{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,11]],"date-time":"2026-05-11T12:09:33Z","timestamp":1778501373049,"version":"3.51.4"},"publisher-location":"Singapore","reference-count":37,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819203680","type":"print"},{"value":"9789819203697","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\/978-981-92-0369-7_9","type":"book-chapter","created":{"date-parts":[[2026,5,11]],"date-time":"2026-05-11T11:19:27Z","timestamp":1778498367000},"page":"133-148","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["From Personal to\u00a0Clinical: Personalisation and\u00a0Depersonalisation for\u00a0Explainable Depression Detection"],"prefix":"10.1007","author":[{"given":"Ziyang","family":"Gao","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Linhai","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yulan","family":"He","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Deyu","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2026,5,12]]},"reference":[{"key":"9_CR1","unstructured":"An, J., Park, D., Kim, H.: Depressllm: interpretable domain-adapted language model for depression detection from real-world narratives. arXiv preprint arXiv:2508.08591 (2025)"},{"key":"9_CR2","doi-asserted-by":"crossref","unstructured":"Burdisso, S., Villatoro-Tello, E., Madikeri, S., Motlicek, P.: Node-weighted graph convolutional network for depression detection in transcribed clinical interviews. arXiv preprint arXiv:2307.00920 (2023)","DOI":"10.21437\/Interspeech.2023-1923"},{"key":"9_CR3","doi-asserted-by":"publisher","first-page":"130","DOI":"10.1016\/j.patrec.2020.07.001","volume":"138","author":"SG Burdisso","year":"2020","unstructured":"Burdisso, S.G., Errecalde, M., Montes-y G\u00f3mez, M.: $$\\tau $$-ss3: a text classifier with dynamic n-grams for early risk detection over text streams. Pattern Recogn. Lett. 138, 130\u2013137 (2020)","journal-title":"Pattern Recogn. Lett."},{"key":"9_CR4","unstructured":"Chen, K., et al.: Mediator-guided multi-agent collaboration among open-source models for medical decision-making. arXiv preprint arXiv:2508.05996 (2025)"},{"key":"9_CR5","doi-asserted-by":"publisher","unstructured":"Chen, Z., Deng, J., Zhou, J., Wu, J., Qian, T., Huang, M.: Depression detection in clinical interviews with LLM-empowered structural element graph. In: Duh, K., Gomez, H., Bethard, S. (eds.) Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, vol. 1: Long Papers, pp. 8181\u20138194. Association for Computational Linguistics, Mexico City (2024). https:\/\/doi.org\/10.18653\/v1\/2024.naacl-long.452. https:\/\/aclanthology.org\/2024.naacl-long.452\/","DOI":"10.18653\/v1\/2024.naacl-long.452"},{"key":"9_CR6","doi-asserted-by":"crossref","unstructured":"Chen, Z., Deng, J., Zhou, J., Wu, J., Qian, T., Huang, M.: Depression detection in clinical interviews with LLM-empowered structural element graph. In: Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, vol. 1: Long Papers, pp. 8181\u20138194 (2024)","DOI":"10.18653\/v1\/2024.naacl-long.452"},{"key":"9_CR7","doi-asserted-by":"crossref","unstructured":"Fang, M., Peng, S., Liang, Y., Hung, C.C., Liu, S.: A multimodal fusion model with multi-level attention mechanism for depression detection. Biomed. Signal Process. Control 82, 104561 (2023)","DOI":"10.1016\/j.bspc.2022.104561"},{"issue":"2","key":"9_CR8","doi-asserted-by":"publisher","first-page":"335","DOI":"10.1016\/S0896-6273(00)00112-4","volume":"28","author":"M Fava","year":"2000","unstructured":"Fava, M., Kendler, K.S.: Major depressive disorder. Neuron 28(2), 335\u2013341 (2000)","journal-title":"Neuron"},{"key":"9_CR9","doi-asserted-by":"crossref","unstructured":"Goldman, L.S., Nielsen, N.H., Champion, H.C., Council\u00a0on Scientific\u00a0Affairs, A.M.A.: Awareness, diagnosis, and treatment of depression. J. General Internal Med. 14(9), 569\u2013580 (1999)","DOI":"10.1046\/j.1525-1497.1999.03478.x"},{"key":"9_CR10","doi-asserted-by":"crossref","unstructured":"Gratch, J., et\u00a0al.: The distress analysis interview corpus of human and computer interviews. In: LREC, vol.