{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,9]],"date-time":"2026-04-09T00:20:53Z","timestamp":1775694053802,"version":"3.50.1"},"publisher-location":"Cham","reference-count":15,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032169945","type":"print"},{"value":"9783032169952","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026]]},"DOI":"10.1007\/978-3-032-16995-2_20","type":"book-chapter","created":{"date-parts":[[2026,4,8]],"date-time":"2026-04-08T23:41:50Z","timestamp":1775691710000},"page":"216-229","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Explainable AI for\u00a0Clinical Data from\u00a0EHR Using SHAP and\u00a0LLM-Based Knowledge"],"prefix":"10.1007","author":[{"given":"Prerna","family":"Jamloki","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Christina","family":"Garcia","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Haru","family":"Kaneko","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sozo","family":"Inoue","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2026,4,1]]},"reference":[{"key":"20_CR1","doi-asserted-by":"publisher","unstructured":"Garcia, C., Inoue, S.: Challenges and opportunities of activity recognition in clinical pathways. In: Human Activity and Behavior Analysis. CRC press (2024). https:\/\/doi.org\/10.1201\/9781003371540-8","DOI":"10.1201\/9781003371540-8"},{"key":"20_CR2","unstructured":"Zeng, X.: Enhancing the Interpretability of SHAP Values Using Large Language Models. arXiv preprint arXiv:2409.00079 (2024)"},{"key":"20_CR3","unstructured":"Ahmed, A., et al.: Leveraging large language models to enhance machine learning interpretability and predictive performance: a case study on emergency department returns for mental health patients (2025). https:\/\/www.arxiv.org\/abs\/2502.00025v2"},{"issue":"2","key":"20_CR4","doi-asserted-by":"publisher","first-page":"91","DOI":"10.4103\/jets.jets_73_23","volume":"17","author":"X Lu","year":"2024","unstructured":"Lu, X., Chen, Y., Zhang, G., Zeng, X., Lai, L., Qu, C.: Comparative analysis of machine learning models for prediction of acute liver injury in sepsis patients. J. Emerg. Trauma Shock 17(2), 91\u2013101 (2024). https:\/\/doi.org\/10.4103\/jets.jets_73_23","journal-title":"J. Emerg. Trauma Shock"},{"issue":"4","key":"20_CR5","doi-asserted-by":"publisher","first-page":"354","DOI":"10.15441\/ceem.23.145","volume":"10","author":"Y Okada","year":"2023","unstructured":"Okada, Y., Ning, Y., Ong, M.E.H.: Explainable artificial intelligence in emergency medicine: an overview. Clin. Exp. Emerg. Med. 10(4), 354\u2013362 (2023). https:\/\/doi.org\/10.15441\/ceem.23.145","journal-title":"Clin. Exp. Emerg. Med."},{"issue":"1","key":"20_CR6","doi-asserted-by":"publisher","DOI":"10.1016\/j.mcpdig.2025.100197","volume":"3","author":"EL Williams","year":"2025","unstructured":"Williams, E.L., Huynh, D., Estai, M., Sinha, T., Summerscales, M., Kanagasingam, Y.: Predicting inpatient admissions from emergency department triage using machine learning: a systematic review. Mayo Clin. Proc. Digit. Health 3(1), 100197 (2025). https:\/\/doi.org\/10.1016\/j.mcpdig.2025.100197","journal-title":"Mayo Clin. Proc. Digit. Health"},{"key":"20_CR7","doi-asserted-by":"publisher","unstructured":"Johnson, A.E.W., Pollard, T.J.: MIMIC-III Clinical Database (v1.4). PhysioNet (2016). https:\/\/www.kaggle.com\/datasets\/asjad99\/mimiciii\/data?select=mimic-iii-clinical-database-demo-1.4. https:\/\/doi.org\/10.13026\/C2XW26","DOI":"10.13026\/C2XW26"},{"key":"20_CR8","unstructured":"Chicco, D.: Neuroblastoma EHRs Data \u2013 dataYM2018 Dataset (2018). https:\/\/davidechicco.github.io\/neuroblastoma_EHRs_data\/datasets.html"},{"key":"20_CR9","doi-asserted-by":"crossref","unstructured":"Goldshmidt, R., Horovicz, M.: TokenSHAP: interpreting large language models with monte carlo shapley value estimation. arXiv preprint arXiv:2407.10114 (2024)","DOI":"10.18653\/v1\/2024.nlp4science-1.1"},{"key":"20_CR10","doi-asserted-by":"publisher","DOI":"10.1016\/j.compeleceng.2024.109370","volume":"239","author":"Z Sadeghi","year":"2024","unstructured":"Sadeghi, Z., et al.: A review of explainable artificial intelligence in healthcare. Comput. Methods Programs Biomed. 