{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T17:35:18Z","timestamp":1742924118037,"version":"3.40.3"},"publisher-location":"Cham","reference-count":38,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031779176"},{"type":"electronic","value":"9783031779183"}],"license":[{"start":{"date-parts":[[2024,11,29]],"date-time":"2024-11-29T00:00:00Z","timestamp":1732838400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,11,29]],"date-time":"2024-11-29T00:00:00Z","timestamp":1732838400000},"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":[[2025]]},"DOI":"10.1007\/978-3-031-77918-3_9","type":"book-chapter","created":{"date-parts":[[2024,11,28]],"date-time":"2024-11-28T10:24:28Z","timestamp":1732789468000},"page":"119-132","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Classification and Recommendation of Mental Health Assistance Events Using an RNN-LSTM, Fast-And-Frugal Trees and Weighted Sum System"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-2689-1829","authenticated-orcid":false,"given":"Nathan R.","family":"Dickson","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0597-0113","authenticated-orcid":false,"given":"Nicholas H. M.","family":"Caldwell","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,11,29]]},"reference":[{"key":"9_CR1","unstructured":"McDaid, D., et al.: The economic case for investing in the prevention of mental health conditions in the UK. London School of Economics and Mental Health (2022). https:\/\/www.mentalhealth.org.uk\/explore-mental-health\/publications\/economic-case-investing-prevention-mental-health-conditions-UK"},{"key":"9_CR2","unstructured":"Hyde, J., et al.: Progress in improving mental health services in England. National Audit Office UK (2023). ISBN: 978-1-78604-472-3, https:\/\/www.nao.org.uk\/reports\/progress-in-improving-mental-health-services-in-england\/"},{"key":"9_CR3","unstructured":"Teuton, J.: Social prescribing for mental health: background paper. NHS Health Scotland (2015). https:\/\/www.healthscotland.scot\/media\/2067\/social-prescribing-for-mental-health-background-paper.pdf"},{"issue":"4","key":"9_CR4","doi-asserted-by":"publisher","first-page":"424","DOI":"10.1080\/10705420903299979","volume":"17","author":"JP Aguilar","year":"2009","unstructured":"Aguilar, J.P., Sen, S.: Comparing conceptualizations of social capital. J. Community Pract. 17(4), 424\u2013443 (2009)","journal-title":"J. Community Pract."},{"key":"9_CR5","doi-asserted-by":"publisher","first-page":"595","DOI":"10.1016\/S0749-0690(02)00025-3","volume":"18","author":"AR Herzog","year":"2002","unstructured":"Herzog, A.R., Ofstedal, M.B., Wheeler, L.M.: Social engagement and its relationship to health. Clin. Geriatr. Med. 18, 595\u2013609 (2002)","journal-title":"Clin. Geriatr. Med."},{"issue":"1","key":"9_CR6","doi-asserted-by":"publisher","first-page":"112","DOI":"10.1080\/10705422.2016.1269380","volume":"25","author":"A Kelley","year":"2017","unstructured":"Kelley, A., Riggleman, K., Clara, I., Navarro, A.E.: Determining the need for social work practice in a public library. J. Community Pract. 25(1), 112\u2013125 (2017)","journal-title":"J. Community Pract."},{"issue":"7","key":"9_CR7","doi-asserted-by":"publisher","first-page":"270","DOI":"10.1177\/0141076819890548","volume":"113","author":"RA Harrington","year":"2020","unstructured":"Harrington, R.A., Gray, M., Jani, A.: Digitally enabled social prescriptions: adaptive interventions to promote health in children and young people. J. R. Soc. Med. 113(7), 270\u2013273 (2020). https:\/\/doi.org\/10.1177\/0141076819890548","journal-title":"J. R. Soc. Med."