\u00a014, pp. 3123\u20133128. Reykjavik (2014)","DOI":"10.63317\/3o7bccg9xequ"},{"key":"9_CR11","unstructured":"Guu, K., Lee, K., Tung, Z., Pasupat, P., Chang, M.: Retrieval augmented language model pre-training. In: International Conference on Machine Learning, pp. 3929\u20133938. PMLR (2020)"},{"key":"9_CR12","unstructured":"Han, S., et al.: Mdocagent: a multi-modal multi-agent framework for document understanding. arXiv preprint arXiv:2503.13964 (2025)"},{"key":"9_CR13","doi-asserted-by":"crossref","unstructured":"Jung, J., Kang, C., Yoon, J., Kim, S., Han, J.: Hique: hierarchical question embedding network for multimodal depression detection. In: Proceedings of the 33rd ACM International Conference on Information and Knowledge Management, pp. 1049\u20131059 (2024)","DOI":"10.1145\/3627673.3679797"},{"key":"9_CR14","doi-asserted-by":"publisher","DOI":"10.2196\/59439","volume":"26","author":"Y Ke","year":"2024","unstructured":"Ke, Y., et al.: Mitigating cognitive biases in clinical decision-making through multi-agent conversations using large language models: simulation study. J. Med. Internet Res. 26, e59439 (2024)","journal-title":"J. Med. Internet Res."},{"issue":"9","key":"9_CR15","doi-asserted-by":"publisher","first-page":"606","DOI":"10.1046\/j.1525-1497.2001.016009606.x","volume":"16","author":"K Kroenke","year":"2001","unstructured":"Kroenke, K., Spitzer, R.L., Williams, J.B.: The phq-9: validity of a brief depression severity measure. J. Gen. Int. Med. 16(9), 606\u2013613 (2001)","journal-title":"J. Gen. Int. Med."},{"issue":"1\u20133","key":"9_CR16","doi-asserted-by":"publisher","first-page":"163","DOI":"10.1016\/j.jad.2008.06.026","volume":"114","author":"K Kroenke","year":"2009","unstructured":"Kroenke, K., Strine, T.W., Spitzer, R.L., Williams, J.B., Berry, J.T., Mokdad, A.H.: The phq-8 as a measure of current depression in the general population. J. Affect. Disord. 114(1\u20133), 163\u2013173 (2009)","journal-title":"J. Affect. Disord."},{"key":"9_CR17","unstructured":"Lewis, P., et al.: Retrieval-augmented generation for knowledge-intensive NLP tasks. Adv. Neural. Inf. Process. Syst. 33, 9459\u20139474 (2020)"},{"key":"9_CR18","unstructured":"Li, A., Xie, Y., Li, S., Tsung, F., Ding, B., Li, Y.: Agent-oriented planning in multi-agent systems. arXiv preprint arXiv:2410.02189 (2024)"},{"key":"9_CR19","doi-asserted-by":"crossref","unstructured":"Lorenzoni, G., Velmovitsky, P.E., Alencar, P., Cowan, D.: Gpt-4 on clinic depression assessment: an LLM-based pilot study. In: 2024 IEEE International Conference on Big Data (BigData), pp. 5043\u20135049. IEEE (2024)","DOI":"10.1109\/BigData62323.2024.10825184"},{"key":"9_CR20","doi-asserted-by":"crossref","unstructured":"Luo, Y., Zhang, M., Zhao, T.: Speecht-rag: reliable depression detection in LLMs with retrieval-augmented generation using speech timing information. In: Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (ACL) (2025)","DOI":"10.18653\/v1\/2025.findings-acl.521"},{"issue":"9","key":"9_CR21","doi-asserted-by":"publisher","first-page":"958","DOI":"10.3390\/jpm14090958","volume":"14","author":"MA Mansoor","year":"2024","unstructured":"Mansoor, M.A., Ansari, K.H.: Early detection of mental health crises through artificial-intelligence-powered social media analysis: a prospective observational study. J. Personalized Med. 14(9), 958 (2024)","journal-title":"J. Personalized Med."},{"key":"9_CR22","doi-asserted-by":"crossref","unstructured":"Muhammad, D., Ahmed, I., Ahmad, M.O., Bendechache, M.: Randomized explainable machine learning models for efficient medical diagnosis. IEEE J. Biomed. Health Inf. (2024)","DOI":"10.1109\/JBHI.2024.3491593"},{"key":"9_CR23","doi-asserted-by":"publisher","unstructured":"Niu, M., Chen, K., Chen, Q., Yang, L.: HCAG: a hierarchical context-aware graph attention model for depression detection. In: ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4235\u20134239 (2021). https:\/\/doi.org\/10.1109\/ICASSP39728.2021.9413486","DOI":"10.1109\/ICASSP39728.2021.9413486"},{"key":"9_CR24","doi-asserted-by":"crossref","unstructured":"Ringeval, F., et\u00a0al.: Avec 2019 workshop and challenge: state-of-mind, detecting depression with ai, and cross-cultural affect recognition. In: Proceedings of the 9th International on Audio\/visual Emotion Challenge and Workshop, pp. 3\u201312 (2019)","DOI":"10.1145\/3347320.3357688"},{"key":"9_CR25","doi-asserted-by":"crossref","unstructured":"Salas-Z\u00e1rate, R., Alor-Hern\u00e1ndez, G., Salas-Z\u00e1rate, M.d.P., Paredes-Valverde, M.A., Bustos-L\u00f3pez, M., S\u00e1nchez-Cervantes, J.L.: Detecting depression signs on social media: a systematic literature review. In: Healthcare, vol.\u00a010, p.\u00a0291. MDPI (2022)","DOI":"10.3390\/healthcare10020291"},{"key":"9_CR26","doi-asserted-by":"publisher","unstructured":"Shen, Y., Yang, H., Lin, L.: Automatic depression detection: an emotional audio-textual corpus and a GRU\/BiLSTM-based model. