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However, the black-box nature of deep learning models limits their explainability, a critical factor in clinical applications where clinicians require transparent, reliable decision-making tools to support clinical interventions. In non-invasive monitoring, sensor data and clinical attributes serve as input features for predicting patient health outcomes. Understanding how these features contribute to model predictions is crucial for informed clinical decisions in a mental health context. This study proposes a novel quantitative explainability framework (QEF) that provides both post-hoc and intrinsic explainability for regression and classification tasks within deep learning models. The framework combines Shapley values to elucidate feature contributions and attention mechanisms to enhance interpretability. Two deep learning models\u2014artificial neural networks (ANN) and attention-based bidirectional long short-term memory (BiLSTM)\u2014were applied to predict heart rate and classify physical activities using sensor data, achieving state-of-the-art performance. Attention weights and Shapley values were computed for each input feature to provide global and local explanations, offering insights into the models\u2019 behavior and feature importance. The QEF framework was evaluated using the PPG-DaLiA dataset for heart rate prediction and the MHEALTH dataset for physical activity classification. To address the computational complexity of Shapley value calculations, a Monte Carlo approximation method was implemented, reducing time and resource demands. This study introduces the QEF framework as a practical solution to balance model performance with explainability, providing clinicians with interpretable insights from deep learning models in the field of psychiatry and mental health.<\/jats:p>","DOI":"10.1007\/s44230-025-00104-7","type":"journal-article","created":{"date-parts":[[2025,6,21]],"date-time":"2025-06-21T05:21:32Z","timestamp":1750483292000},"page":"209-229","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Towards Transparent Deep Learning in Medicine: Feature Contribution and Attention Mechanism-Based Explainability"],"prefix":"10.1007","volume":"5","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9730-665X","authenticated-orcid":false,"given":"Thanveer","family":"Shaik","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaohui","family":"Tao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haoran","family":"Xie","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lin","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Niall","family":"Higgins","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Juan D.","family":"Vel\u00e1squez","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,6,21]]},"reference":[{"issue":"1","key":"104_CR1","doi-asserted-by":"publisher","first-page":"57","DOI":"10.1007\/s12652-017-0598-x","volume":"10","author":"LP Malasinghe","year":"2019","unstructured":"Malasinghe LP, Ramzan N, Dahal K. 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Comput Biol Med. 2021;136: 104672.","journal-title":"Comput Biol Med"}],"container-title":["Human-Centric Intelligent Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s44230-025-00104-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s44230-025-00104-7\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s44230-025-00104-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,6]],"date-time":"2025-09-06T21:23:33Z","timestamp":1757193813000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s44230-025-00104-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,6,21]]},"references-count":52,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2025,6]]}},"alternative-id":["104"],"URL":"https:\/\/doi.org\/10.1007\/s44230-025-00104-7","relation":{},"ISSN":["2667-1336"],"issn-type":[{"value":"2667-1336","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,6,21]]},"assertion":[{"value":"17 January 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 May 2025","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"28 May 2025","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"21 June 2025","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Author Xiaohui Tao is a member of the Editorial Board of the journal Human-Centric Intelligent Systems. The paper was handled by another Editor and has undergone a rigorous peer review process. Author Xiaohui Tao was not involved in the journal\u2019s peer review of, or decisions related to, this manuscript. Author Lin Li is one of the Executive Editors of the Journal Human-Centric Intelligent Systems. The paper was handled by another Editor and has undergone a rigorous peer review process. Author Lin Li was not involved in the journal\u2019s peer review of, or decisions related to, this manuscript. Author Haoran Xie is a member of the Editorial Board of the journal Human-Centric Intelligent Systems. The paper was handled by another Editor and has undergone a rigorous peer review process. Author Haoran Xie was not involved in the journal\u2019s peer review of, or decisions related to, this manuscript.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"Not applicable. This study does not involve human participants or animal studies requiring ethical approval.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"All authors have provided their consent for the publication of this manuscript.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"Not applicable. This study does not rely on specialized materials requiring conflict of interest.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Materials availability"}},{"value":"Not applicable.","order":6,"name":"Ethics","group":{"name":"EthicsHeading","label":"Clinical Trial Registration (if applicable)"}}]}}