{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,28]],"date-time":"2025-03-28T07:05:02Z","timestamp":1743145502115,"version":"3.40.3"},"publisher-location":"Cham","reference-count":24,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031468124"},{"type":"electronic","value":"9783031468131"}],"license":[{"start":{"date-parts":[[2023,10,24]],"date-time":"2023-10-24T00:00:00Z","timestamp":1698105600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,10,24]],"date-time":"2023-10-24T00:00:00Z","timestamp":1698105600000},"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":[[2024]]},"DOI":"10.1007\/978-3-031-46813-1_4","type":"book-chapter","created":{"date-parts":[[2023,10,23]],"date-time":"2023-10-23T12:01:50Z","timestamp":1698062510000},"page":"47-57","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Predictive Modeling for Detection of Depression Using Machine Learning"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1388-3220","authenticated-orcid":false,"given":"Mart\u00edn","family":"Di Felice","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-0354-8803","authenticated-orcid":false,"given":"Ariel","family":"Deroche","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-6594-643X","authenticated-orcid":false,"given":"Ilan","family":"Trupkin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6760-4704","authenticated-orcid":false,"given":"Parag","family":"Chatterjee","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4197-3880","authenticated-orcid":false,"given":"Mar\u00eda F.","family":"Pollo-Cattaneo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,10,24]]},"reference":[{"issue":"6","key":"4_CR1","doi-asserted-by":"publisher","DOI":"10.1136\/bmjopen-2014-007079","volume":"5","author":"LA Manwell","year":"2015","unstructured":"Manwell, L.A., et al.: What is mental health? Evidence towards a new definition from a mixed methods multidisciplinary international survey. BMJ Open 5(6), e007079 (2015). https:\/\/doi.org\/10.1136\/bmjopen-2014-007079","journal-title":"BMJ Open"},{"key":"4_CR2","unstructured":"ICD-11 for Mortality and Morbidity Statistics. https:\/\/icd.who.int\/browse11\/l-m\/en#\/http%3a%2f%2fid.who.int%2ficd%2fentity%2f1563440232. Accessed 31 Mar 2023"},{"key":"4_CR3","doi-asserted-by":"publisher","unstructured":"Lim, G.Y., Tam, W.W., Lu, Y., Ho, C.S., Zhang, M.W., Ho, R.C.: Prevalence of depression in the community from 30 countries between 1994 and 2014. Sci. Rep. 8(1), Article no. 1 (2018). https:\/\/doi.org\/10.1038\/s41598-018-21243-x","DOI":"10.1038\/s41598-018-21243-x"},{"key":"4_CR4","unstructured":"Diagnostic and Statistical Manual of Mental Disorders: DSM Library. https:\/\/dsm.psychiatryonline.org\/doi\/book\/10.1176\/appi.books.9780890425787. Accessed 23 Jun 2022"},{"key":"4_CR5","unstructured":"Bains, N., Abdijadid, S.: Major depressive disorder. In: StatPearls. StatPearls Publishing, Treasure Island (2023). http:\/\/www.ncbi.nlm.nih.gov\/books\/NBK559078\/. Accessed 3 Apr 2023"},{"key":"4_CR6","unstructured":"Depressive disorder (depression). https:\/\/www.who.int\/news-room\/fact-sheets\/detail\/depression. Accessed 19 Apr 2023"},{"key":"4_CR7","unstructured":"Boden, M.A.: Artificial Intelligence. Elsevier (1996)"},{"key":"4_CR8","doi-asserted-by":"publisher","DOI":"10.1016\/j.artmed.2019.101704","volume":"99","author":"AMY Tai","year":"2019","unstructured":"Tai, A.M.Y., et al.: Machine learning and big data: implications for disease modeling and therapeutic discovery in psychiatry. Artif. Intell. Med. 99, 101704 (2019). https:\/\/doi.org\/10.1016\/j.artmed.2019.101704","journal-title":"Artif. Intell. Med."},{"key":"4_CR9","unstructured":"5 Trends Emerge in Gartner Hype Cycle For Emerging Technologies. Gartner (2018). https:\/\/www.gartner.com\/smarterwithgartner\/5-trends-emerge-in-gartner-hype-cycle-for-emerging-technologies-2018. Accessed 14 June 2022"},{"key":"4_CR10","unstructured":"Villars, R.L., Eastwood, M., Olofson, C.W.: Big Data: What It Is and Why You Should Care, p. 14 (2011)"},{"issue":"1","key":"4_CR11","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.W., Berry, J.T., Mokdad, A.H.: The PHQ-8 as a measure of current depression in the general population. J. Affect. Disord. 114(1), 163\u2013173 (2009). https:\/\/doi.org\/10.1016\/j.jad.2008.06.026","journal-title":"J. Affect. Disord."},{"issue":"9","key":"4_CR12","doi-asserted-by":"publisher","first-page":"509","DOI":"10.3928\/0048-5713-20020901-06","volume":"32","author":"K Kroenke","year":"2002","unstructured":"Kroenke, K., Spitzer, R.L.: The PHQ-9: a new depression diagnostic and severity measure. Psychiatr. Ann. 32(9), 509\u2013515 (2002). https:\/\/doi.org\/10.3928\/0048-5713-20020901-06","journal-title":"Psychiatr. Ann."