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 6247\u20136251 (2022). https:\/\/doi.org\/10.1109\/ICASSP43922.2022.9746569","DOI":"10.1109\/ICASSP43922.2022.9746569"},{"key":"9_CR27","unstructured":"Shi, F., et al.: Large language models can be easily distracted by irrelevant context. In: International Conference on Machine Learning, pp. 31210\u201331227. PMLR (2023)"},{"key":"9_CR28","unstructured":"Sun, Z., Zhou, X., Li, G., Yu, X., Feng, J., Zhang, Y.: R-bot: an LLM-based query rewrite system. arXiv preprint arXiv:2412.01661 (2024)"},{"key":"9_CR29","doi-asserted-by":"crossref","unstructured":"Teng, S., et al.: Enhancing depression detection with chain-of-thought prompting: From emotion to reasoning using large language models. arXiv preprint arXiv:2502.05879 (2025)","DOI":"10.1109\/EMBC58623.2025.11253093"},{"key":"9_CR30","doi-asserted-by":"crossref","unstructured":"Wang, Y., Inkpen, D., Gamaarachchige, P.K.: Explainable depression detection using large language models on social media data. In: Proceedings of the 9th Workshop on Computational Linguistics and Clinical Psychology (CLPsych 2024), pp. 108\u2013126 (2024)","DOI":"10.18653\/v1\/2024.clpsych-1.8"},{"key":"9_CR31","unstructured":"World Health Organization: Depressive disorder (depression) [fact sheet] (2025). https:\/\/www.who.int\/news-room\/fact-sheets\/detail\/depression. Accessed 09 Oct 2025"},{"key":"9_CR32","unstructured":"Yuan, S., Song, K., Chen, J., Tan, X., Li, D., Yang, D.: Evoagent: towards automatic multi-agent generation via evolutionary algorithms. arXiv preprint arXiv:2406.14228 (2024)"},{"key":"9_CR33","doi-asserted-by":"publisher","unstructured":"Zhang, L., Gao, Z., Zhou, D., He, Y.: Explainable depression detection in clinical interviews with personalized retrieval-augmented generation. In: Che, W., Nabende, J., Shutova, E., Pilehvar, M.T. (eds.) Findings of the Association for Computational Linguistics: ACL 2025, pp. 9927\u20139944. Association for Computational Linguistics, Vienna (2025). https:\/\/doi.org\/10.18653\/v1\/2025.findings-acl.517. https:\/\/aclanthology.org\/2025.findings-acl.517\/","DOI":"10.18653\/v1\/2025.findings-acl.517"},{"key":"9_CR34","doi-asserted-by":"crossref","unstructured":"Zhang, T., Li, K., Luo, H., Wu, X., Glass, J., Meng, H.: Adaptive query rewriting: aligning rewriters through marginal probability of conversational answers. arXiv preprint arXiv:2406.10991 (2024)","DOI":"10.18653\/v1\/2024.emnlp-main.746"},{"key":"9_CR35","doi-asserted-by":"crossref","unstructured":"Zhang, X., Cui, W., Wang, J., Li, Y.: Chat, summary and diagnosis: A LLM-enhanced conversational agent for interactive depression detection. In: 2024 4th International Conference on Industrial Automation, Robotics and Control Engineering (IARCE), pp. 343\u2013348. IEEE (2024)","DOI":"10.1109\/IARCE64300.2024.00070"},{"key":"9_CR36","doi-asserted-by":"publisher","unstructured":"Zhao, X., Lyu, Y., Wang, D., Tang, B.: Predicting depression in screening interviews from interactive multi-theme collaboration. In: Che, W., Nabende, J., Shutova, E., Pilehvar, M.T. (eds.) Findings of the Association for Computational Linguistics: ACL 2025, pp. 23025\u201323035. Association for Computational Linguistics, Vienna (2025). https:\/\/doi.org\/10.18653\/v1\/2025.findings-acl.1181. https:\/\/aclanthology.org\/2025.findings-acl.1181\/","DOI":"10.18653\/v1\/2025.findings-acl.1181"},{"key":"9_CR37","doi-asserted-by":"crossref","unstructured":"Zheng, H., Shi, Z., Yi, P.: Medcoact: confidence-aware multi-agent collaboration for complete clinical decision. arXiv preprint arXiv:2510.10461 (2025)","DOI":"10.1109\/BIBM66473.2025.11356820"}],"container-title":["Lecture Notes in Computer Science","Database Systems for Advanced Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-92-0369-7_9","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,11]],"date-time":"2026-05-11T11:19:42Z","timestamp":1778498382000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-92-0369-7_9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"ISBN":["9789819203680","9789819203697"],"references-count":37,"URL":"https:\/\/doi.org\/10.1007\/978-981-92-0369-7_9","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026]]},"assertion":[{"value":"12 May 2026","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"DASFAA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Database Systems for Advanced Applications","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Jeju","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Korea (Republic of)","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2026","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 April 2026","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"30 April 2026","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"31","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"dasfaa2026","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/dasfaa2026.github.io\/index.html","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}