{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,5]],"date-time":"2026-03-05T07:31:59Z","timestamp":1772695919761,"version":"3.50.1"},"publisher-location":"Cham","reference-count":24,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031282409","type":"print"},{"value":"9783031282416","type":"electronic"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"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":[[2023]]},"DOI":"10.1007\/978-3-031-28241-6_36","type":"book-chapter","created":{"date-parts":[[2023,3,16]],"date-time":"2023-03-16T01:02:20Z","timestamp":1678928540000},"page":"349-356","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Trends and\u00a0Overview: The Potential of\u00a0Conversational Agents in\u00a0Digital Health"],"prefix":"10.1007","author":[{"given":"Tulika","family":"Saha","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Abhishek","family":"Tiwari","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sriparna","family":"Saha","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,3,16]]},"reference":[{"key":"36_CR1","doi-asserted-by":"publisher","first-page":"463","DOI":"10.1162\/tacl_a_00111","volume":"4","author":"T Althoff","year":"2016","unstructured":"Althoff, T., Clark, K., Leskovec, J.: Large-scale analysis of counseling conversations: an application of natural language processing to mental health. Trans. Assoc. Comput. Linguist. 4, 463\u2013476 (2016)","journal-title":"Trans. Assoc. Comput. Linguist."},{"key":"36_CR2","doi-asserted-by":"crossref","unstructured":"Chen, X., et al.: Bridging the gap between prior and posterior knowledge selection for knowledge-grounded dialogue generation. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 3426\u20133437 (2020)","DOI":"10.18653\/v1\/2020.emnlp-main.275"},{"key":"36_CR3","unstructured":"DeVault, D., et al.: Simsensei kiosk: a virtual human interviewer for healthcare decision support. In: Proceedings of the 2014 International Conference on Autonomous Agents and Multi-agent Systems, pp. 1061\u20131068 (2014)"},{"key":"36_CR4","doi-asserted-by":"crossref","unstructured":"Enarvi, S., et al.: Generating medical reports from patient-doctor conversations using sequence-to-sequence models. In: Proceedings of the First Workshop on Natural Language Processing for Medical Conversations, pp. 22\u201330 (2020)","DOI":"10.18653\/v1\/2020.nlpmc-1.4"},{"issue":"13","key":"36_CR5","doi-asserted-by":"publisher","first-page":"10309","DOI":"10.1007\/s00521-021-06208-y","volume":"34","author":"S Ji","year":"2022","unstructured":"Ji, S., Li, X., Huang, Z., Cambria, E.: Suicidal ideation and mental disorder detection with attentive relation networks. Neural Comput. Appl. 34(13), 10309\u201310319 (2022)","journal-title":"Neural Comput. Appl."},{"key":"36_CR6","doi-asserted-by":"crossref","unstructured":"Kao, H.C., Tang, K.F., Chang, E.: Context-aware symptom checking for disease diagnosis using hierarchical reinforcement learning. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018)","DOI":"10.1609\/aaai.v32i1.11902"},{"key":"36_CR7","doi-asserted-by":"crossref","unstructured":"Li, D., Ren, Z., Ren, P., Chen, Z., Fan, M., Ma, J., de Rijke, M.: Semi-supervised variational reasoning for medical dialogue generation. In: Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 544\u2013554 (2021)","DOI":"10.1145\/3404835.3462921"},{"key":"36_CR8","unstructured":"Liao, K., et al.: Task-oriented dialogue system for automatic disease diagnosis via hierarchical reinforcement learning. arXiv preprint arXiv:2004.14254 (2020)"},{"key":"36_CR9","doi-asserted-by":"crossref","unstructured":"Lin, S., et al.: Graph-evolving meta-learning for low-resource medical dialogue generation. In: Proceedings of the 35th AAAI Conference on Artificial Intelligence, pp. 13362\u201313370. AAAI Press (2021)","DOI":"10.1609\/aaai.v35i15.17577"},{"key":"36_CR10","doi-asserted-by":"publisher","first-page":"260","DOI":"10.1016\/j.neucom.2021.02.021","volume":"442","author":"W Liu","year":"2021","unstructured":"Liu, W., Tang, J., Liang, X., Cai, Q.: Heterogeneous graph reasoning for knowledge-grounded medical dialogue system. Neurocomputing 442, 260\u2013268 (2021)","journal-title":"Neurocomputing"},{"key":"36_CR11","doi-asserted-by":"crossref","unstructured":"Lokala, U., et al.: A computational approach to understand mental health from reddit: knowledge-aware multitask learning framework. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 16, pp. 640\u2013650 (2022)","DOI":"10.1609\/icwsm.v16i1.19322"},{"key":"36_CR12","first-page":"487","volume":"2020","author":"BG Patra","year":"2020","unstructured":"Patra, B.G., Kar, R., Roberts, K., Wu, H.: Mental health severity detection from psychological forum data using domain-specific unlabelled data. AMIA Summits Transl. Sci. Proc. 2020, 487 (2020)","journal-title":"AMIA Summits Transl. Sci. Proc."},{"issue":"5","key":"36_CR13","doi-asserted-by":"publisher","first-page":"45","DOI":"10.1109\/MIS.2019.2925204","volume":"34","author":"SA Qureshi","year":"2019","unstructured":"Qureshi, S.A., Saha, S., Hasanuzzaman, M., Dias, G.: Multitask representation learning for multimodal estimation of depression level. IEEE Intell. Syst. 34(5), 45\u201352 (2019)","journal-title":"IEEE Intell. Syst."