},{"key":"9_CR8","doi-asserted-by":"publisher","unstructured":"Patel S., Craigen, G., Pinto da Costa, M., Inkster, B.: Opportunities and challenges for digital social prescribing in mental health: questionnaire study. J. Med. Internet Res. 23(3), e17438 (2021). https:\/\/doi.org\/10.2196\/17438","DOI":"10.2196\/17438"},{"key":"9_CR9","unstructured":"Reshamwala, A., Mishra, D., Pawar, P.: Review on natural language processing. IRACST \u2013 Eng. Sci. Technol. Int. J. (ESTIJ) 3(1), 113\u20136 (2013)"},{"issue":"1","key":"9_CR10","doi-asserted-by":"publisher","first-page":"51","DOI":"10.1002\/aris.1440370103","volume":"37","author":"GG Chowdhury","year":"2005","unstructured":"Chowdhury, G.G.: Natural language processing. Annu. Rev. Inf. Sci. 37(1), 51\u201389 (2005). https:\/\/doi.org\/10.1002\/aris.1440370103","journal-title":"Annu. Rev. Inf. Sci."},{"key":"9_CR11","doi-asserted-by":"crossref","unstructured":"Indurkhya, N., Damerau, F.J.: Handbook of Natural Language Processing. CRC Press, Taylor & Francis Group, Boca Raton, FL (2010)","DOI":"10.1201\/9781420085938"},{"key":"9_CR12","unstructured":"Marquez, L.: Machine Learning and Natural Language Processing. In: Curso de Industrias de la Lengua: La ingenier\u00eda Ling\u00fc\u00edstica en la Sociedad de la Informaci\u00f3n: 1\u201353. Universitat Politecnica de Catalunya, Barcelona (2000). https:\/\/upcommons.upc.edu\/handle\/2117\/96428. Accessed 15 May 2014"},{"key":"9_CR13","unstructured":"Mooney, R.J.: Comparative experiments on disambiguating word senses: an illustration of the role of bias in machine learning. In: Conference on Empirical Methods in Natural Language Processing, pp. 17\u201318. University of Pennsylvania, Philadelphia (1996). https:\/\/aclanthology.org\/W96-0208\/"},{"key":"9_CR14","unstructured":"Rish, I.: An empirical study of the naive Bayes classifier. In: IJCAI 2001 Workshop on Empirical Methods in Artificial Intelligence, pp. 41\u20136. IBM, New York (2001)"},{"key":"9_CR15","doi-asserted-by":"publisher","unstructured":"Islam, M.J., Wu, Q.M.J., Ahmadi, M., Sid-Ahmed, M.A.: Investigating the performance of I-Bayes classifiers and K-nearest neighbor classifiers. In: 2007 International Conference on Convergence Information Technology (ICCIT 2007), pp. 1541\u201346. IEEE (2007). https:\/\/doi.org\/10.1109\/ICCIT.2007.148","DOI":"10.1109\/ICCIT.2007.148"},{"key":"9_CR16","doi-asserted-by":"publisher","unstructured":"Granik, M., Mesyura, V.: Fake news detection using naive Bayes classifier. In: 2017 IEEE First Ukraine Conference on Electrical and Computer Engineering (UKRCON), pp. 900\u201303. IEEE (2017). https:\/\/doi.org\/10.1109\/UKRCON.2017.8100379","DOI":"10.1109\/UKRCON.2017.8100379"},{"issue":"3","key":"9_CR17","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3439726","volume":"54","author":"S Minaee","year":"2021","unstructured":"Minaee, S., et al.: Deep learning\u2013based text classification. ACM Comput. Surv. 54(3), 1\u201340 (2021). https:\/\/doi.org\/10.1145\/3439726","journal-title":"ACM Comput. Surv."},{"key":"9_CR18","doi-asserted-by":"publisher","first-page":"79","DOI":"10.1016\/j.artmed.2018.11.004","volume":"97","author":"I Banerjee","year":"2019","unstructured":"Banerjee, I., et al.: Comparative effectiveness of convolutional neural network (CNN) and recurrent neural network (RNN) architectures for radiology text report classification. Artif. Intell. Med. 97, 79\u201388 (2019). https:\/\/doi.org\/10.1016\/j.artmed.2018.11.004","journal-title":"Artif. Intell. Med."},{"key":"9_CR19","doi-asserted-by":"publisher","unstructured":"Lee, J.Y., Dernoncourt, F.: Sequential short-text classification with recurrent and convolutional neural network. In: Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 515\u201320. Association for Computational Linguistics. (2016). https:\/\/doi.org\/10.18653\/v1\/N16-1062","DOI":"10.18653\/v1\/N16-1062"},{"key":"9_CR20","unstructured":"Yin, W., Kann, K., Yu, M., Sch\u00fctze, H.: Comparative study of CNN and RNN for natural language processing. (2017). https:\/\/arxiv.org\/abs\/1702.01923"},{"issue":"1","key":"9_CR21","doi-asserted-by":"publisher","first-page":"1","DOI":"10.21608\/ejle.2021.45203.1014","volume":"8","author":"AN Ragheb","year":"2021","unstructured":"Ragheb, A.N., Gody, A., Said, T.: Comparative study of different types of RNN in speech classification. Egypt. J. Lang. Eng. 8(1), 1\u201316 (2021). https:\/\/doi.org\/10.21608\/ejle.2021.45203.1014","journal-title":"Egypt. J. Lang. Eng."