{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T14:26:49Z","timestamp":1742912809959,"version":"3.40.3"},"publisher-location":"Cham","reference-count":22,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783031059803"},{"type":"electronic","value":"9783031059810"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-3-031-05981-0_17","type":"book-chapter","created":{"date-parts":[[2022,5,9]],"date-time":"2022-05-09T12:02:50Z","timestamp":1652097770000},"page":"212-222","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Mental Health Treatments Using an\u00a0Explainable Adaptive Clustering Model"],"prefix":"10.1007","author":[{"given":"Usman","family":"Ahmed","sequence":"first","affiliation":[]},{"given":"Jerry Chun-Wei","family":"Lin","sequence":"additional","affiliation":[]},{"given":"Gautam","family":"Srivastava","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,5,10]]},"reference":[{"key":"17_CR1","doi-asserted-by":"publisher","first-page":"471","DOI":"10.3389\/fpsyg.2021.642347","volume":"12","author":"U Ahmed","year":"2021","unstructured":"Ahmed, U., Mukhiya, S.K., Srivastava, G., Lamo, Y., Lin, J.C.W.: Attention-based deep entropy active learning using lexical algorithm for mental health treatment. Front. Psychol. 12, 471 (2021)","journal-title":"Front. Psychol."},{"issue":"4","key":"17_CR2","doi-asserted-by":"publisher","first-page":"505","DOI":"10.1017\/S0142716400004057","volume":"21","author":"WG Charles","year":"2000","unstructured":"Charles, W.G.: Contextual correlates of meaning. Appl. Psycholinguist. 21(4), 505\u2013524 (2000)","journal-title":"Appl. Psycholinguist."},{"key":"17_CR3","doi-asserted-by":"crossref","unstructured":"Chen, E., Lerman, K., Ferrara, E.: Tracking social media discourse about the COVID-19 pandemic: development of a public coronavirus twitter data set. JMIR Public Health Surveill. 6(2), e19273 (2020)","DOI":"10.2196\/19273"},{"key":"17_CR4","unstructured":"Chen, X., Wu, S.Z., Hong, M.: Understanding gradient clipping in private SGD: a geometric perspective. Adv. Neural. Inf. Process. Syst. 33, 13773\u201313782 (2020)"},{"key":"17_CR5","doi-asserted-by":"crossref","unstructured":"Cho, K., et al.: Learning phrase representations using RNN encoder-decoder for statistical machine translation. In: Moschitti, A., Pang, B., Daelemans, W. (eds.) The Conference on Empirical Methods in Natural Language Processing, pp. 1724\u20131734 (2014)","DOI":"10.3115\/v1\/D14-1179"},{"key":"17_CR6","doi-asserted-by":"crossref","unstructured":"Dinakar, K., Weinstein, E., Lieberman, H., Selman, R.: Stacked generalization learning to analyze teenage distress. In: Proceedings of the International AAAI Conference on Web and Social Media, pp. 1\u20138 (2014)","DOI":"10.1609\/icwsm.v8i1.14527"},{"key":"17_CR7","doi-asserted-by":"crossref","unstructured":"Ebadi, A., Xi, P., Tremblay, S., Spencer, B., Pall, R., Wong, A.: Understanding the temporal evolution of COVID-19 research through machine learning and natural language processing. CoRR abs\/2007.11604 (2020)","DOI":"10.1007\/s11192-020-03744-7"},{"key":"17_CR8","doi-asserted-by":"publisher","unstructured":"Henry, S., Yetisgen, M., Uzuner, O.: Natural language processing in mental health research and practice. In: Tenenbaum, J.D., Ranallo, P.A. (eds.) Mental Health Informatics, pp. 317\u2013353. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-70558-9_13","DOI":"10.1007\/978-3-030-70558-9_13"},{"key":"17_CR9","doi-asserted-by":"crossref","unstructured":"James, S.L., et al.: Global, regional, and national incidence, prevalence, and years lived with disability for 354 diseases and injuries for 195 countries and territories, 1990\u20132017: a systematic analysis for the global burden of disease study 2017. Lancet 392(10159), 1789\u20131858 (2018)","DOI":"10.1016\/S0140-6736(18)32279-7"},{"key":"17_CR10","unstructured":"Kulkarni, A., Hengle, A., Kulkarni, P., Marathe, M.: Cluster analysis of online mental health discourse using topic-infused deep contextualized representations. In: Proceedings of the 12th International Workshop on Health Text Mining and Information Analysis, pp. 83\u201393 (2021)"},{"key":"17_CR11","doi-asserted-by":"crossref","unstructured":"Le Glaz, A., Berrouiguet, S., et al.: Machine learning and natural language processing in mental health: systematic review. J. Med. Internet Res. 23(5), e15708 (2021)","DOI":"10.2196\/15708"},{"key":"17_CR12","doi-asserted-by":"crossref","unstructured":"Lin, H., et al.: User-level psychological stress detection from social media using deep neural network. In: Proceedings of the 22nd ACM International Conference on Multimedia, pp. 507\u2013516 (2014)","DOI":"10.1145\/2647868.2654945"},{"key":"17_CR13","unstructured":"Lu, J., Yang, J., Batra, D., Parikh, D.: Hierarchical question-image co-attention for visual question answering. In: Lee, D.D., Sugiyama, M., von Luxburg, U., Guyon, I., Garnett, R. (eds.) Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems, pp. 289\u2013297 (2016)"},{"key":"17_CR14","doi-asserted-by":"crossref","unstructured":"Luong, T., Pham, H., Manning, C.D.: Effective approaches to attention-based neural machine translation. In: M\u00e0rquez, L., Callison-Burch, C., Su, J., Pighin, D., Marton, Y. (eds.) The Conference on Empirical Methods in Natural Language Processing, pp. 1412\u20131421 (2015)","DOI":"10.18653\/v1\/D15-1166"},{"issue":"1","key":"17_CR15","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s10579-018-9423-1","volume":"54","author":"DE Losada","year":"2018","unstructured":"Losada, D.E., Gamallo, P.: Evaluating and improving lexical resources for detecting signs of depression in text. Lang. Resour. Eval. 54(1), 1\u201324 (2018)","journal-title":"Lang. Resour. Eval."},{"key":"17_CR16","doi-asserted-by":"crossref","unstructured":"Mazza, M.G., et al.: Anxiety and depression in COVID-19 survivors: role of inflammatory and clinical predictors. Brain Behav. Immun. 89, 594\u2013600 (2020)","DOI":"10.1016\/j.bbi.2020.07.037"},{"key":"17_CR17","doi-asserted-by":"crossref","unstructured":"McDonnell, M., Owen, J.E., Bantum, E.O.: Identification of emotional expression with cancer survivors: validation of linguistic inquiry and word count. JMIR Form. Res. 4(10), e18246 (2020)","DOI":"10.2196\/18246"},{"key":"17_CR18","doi-asserted-by":"crossref","unstructured":"Mukhiya, S.K., Ahmed, U., Rabbi, F., Pun, K.I., Lamo, Y.: Adaptation of IDPT system based on patient-authored text data using NLP. In: International Symposium on Computer-Based Medical Systems (CBMS), pp. 226\u2013232. IEEE (2020)","DOI":"10.1109\/CBMS49503.2020.00050"},{"key":"17_CR19","doi-asserted-by":"crossref","unstructured":"Mukhiya, S.K., Wake, J.D., Inal, Y., Pun, K.I., Lamo, Y.: Adaptive elements in internet-delivered psychological treatment systems: systematic review. J. Med. Internet Res. 22(11), e21066 (2020)","DOI":"10.2196\/21066"},{"key":"17_CR20","doi-asserted-by":"crossref","unstructured":"Neuraz, A., et al.: Natural language processing for rapid response to emergent diseases: case study of calcium channel blockers and hypertension in the covid-19 pandemic. J. Med. Internet Res. 22(8), e20773 (2020)","DOI":"10.2196\/20773"},{"key":"17_CR21","doi-asserted-by":"crossref","unstructured":"Pennington, J., Socher, R., Manning, C.D.: Glove: global vectors for word representation. In: The Conference on Empirical Methods in Natural Language Processing, pp. 1532\u20131543 (2014)","DOI":"10.3115\/v1\/D14-1162"},{"key":"17_CR22","unstructured":"Xu, K., et al.: Show, attend and tell: neural image caption generation with visual attention. In: Bach, F.R., Blei, D.M. (eds.) The International Conference on Machine Learning. JMLR Workshop and Conference Proceedings, vol. 37, pp. 2048\u20132057 (2015)"}],"container-title":["Lecture Notes in Computer Science","Advances in Knowledge Discovery and Data Mining"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-05981-0_17","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,13]],"date-time":"2024-03-13T13:40:01Z","timestamp":1710337201000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-05981-0_17"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031059803","9783031059810"],"references-count":22,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-05981-0_17","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"10 May 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"PAKDD","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Pacific-Asia Conference on Knowledge Discovery and Data Mining","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Chengdu","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"16 May 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"19 May 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"pakdd2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/pakdd.net\/index.html","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":"CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"558","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":"121","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":"0","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":"22% - 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":"3.75","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":"6.45","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":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}