239, 107798 (2024). https:\/\/doi.org\/10.1016\/j.compeleceng.2024.109370","journal-title":"Comput. Methods Programs Biomed."},{"issue":"3","key":"20_CR11","doi-asserted-by":"publisher","first-page":"277","DOI":"10.3390\/jpm14030277","volume":"14","author":"B Allen","year":"2024","unstructured":"Allen, B.: The promise of explainable AI in digital health for precision medicine: a systematic review. J. Pers. Med. 14(3), 277 (2024). https:\/\/doi.org\/10.3390\/jpm14030277","journal-title":"J. Pers. Med."},{"issue":"2","key":"20_CR12","doi-asserted-by":"publisher","DOI":"10.1002\/widm.70018","volume":"15","author":"AA Noor","year":"2025","unstructured":"Noor, A.A., et al.: Unveiling explainable AI in healthcare: current trends, challenges, and future directions. Wiley Interdisc. Rev. Data Min. Knowl. Disc. 15(2), e70018 (2025). https:\/\/doi.org\/10.1002\/widm.70018","journal-title":"Wiley Interdisc. Rev. Data Min. Knowl. Disc."},{"key":"20_CR13","doi-asserted-by":"publisher","first-page":"110","DOI":"10.1186\/s12911-025-02944-6","volume":"25","author":"R Alkhanbouli","year":"2025","unstructured":"Alkhanbouli, R., Almadhaani, H.M.A., Alhosani, F., Simsekler, M.C.E.: The role of explainable artificial intelligence in disease prediction: a systematic literature review and future research directions. BMC Med. Inf. Decis. Mak. 25, 110 (2025). https:\/\/doi.org\/10.1186\/s12911-025-02944-6","journal-title":"BMC Med. Inf. Decis. Mak."},{"key":"20_CR14","doi-asserted-by":"publisher","unstructured":"Ribeiro, M.T., Singh, S., Guestrin, C.: \u201cWhy Should I Trust You?\u201d Explaining the predictions of any classifier. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), pp. 1135\u20131144 (2016). https:\/\/doi.org\/10.1145\/2939672.2939778","DOI":"10.1145\/2939672.2939778"},{"issue":"4","key":"20_CR15","doi-asserted-by":"publisher","first-page":"105","DOI":"10.1609\/aimag.v35i4.2513","volume":"35","author":"S Amershi","year":"2014","unstructured":"Amershi, S., Cakmak, M., Knox, W.B., Kulesza, T.: Power to the people: the role of humans in interactive machine learning. AI Mag. 35(4), 105\u2013120 (2014). https:\/\/doi.org\/10.1609\/aimag.v35i4.2513","journal-title":"AI Mag."}],"container-title":["Lecture Notes in Networks and Systems","Proceedings of the International Conference on Ubiquitous Computing and Ambient Intelligence (UCAmI 2025), Volume 2"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-16995-2_20","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,8]],"date-time":"2026-04-08T23:41:51Z","timestamp":1775691711000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-16995-2_20"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"ISBN":["9783032169945","9783032169952"],"references-count":15,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-16995-2_20","relation":{},"ISSN":["2367-3370","2367-3389"],"issn-type":[{"value":"2367-3370","type":"print"},{"value":"2367-3389","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026]]},"assertion":[{"value":"1 April 2026","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"UCAmI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Ubiquitous Computing and Ambient Intelligence","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Florence","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Italy","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26 November 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28 November 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ucami2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/ucami.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}