},{"key":"9_CR22","doi-asserted-by":"publisher","unstructured":"Surkan, A.J., Singleton, J.C.: Neural networks for bond rating improved by multiple hidden layers. In: 1990 IJCNN International Joint Conference on Neural Networks (Volume 2), pp. 157\u201362. IEEE (1990). https:\/\/doi.org\/10.1109\/IJCNN.1990.137709","DOI":"10.1109\/IJCNN.1990.137709"},{"key":"9_CR23","unstructured":"Allen-Zhu, Z., Li, Y., Liang, Y.: Learning and generalization in overparameterized neural networks, going beyond two layers. In: 33rd Conference on Neural Information Processing Systems (NeurIPS 2019), pp. 1\u201312 (2019). https:\/\/papers.nips.cc\/paper\/2019\/file\/62dad6e273d32235ae02b7d321578ee8-Paper.pdf"},{"key":"9_CR24","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s10994-012-5312-9","volume":"91","author":"L Chekina","year":"2013","unstructured":"Chekina, L., Gutfreund, D., Kontorovich, A., Rokach, L., Shapira, B.: Exploiting label dependencies for improved sample complexity. Mach. Learn. 91, 1\u201342 (2013). https:\/\/doi.org\/10.1007\/s10994-012-5312-9","journal-title":"Mach. Learn."},{"key":"9_CR25","doi-asserted-by":"publisher","first-page":"7955","DOI":"10.1109\/TPAMI.2021.3119334","volume":"44","author":"W Liu","year":"2022","unstructured":"Liu, W., Wang, H., Shen, X., Tsang, I.W.: The emerging trends of multi-label learning. IEEE Trans. Pattern Anal. 44, 7955\u20137974 (2022). https:\/\/doi.org\/10.1109\/TPAMI.2021.3119334","journal-title":"IEEE Trans. Pattern Anal."},{"key":"9_CR26","doi-asserted-by":"publisher","first-page":"107965","DOI":"10.1016\/j.patcog.2021.107965","volume":"118","author":"AN Tarekegn","year":"2021","unstructured":"Tarekegn, A.N., Giacobini, M., Michalak, K.: A review of methods for imbalanced multi-label classification. Pattern Recogn. 118, 107965 (2021). https:\/\/doi.org\/10.1016\/j.patcog.2021.107965","journal-title":"Pattern Recogn."},{"key":"9_CR27","unstructured":"Sorower, M.: A literature survey on algorithms for multi-label learning. Oregon State University, Corvallis, Oregon, Technical Report (2010). https:\/\/www.researchgate.net\/publication\/266888594_A_Literature_Survey_on_Algorithms_for_Multi-label_Learning"},{"key":"9_CR28","doi-asserted-by":"publisher","unstructured":"Nasierding, G., Kouzani, A.Z..: Comparative evaluation of multi-label classification methods. In: 2012 9th International Conference on Fuzzy Systems and Knowledge Discovery, pp. 679\u2013683. IEEE (2012). https:\/\/doi.org\/10.1109\/FSKD.2012.6234347","DOI":"10.1109\/FSKD.2012.6234347"},{"key":"9_CR29","doi-asserted-by":"publisher","unstructured":"Sarwar, B., Karypis, G., Konstan, J., Riedl, J.: Analysis of recommendation algorithms for e-commerce. In: EC \u201900: Proceedings of the 2nd ACM Conference on Electronic Commerce, pp. 158\u201367. ACM Press, New York (2000). https:\/\/doi.org\/10.1145\/352871.352887","DOI":"10.1145\/352871.352887"},{"key":"9_CR30","doi-asserted-by":"publisher","unstructured":"Tang, J., Wang, K: Personalized Top-N sequential recommendation via convolutional sequence embedding. In: WSDM \u201918: Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining, pp. 656\u201373. ACM Press, New York (2018). https:\/\/doi.org\/10.1145\/3159652.3159656","DOI":"10.1145\/3159652.3159656"},{"key":"9_CR31","unstructured":"Gigerenzer, G., Todd, P.M.: Fast and frugal heuristics: the adaptive toolbox. In: Gigerenzer, G., Todd, P.M., ABC Research Group (eds.), Simple Heuristics That Make Us Smart, pp. 3\u201334. Oxford University Press, Oxford (1999)"},{"key":"9_CR32","doi-asserted-by":"publisher","unstructured":"Grefenstette, G.: Tokenization. In: van Halteren, H. (ed.) Syntactic