A Collaborative Approach to Identifying Social Media Markers of Schizophrenia by Employing Machine Learning and Clinical Appraisals
A Collaborative Approach to Identifying Social Media Markers of Schizophrenia by Employing Machine Learning and Clinical Appraisals
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DOI：
10.2196/jmir.7956
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发表时间：
2017-08-01
影响因子：
7.4
通讯作者：
Kane, John M.
中科院分区：
文献类型：
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作者：
Birnbaum, Michael L.;Ernala, Sindhu Kiranmai;Kane, John M.
工作背景：对公开可用的Twitter提要的语言分析成功地区分了那些在网上自我披露患有精神分裂症的人和健康对照组。到目前为止，有限的努力包括专家输入来评估诊断自我披露的真实性。目的：本研究旨在通过探索人机合作的方法，从社交媒体上嘈杂的精神分裂症自我报告转移到更准确的诊断识别，其中共享内容的计算语言分析与临床评估相结合。方法：Twitter的时间轴数据是从671名自我披露诊断为精神分裂症的用户中提取的，由临床专家评估其真实性。来自被认为是真实的披露的数据被用来建立一个分类器，旨在区分精神分裂症患者和健康对照者。将分类器的结果与专家对新的、看不见的Twitter用户的评价进行比较。结果：在精神分裂症组中发现了显著的语言差异，包括更多地使用人际代词（P
Background: Linguistic analysis of publicly available Twitter feeds have achieved success in differentiating individuals who self-disclose online as having schizophrenia from healthy controls. To date, limited efforts have included expert input to evaluate the authenticity of diagnostic self-disclosures.Objective: This study aims to move from noisy self-reports of schizophrenia on social media to more accurate identification of diagnoses by exploring a human-machine partnered approach, wherein computational linguistic analysis of shared content is combined with clinical appraisals.Methods: Twitter timeline data, extracted from 671 users with self-disclosed diagnoses of schizophrenia, was appraised for authenticity by expert clinicians. Data from disclosures deemed true were used to build a classifier aiming to distinguish users with schizophrenia from healthy controls. Results from the classifier were compared to expert appraisals on new, unseen Twitter users.Results: Significant linguistic differences were identified in the schizophrenia group including greater use of interpersonal pronouns (P