, April 27). Available online: https:\/\/www.sleepfoundation.org\/."},{"key":"ref_25","unstructured":"(2025, April 27). Available online: https:\/\/sleepeducation.org\/."},{"key":"ref_26","unstructured":"(2025, April 27). Available online: https:\/\/www.cdc.gov\/sleep\/."},{"key":"ref_27","unstructured":"(2025, April 27). Available online: https:\/\/www.ninds.nih.gov\/health-information\/disorders\/sleep-disorders."},{"key":"ref_28","unstructured":"(2025, April 27). Available online: https:\/\/worldsleepsociety.org\/."},{"key":"ref_29","unstructured":"(2025, April 27). Available online: https:\/\/github.com\/cfrantzidis\/sleepCare."},{"key":"ref_30","unstructured":"Devlin, J., Chang, M.W., Lee, K., and Toutanova, K. (2019). Bert: Pre-training of deep bidirectional transformers for language understanding. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), Association for Computational Linguistics."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Kowsari, K., Jafari Meimandi, K., Heidarysafa, M., Mendu, S., Barnes, L., and Brown, D. (2019). Text classification algorithms: A survey. Information, 10.","DOI":"10.3390\/info10040150"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1038\/s41746-020-0244-4","article-title":"The future of sleep health: A data-driven revolution in sleep science and medicine","volume":"3","author":"Zhai","year":"2020","journal-title":"NPJ Digit. Med."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1103","DOI":"10.5664\/jcsm.9194","article-title":"The use of telemedicine for the diagnosis and treatment of sleep disorders: An American Academy of Sleep Medicine update","volume":"17","author":"Bae","year":"2021","journal-title":"J. Clin. Sleep Med."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"1232","DOI":"10.1016\/j.chest.2020.09.249","article-title":"Disparities in sleep health and potential intervention models: A focused review","volume":"159","author":"Billings","year":"2021","journal-title":"Chest"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"e47735","DOI":"10.2196\/47735","article-title":"The role of novel digital clinical tools in the screening or diagnosis of obstructive sleep apnea: Systematic review","volume":"25","author":"Duarte","year":"2023","journal-title":"J. Med. Internet Res."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"193","DOI":"10.1016\/0165-1781(89)90047-4","article-title":"The Pittsburgh Sleep Quality Index: A new instrument for psychiatric practice and research","volume":"28","author":"Buysse","year":"1989","journal-title":"Psychiatry Res."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"20","DOI":"10.1145\/3639372","article-title":"Explainability for large language models: A survey","volume":"15","author":"Zhao","year":"2024","journal-title":"ACM Trans. Intell. Syst. Technol."},{"key":"ref_38","unstructured":"Zhang, X., Zhao, J., and LeCun, Y. (2015). Character-level convolutional networks for text classification. Adv. Neural Inf. Process. Syst., 28."},{"key":"ref_39","first-page":"98","article-title":"A Bayesian approach to filtering junk e-mail","volume":"Volume 62","author":"Sahami","year":"1998","journal-title":"Learning for Text Categorization: Papers from the 1998 Workshop"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Pennington, J., Socher, R., and Manning, C.D. (2014). Glove: Global vectors for word representation. Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), Association for Computational Linguistics.","DOI":"10.3115\/v1\/D14-1162"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Joachims, T. (1998). Text categorization with support vector machines: Learning with many relevant features. European Conference on Machine Learning, Springer.","DOI":"10.1007\/BFb0026683"},{"key":"ref_42","first-page":"3133","article-title":"Do we need hundreds of classifiers to solve real world classification problems?","volume":"15","author":"Cernadas","year":"2014","journal-title":"J. Mach. Learn. Res."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Sun, C., Qiu, X., Xu, Y., and Huang, X. (2019). How to fine-tune bert for text classification. China National Conference on Chinese Computational Linguistics, Springer International Publishing.","DOI":"10.1007\/978-3-030-32381-3_16"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"373","DOI":"10.1007\/s10994-019-05855-6","article-title":"A survey on semi-supervised learning","volume":"109","author":"Hoos","year":"2020","journal-title":"Mach. Learn."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"1263","DOI":"10.1109\/TKDE.2008.239","article-title":"Learning from imbalanced data","volume":"21","author":"He","year":"2009","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"1449","DOI":"10.1109\/JPROC.2015.2460697","article-title":"Multimodal data fusion: An overview of methods, challenges, and prospects","volume":"103","author":"Lahat","year":"2015","journal-title":"Proc. IEEE"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"3057","DOI":"10.1109\/TMM.2018.2829162","article-title":"A multiview multimodal system for monitoring patient sleep","volume":"20","author":"Torres","year":"2018","journal-title":"IEEE Trans. Multimed."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"2199","DOI":"10.1001\/jama.2018.17163","article-title":"Clinical decision support in the era of artificial intelligence","volume":"320","author":"Shortliffe","year":"2018","journal-title":"JAMA"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"2613","DOI":"10.1038\/s41591-024-03097-1","article-title":"Evaluation and mitigation of the limitations of large language models in clinical decision-making","volume":"30","author":"Hager","year":"2024","journal-title":"Nat. Med."}],"container-title":["Brain Sciences"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2076-3425\/15\/7\/667\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T17:55:28Z","timestamp":1760032528000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2076-3425\/15\/7\/667"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,6,20]]},"references-count":49,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2025,7]]}},"alternative-id":["brainsci15070667"],"URL":"https:\/\/doi.org\/10.3390\/brainsci15070667","relation":{},"ISSN":["2076-3425"],"issn-type":[{"value":"2076-3425","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,6,20]]}}}