{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,4]],"date-time":"2025-10-04T00:34:01Z","timestamp":1759538041646,"version":"build-2065373602"},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"value":"9781643686295","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,10,2]],"date-time":"2025-10-02T00:00:00Z","timestamp":1759363200000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,10,2]]},"abstract":"<jats:p>Insomnia is a common but often underdiagnosed condition in clinical settings, where relevant information is typically buried in unstructured free-text notes. Automated tools that can identify both the presence of insomnia and the supporting evidence are essential to improve diagnosis and enable large-scale studies. However, existing models often prioritize accuracy at the cost of interpretability, which is critical for clinical adoption. To address this, we explore a hybrid approach that balances performance with explainability. Our method combines Finite Context Models (FCMs) for character-level classification of insomnia status with a BERT-based token classification model for extracting textual evidence, using structured annotations from the MIMIC-III dataset. This complementary setup enables both accurate prediction and transparent decision-making in clinical text analysis.<\/jats:p>","DOI":"10.3233\/shti251525","type":"book-chapter","created":{"date-parts":[[2025,10,3]],"date-time":"2025-10-03T09:57:48Z","timestamp":1759485468000},"source":"Crossref","is-referenced-by-count":0,"title":["Combining Statistical and Deep Learning Models for Insomnia Detection"],"prefix":"10.3233","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-6728-3089","authenticated-orcid":false,"given":"Lu\u00eds Carlos","family":"Afonso","sequence":"first","affiliation":[{"name":"IEETA \/ DETI, LASI, University of Aveiro, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0729-2264","authenticated-orcid":false,"given":"Jo\u00e3o Rafael","family":"Almeida","sequence":"additional","affiliation":[{"name":"IEETA \/ DETI, LASI, University of Aveiro, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6672-6176","authenticated-orcid":false,"given":"Jos\u00e9 Lu\u00eds","family":"Oliveira","sequence":"additional","affiliation":[{"name":"IEETA \/ DETI, LASI, University of Aveiro, Portugal"}]}],"member":"7437","container-title":["Studies in Health Technology and Informatics","Good Evaluation - Better Digital Health"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/SHTI251525","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,3]],"date-time":"2025-10-03T09:57:48Z","timestamp":1759485468000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/SHTI251525"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,2]]},"ISBN":["9781643686295"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/shti251525","relation":{},"ISSN":["0926-9630","1879-8365"],"issn-type":[{"value":"0926-9630","type":"print"},{"value":"1879-8365","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,10,2]]}}}