[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127760-en":3,"doc-seo-127760-105":31,"detail-sidebar-cat-0-en-105":96},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},127760,1099523882182,"Eliana","https://ap-avatar.wpscdn.com/davatar_6f874abed73319feea01a86fa6f0fab8",8,"Research & Report","A novel machine learning-based prediction method for patients at risk of developing depressive symptoms - Abstract","Depression prediction is a high-priority research area because early identification enables timely intervention and can improve treatment outcomes. Self-reported feelings can serve as valuable biomarkers when they are represented in a lower-dimensional network form, simplifying interaction patterns among depression-related symptoms. The study applies a graph convolutional network (GCN) to predict depression-prone patients using self-reported log data as input, with data augmentation to address limited dataset size, achieving 86–97% accuracy and 0.83–0.94 F1 across three experimental cases.","UCLA  \nUCLA Previously Published Works  \nTitle  \nA novel machine learning-based prediction method for patients at risk of developing depressive symptoms using a small data.  \nPermalink  \n[https://escholarship.org/uc/item/4hz1n15w](https://escholarship.org/uc/item/4hz1n15w)  \nJournal  \nPLoS ONE, 19(5)  \nAuthors  \nYun, Minyoung  \nJeon, Minjeong Yang, Heyoung  \nPublication Date  \n2024  \nDOI  \n10.1371/journal.pone.0303889  \nPeer reviewed  \n[eScholarship.org](eScholarship.org) Powered by the California Digital Library  \nUniversity of California  \nPLOS ONE  \nOPEN ACCESS  \nCitation: Yun M, Jeon M, Yang H (2024) A novel machine learning-based prediction method for patients at risk of developing depressive symptoms using a small data. PLoS ONE 19(5): e0303889 .  \n[https://doi.org/10.1371/journal.pone.0303889](https://doi.org/10.1371/journal.pone.0303889)  \n[Editor:](Editor: Najmul Hasan)[ Najmul Hasan](Editor: Najmul Hasan), BRAC Business School, BRAC University, BANGLADESH  \nReceived: May 30, 2023  \nAccepted: May 3, 2024  \nPublished: May 22, 2024  \nCopyright: © 2024 Yun et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.  \nData Availability Statement: Data Availability Statement We uploaded the code and data we produced for this study to a public repository. Anyone can download them from [https://github](https://github). com/hyyangkisti/prediction_of_depression.  \nFunding: This research was supported by the Korea Institute of Science and Technology Information (KISTI) grant funded by the Korea government (No. K-23-L05-C02-S16 and No. K-23-L03-C02) . The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.  \nRESEARCH ARTICLE  \nA novel machine learning-based prediction method for patients at risk of developing depressive symptoms using a small data  \nMinyoung Yun1,2, Minjeong Jeon3, Heyoung Yang4 *  \n1 Center for R&D Investment and Strategy Research, Korea Institute of Science and Technology Information, Seoul, Korea, 2 ´Ecole nationale sup´erieure d’Arts et M´etiers, Paris, France, 3 School of Education & Information Studies, University of California, Los Angeles, Los Angeles, LA, United States of America,  \n4 Center for Future Technology Analysis, Korea Institute of Science and Technology Information, Seoul, Korea  \n* [hyyang@kisti.re.kr](hyyang@kisti.re.kr)  \nAbstract  \nThe prediction of depression is a crucial area of research which makes it one of the top priorities in mental health research as it enables early intervention and can lead to higher success rates in treatment. Self-reported feelings by patients represent a valuable biomarker for predicting depression as they can be expressed in a lower-dimensional network form, offering an advantage in visualizing the interactive characteristics of depression-related feelings. Furthermore, the network form of data expresses high-dimensional data in a compact form, making the data easy to use as input for the machine learning processes. In this study, we applied the graph convolutional network (GCN) algorithm, an effective machine learning tool for handling network data, to predict depression-prone patients using the network form of self-reported log data as the input. We took a data augmentation step to expand the initially small dataset and fed the resulting data into the GCN algorithm, which achieved a high level of accuracy from 86–97% and an F1 (harmonic mean of precision and recall) score of 0.83–0.94 through three experimental cases. In these cases, the ratio of depressive cases varied, and high accuracy and F1 scores were observed in all three cases. This study not only demonstrates the potential for predicting depression-prone patients using self-reported logs as a biomarker in advance, but also shows promise in handling ","cbCaitchK1AGnt3V","https://ap.wps.com/l/cbCaitchK1AGnt3V","pdf",1005030,2,1,10,"English","en",105,"# Abstract\n# Introduction","[{\"question\":\"Why is depression prediction important in mental health research?\",\"answer\":\"Depression prediction supports early intervention, which can improve the success rates of treatment and is therefore a key research priority in mental health.\"},{\"question\":\"How does the study represent patient information for machine learning?\",\"answer\":\"It converts self-reported feelings into a network form based on self-reported log data, enabling the model to learn interaction patterns among depression-related symptoms.\"},{\"question\":\"What algorithm and strategy are used to handle limited data?\",\"answer\":\"The study uses a graph convolutional network (GCN) and performs data augmentation to expand the initially small dataset before training and evaluation.\"},{\"question\":\"What performance results were achieved in the experiments?\",\"answer\":\"Across three experimental cases with varying ratios of depressive cases, the method reached 86–97% accuracy and F1 scores of 0.83–0.94.\"}]","A novel machine learning-based prediction method for patients at risk of developing depressive symptoms - 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