},{"key":"4_CR13","unstructured":"Beck Depression Inventory (BDI). https:\/\/www.apa.org. https:\/\/www.apa.org\/pi\/about\/publications\/caregivers\/practice-settings\/assessment\/tools\/beck-depression. Accessed 04 July 2022"},{"issue":"11","key":"4_CR14","doi-asserted-by":"publisher","first-page":"116","DOI":"10.1007\/s11920-019-1094-0","volume":"21","author":"S Graham","year":"2019","unstructured":"Graham, S., et al.: Artificial intelligence for mental health and mental illnesses: an overview. Curr. Psychiatry Rep. 21(11), 116 (2019). https:\/\/doi.org\/10.1007\/s11920-019-1094-0","journal-title":"Curr. Psychiatry Rep."},{"key":"4_CR15","doi-asserted-by":"publisher","unstructured":"Tran, B.X., et al.: The current research landscape on the artificial intelligence application in the management of depressive disorders: a bibliometric analysis. Int. J. Environ. Res. Public. Health 16(12), Article no. 12 (2019). https:\/\/doi.org\/10.3390\/ijerph16122150","DOI":"10.3390\/ijerph16122150"},{"key":"4_CR16","doi-asserted-by":"publisher","unstructured":"Morley, J., et al.: The ethics of AI in health care: a mapping review. Soc. Sci. Med. 1982 260, 113172 (2020). https:\/\/doi.org\/10.1016\/j.socscimed.2020.113172","DOI":"10.1016\/j.socscimed.2020.113172"},{"issue":"4","key":"4_CR17","doi-asserted-by":"publisher","first-page":"966","DOI":"10.1111\/inm.13114","volume":"32","author":"O Higgins","year":"2023","unstructured":"Higgins, O., Short, B.L., Chalup, S.K., Wilson, R.L.: Artificial intelligence (AI) and machine learning (ML) based decision support systems in mental health: an integrative review. Int. J. Ment. Health Nurs. 32(4), 966\u2013978 (2023). https:\/\/doi.org\/10.1111\/inm.13114","journal-title":"Int. J. Ment. Health Nurs."},{"key":"4_CR18","unstructured":"Depression and anxiety data. https:\/\/www.kaggle.com\/datasets\/shahzadahmad0402\/depression-and-anxiety-data . Accessed 19 Apr 2023"},{"key":"4_CR19","doi-asserted-by":"publisher","unstructured":"Patro, S.G.K., Sahu, K.K.: Normalization: a preprocessing stage. arXiv (2015). https:\/\/doi.org\/10.48550\/arXiv.1503.06462","DOI":"10.48550\/arXiv.1503.06462"},{"key":"4_CR20","volume-title":"Practical Statistics for Data Scientists: 50+ Essential Concepts Using R and Python","author":"PC Bruce","year":"2020","unstructured":"Bruce, P.C., Bruce, A., Gedeck, P.: Practical Statistics for Data Scientists: 50+ Essential Concepts Using R and Python, 2nd edn. O\u2019Reilly Media Inc, Sebastopol (2020)","edition":"2"},{"key":"4_CR21","unstructured":"Vance, W.: Data Science: Tips and Tricks to Learn Data Science Theories Effectively (2020)"},{"key":"4_CR22","unstructured":"Garc\u00eda Herrero, J.: Ciencia de datos: t\u00e9cnicas anal\u00edticas y aprendizaje estad\u00edstico en un enfoque pr\u00e1ctico. Alfaomega (2018)"},{"key":"4_CR23","unstructured":"Welcome to Python.org. Python.org. https:\/\/www.python.org\/. Accessed 28 Oct 2022"},{"key":"4_CR24","doi-asserted-by":"publisher","first-page":"2","DOI":"10.1016\/j.bdr.2015.12.001","volume":"5","author":"Q Zou","year":"2016","unstructured":"Zou, Q., Xie, S., Lin, Z., Wu, M., Ju, Y.: Finding the best classification threshold in imbalanced classification. Big Data Res. 5, 2\u20138 (2016). https:\/\/doi.org\/10.1016\/j.bdr.2015.12.001","journal-title":"Big Data Res."}],"container-title":["Communications in Computer and Information Science","Applied Informatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-46813-1_4","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,1,17]],"date-time":"2024-01-17T05:03:36Z","timestamp":1705467816000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-46813-1_4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,10,24]]},"ISBN":["9783031468124","9783031468131"],"references-count":24,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-46813-1_4","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"type":"print","value":"1865-0929"},{"type":"electronic","value":"1865-0937"}],"subject":[],"published":{"date-parts":[[2023,10,24]]},"assertion":[{"value":"24 October 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICAI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Applied Informatics","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Guayaquil","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Ecuador","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":"26 October 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28 October 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"6","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icai22023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/icai.itiud.org","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":"EquinOCS","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"132","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":"30","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":"23% - 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)"}}]}}