{"title":"基于机器学习的单相抑郁症和双相情感障碍的分辨方法,以及不同年龄青少年的精简候选名单","authors":"","doi":"10.1016/j.compbiomed.2024.109107","DOIUrl":null,"url":null,"abstract":"<div><h3>Background</h3><p>Variations in symptoms and indistinguishable depression episodes of unipolar depression (UD) and bipolar disorder (BD) make the discrimination difficult and time-consuming. For adolescents with high disease prevalence, an efficient diagnostic tool is important for the discrimination and treatment of BU and UD.</p></div><div><h3>Methods</h3><p>This multi-center cross-sectional study involved 1587 UD and 246 BD adolescents aged 12–18. A combination of standard questionnaires and demographic information was collected for the construction of a full-item list. The unequal patient number was balanced with three data balancing algorithms, and 4 machine learning algorithms were compared for the discrimination ability of UD and BD in three age groups: all ages, 12–15 and 16–18. Random forest (RF) with the highest accuracy were used to rank the importance of features/items and construct the 25-item shortlist. A separate dataset was used for the final performance evaluation with the shortlist, and the discrimination ability for UD and BD was investigated.</p></div><div><h3>Results</h3><p>RF performed the best for UD and BD discrimination in all 3 age groups (AUC 0.88–0.90). The most important features that differentiate UD from BD belong to Parental Bonding Instrument (PBI) and Loneliness Scale of the University of California at Los Angeles (UCLA). With RF and the 25-item shortlist, the diagnostic accuracy can still reach around 80 %, achieving 95 % of the accuracy levels obtained with all features.</p></div><div><h3>Conclusions</h3><p>Through machine learning algorithms, the most influencing factors for UD and BD classification were recombined and applied for rapid diagnosis. This highly feasible method holds the potential for convenient and accurate diagnosis of young patients in research and clinical practice.</p></div>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":null,"pages":null},"PeriodicalIF":7.0000,"publicationDate":"2024-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Machine learning-based discrimination of unipolar depression and bipolar disorder with streamlined shortlist in adolescents of different ages\",\"authors\":\"\",\"doi\":\"10.1016/j.compbiomed.2024.109107\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><h3>Background</h3><p>Variations in symptoms and indistinguishable depression episodes of unipolar depression (UD) and bipolar disorder (BD) make the discrimination difficult and time-consuming. For adolescents with high disease prevalence, an efficient diagnostic tool is important for the discrimination and treatment of BU and UD.</p></div><div><h3>Methods</h3><p>This multi-center cross-sectional study involved 1587 UD and 246 BD adolescents aged 12–18. A combination of standard questionnaires and demographic information was collected for the construction of a full-item list. The unequal patient number was balanced with three data balancing algorithms, and 4 machine learning algorithms were compared for the discrimination ability of UD and BD in three age groups: all ages, 12–15 and 16–18. Random forest (RF) with the highest accuracy were used to rank the importance of features/items and construct the 25-item shortlist. A separate dataset was used for the final performance evaluation with the shortlist, and the discrimination ability for UD and BD was investigated.</p></div><div><h3>Results</h3><p>RF performed the best for UD and BD discrimination in all 3 age groups (AUC 0.88–0.90). The most important features that differentiate UD from BD belong to Parental Bonding Instrument (PBI) and Loneliness Scale of the University of California at Los Angeles (UCLA). With RF and the 25-item shortlist, the diagnostic accuracy can still reach around 80 %, achieving 95 % of the accuracy levels obtained with all features.</p></div><div><h3>Conclusions</h3><p>Through machine learning algorithms, the most influencing factors for UD and BD classification were recombined and applied for rapid diagnosis. This highly feasible method holds the potential for convenient and accurate diagnosis of young patients in research and clinical practice.</p></div>\",\"PeriodicalId\":10578,\"journal\":{\"name\":\"Computers in biology and medicine\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":7.0000,\"publicationDate\":\"2024-09-16\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Computers in biology and medicine\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0010482524011922\",\"RegionNum\":2,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"BIOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computers in biology and medicine","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0010482524011922","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"BIOLOGY","Score":null,"Total":0}
引用次数: 0
摘要
背景单相抑郁症(UD)和双相情感障碍(BD)的症状存在差异,抑郁发作也难以区分,这使得鉴别工作既困难又耗时。这项多中心横断面研究涉及 1587 名 12-18 岁的单相抑郁症和 246 名双相抑郁症青少年。研究结合了标准问卷和人口统计学信息,建立了全项目表。通过三种数据平衡算法对不相等的患者人数进行了平衡,并比较了 4 种机器学习算法对所有年龄、12-15 岁和 16-18 岁三个年龄组的 UD 和 BD 的辨别能力。采用准确率最高的随机森林(RF)对特征/项目的重要性进行排序,并构建 25 个项目的候选名单。结果在所有 3 个年龄组中,随机森林在区分 UD 和 BD 方面表现最佳(AUC 0.88-0.90)。区分 UD 和 BD 的最重要特征是父母亲子关系量表(PBI)和加州大学洛杉矶分校(UCLA)的孤独感量表。结论通过机器学习算法,将 UD 和 BD 分类的最大影响因素重新组合并应用于快速诊断。这种高度可行的方法有望在研究和临床实践中为年轻患者提供便捷、准确的诊断。
Machine learning-based discrimination of unipolar depression and bipolar disorder with streamlined shortlist in adolescents of different ages
Background
Variations in symptoms and indistinguishable depression episodes of unipolar depression (UD) and bipolar disorder (BD) make the discrimination difficult and time-consuming. For adolescents with high disease prevalence, an efficient diagnostic tool is important for the discrimination and treatment of BU and UD.
Methods
This multi-center cross-sectional study involved 1587 UD and 246 BD adolescents aged 12–18. A combination of standard questionnaires and demographic information was collected for the construction of a full-item list. The unequal patient number was balanced with three data balancing algorithms, and 4 machine learning algorithms were compared for the discrimination ability of UD and BD in three age groups: all ages, 12–15 and 16–18. Random forest (RF) with the highest accuracy were used to rank the importance of features/items and construct the 25-item shortlist. A separate dataset was used for the final performance evaluation with the shortlist, and the discrimination ability for UD and BD was investigated.
Results
RF performed the best for UD and BD discrimination in all 3 age groups (AUC 0.88–0.90). The most important features that differentiate UD from BD belong to Parental Bonding Instrument (PBI) and Loneliness Scale of the University of California at Los Angeles (UCLA). With RF and the 25-item shortlist, the diagnostic accuracy can still reach around 80 %, achieving 95 % of the accuracy levels obtained with all features.
Conclusions
Through machine learning algorithms, the most influencing factors for UD and BD classification were recombined and applied for rapid diagnosis. This highly feasible method holds the potential for convenient and accurate diagnosis of young patients in research and clinical practice.
期刊介绍:
Computers in Biology and Medicine is an international forum for sharing groundbreaking advancements in the use of computers in bioscience and medicine. This journal serves as a medium for communicating essential research, instruction, ideas, and information regarding the rapidly evolving field of computer applications in these domains. By encouraging the exchange of knowledge, we aim to facilitate progress and innovation in the utilization of computers in biology and medicine.