},{"key":"36_CR14","doi-asserted-by":"crossref","unstructured":"Rasmussen, K., et al.: Offline elearning for undergraduates in health professions: a systematic review of the impact on knowledge, skills, attitudes and satisfaction. J. Global Health 4(1) (2014)","DOI":"10.7189\/jogh.04.010405"},{"key":"36_CR15","doi-asserted-by":"crossref","unstructured":"Saha, T., Gakhreja, V., Das, A.S., Chakraborty, S., Saha, S.: Towards motivational and empathetic response generation in online mental health support. In: Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 2650\u20132656 (2022)","DOI":"10.1145\/3477495.3531912"},{"key":"36_CR16","doi-asserted-by":"crossref","unstructured":"Saha, T., Reddy, S., Das, A., Saha, S., Bhattacharyya, P.: A shoulder to cry on: towards a motivational virtual assistant for assuaging mental agony. In: Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 2436\u20132449 (2022)","DOI":"10.18653\/v1\/2022.naacl-main.174"},{"key":"36_CR17","doi-asserted-by":"crossref","unstructured":"Saha, T., Reddy, S.M., Saha, S., Bhattacharyya, P.: Mental health disorder identification from motivational conversations. IEEE Trans. Comput. Soc. Syst. (2022)","DOI":"10.1109\/TCSS.2022.3143763"},{"key":"36_CR18","doi-asserted-by":"publisher","unstructured":"Sharma, A., Lin, I.W., Miner, A.S., Atkins, D.C., Althoff, T.: Towards facilitating empathic conversations in online mental health support: a reinforcement learning approach. In: Leskovec, J., Grobelnik, M., Najork, M., Tang, J., Zia, L. (eds.) WWW \u201921: The Web Conference 2021, Virtual Event\/Ljubljana, Slovenia, April 19-23, 2021, pp. 194\u2013205. ACM\/IW3C2 (2021). https:\/\/doi.org\/10.1145\/3442381.3450097, https:\/\/doi.org\/10.1145\/3442381.3450097","DOI":"10.1145\/3442381.3450097"},{"key":"36_CR19","doi-asserted-by":"publisher","unstructured":"Sharma, A., Miner, A.S., Atkins, D.C., Althoff, T.: A computational approach to understanding empathy expressed in text-based mental health support. In: Webber, B., Cohn, T., He, Y., Liu, Y. (eds.) Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing, EMNLP 2020, Online, 16\u201320 November, 2020, pp. 5263\u20135276. Association for Computational Linguistics (2020). https:\/\/doi.org\/10.18653\/v1\/2020.emnlp-main.425, https:\/\/doi.org\/10.18653\/v1\/2020.emnlp-main.425","DOI":"10.18653\/v1\/2020.emnlp-main.425"},{"key":"36_CR20","doi-asserted-by":"crossref","unstructured":"Shi, X., Hu, H., Che, W., Sun, Z., Liu, T., Huang, J.: Understanding medical conversations with scattered keyword attention and weak supervision from responses. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 8838\u20138845 (2020)","DOI":"10.1609\/aaai.v34i05.6412"},{"key":"36_CR21","doi-asserted-by":"crossref","unstructured":"Tiwari, A., Manthena, M., Saha, S., Bhattacharyya, P., Dhar, M., Tiwari, S.: Dr. can see: towards a multi-modal disease diagnosis virtual assistant. In: Proceedings of the 31st ACM International Conference on Information & Knowledge Management, pp. 1935\u20131944 (2022)","DOI":"10.1145\/3511808.3557296"},{"key":"36_CR22","doi-asserted-by":"crossref","unstructured":"Wei, Z., et al.: Task-oriented dialogue system for automatic diagnosis. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 201\u2013207 (2018)","DOI":"10.18653\/v1\/P18-2033"},{"issue":"6","key":"36_CR23","doi-asserted-by":"publisher","first-page":"957","DOI":"10.1093\/jamia\/ocaa067","volume":"27","author":"J Wosik","year":"2020","unstructured":"Wosik, J., et al.: Telehealth transformation: Covid-19 and the rise of virtual care. J. Am. Med. Inform. Assoc. 27(6), 957\u2013962 (2020)","journal-title":"J. Am. Med. Inform. Assoc."},{"key":"36_CR24","doi-asserted-by":"crossref","unstructured":"Xu, L., Zhou, Q., Gong, K., Liang, X., Tang, J., Lin, L.: End-to-end knowledge-routed relational dialogue system for automatic diagnosis. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, pp. 7346\u20137353 (2019)","DOI":"10.1609\/aaai.v33i01.33017346"}],"container-title":["Lecture Notes in Computer Science","Advances in Information Retrieval"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-28241-6_36","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,5]],"date-time":"2024-03-05T13:04:57Z","timestamp":1709643897000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-28241-6_36"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031282409","9783031282416"],"references-count":24,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-28241-6_36","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"16 March 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ECIR","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"European Conference on Information Retrieval","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Dublin","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Ireland","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2 April 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"6 April 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"45","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ecir2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/ecir2023.org\/index.html?v=1.0","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"489","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"77","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"83","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"16% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}