Worldclass Tagging: Text, Speech and Language Technology, pp. 117\u201333. Springer, Dordrecht (1999). https:\/\/doi.org\/10.1007\/978-94-015-9273-4_9","DOI":"10.1007\/978-94-015-9273-4_9"},{"key":"9_CR33","unstructured":"Plisson, J., Lavra\u010d, N., Mladeni\u0107, D.: A Rule based approach to word lemmatization. In: Proceedings of the 2004 International Symposium on Information and Communication Technologies, pp.83\u201386 (2004). https:\/\/www.semanticscholar.org\/paper\/A-Rule-based-Approach-to-Word-Lemmatization-Plisson-Lavrac\/5319539616e81b02637b1bf90fb667ca2066cf14"},{"key":"9_CR34","doi-asserted-by":"publisher","unstructured":"Chandra, N., Khatri, S.K., Som, S.: Natural language processing approach to identify analogous data in offline data repository. In: Kapur, P., Klochkov, Y., Verma, A., Singh, G. (eds.) System Performance and Management Analytics. Asset Analytics, pp. 65\u201376. Springer, Singapore (2019). https:\/\/doi.org\/10.1007\/978-981-10-7323-6_6","DOI":"10.1007\/978-981-10-7323-6_6"},{"key":"9_CR35","unstructured":"Srivastava, N., et al.: Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15(1):1929\u201358 (2014). https:\/\/jmlr.org\/papers\/volume15\/srivastava14a\/srivastava14a.pdf"},{"key":"9_CR36","doi-asserted-by":"publisher","unstructured":"Song, M., Zhao, X., Liu, Y., Zhao, Z.: Text sentiment analysis based on convolutional neural network and bidirectional LSTM model. In: International Conference of Pioneering Computer Scientists, Engineers and Educators, pp. 55\u201368 (2018). https:\/\/doi.org\/10.1007\/978-981-13-2206-8_6","DOI":"10.1007\/978-981-13-2206-8_6"},{"key":"9_CR37","doi-asserted-by":"publisher","first-page":"229","DOI":"10.1016\/j.ins.2022.10.078","volume":"616","author":"K Zarzycki","year":"2022","unstructured":"Zarzycki, K., \u0141awry\u0144czuk, M.: Advanced predictive control for GRU and LSTM networks. Inf. Sci. 616, 229\u2013254 (2022). https:\/\/doi.org\/10.1016\/j.ins.2022.10.078","journal-title":"Inf. Sci."},{"key":"9_CR38","doi-asserted-by":"publisher","unstructured":"Cahuantzi, R., Chen, X., G\u00fcttel, S.: A Comparison of LSTM and GRU networks for learning symbolic sequences. In: Arai, K. (ed.) Intelligent Computing. SAI 2023. LNNS, vol. 739, pp. 771\u2013785. Springer, Cham (2023). https:\/\/doi.org\/10.1007\/978-3-031-37963-5_53","DOI":"10.1007\/978-3-031-37963-5_53"}],"container-title":["Lecture Notes in Computer Science","Artificial Intelligence XLI"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-77918-3_9","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,28]],"date-time":"2024-11-28T11:08:47Z","timestamp":1732792127000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-77918-3_9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,11,29]]},"ISBN":["9783031779176","9783031779183"],"references-count":38,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-77918-3_9","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2024,11,29]]},"assertion":[{"value":"29 November 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"Nicholas HM Caldwell declares no conflicts of interest. Nathan Dickson received a part-time salary for this work\u00a0from Suffolk Libraries.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Disclosure of Interests"}},{"value":"SGAI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Innovative Techniques and Applications of Artificial Intelligence","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Cambridge","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"United Kingdom","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17 December 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"19 December 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"44","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"sgai2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/bcs-sgai.org\/ai2024\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}