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Data Analysis and Knowledge Discovery  2022, Vol. 6 Issue (2/3): 222-232    DOI: 10.11925/infotech.2096-3467.2021.0883
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Clustering and Characterizing Depression Patients Based on Online Medical Records
Nie Hui(),Wu Xiaoyan,Lin Yun
School of Information Management, Sun Yat-Sen University, Guangzhou 510006, China
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Abstract  

[Objective] This study examines the online consultation records of depression patients, aiming to thoroughly understand their situation. [Methods] First, we retrieved the depression consultation records from haodf.com, an online medical platform. Then, we modeled the patients with word vectors, and identified patient groups with the K-means clustering algorithm. Third, we used visualization techniques, such as t-SNE, heat map, and word-cloud, to analyze the group structure and relationship among them. Finally,we identified the emotional-psychological, social, and behavioral differences of different groups and decided their treatment needs with the LDA topic model. [Results] We found six depression groups with different emotional-psychology, social relationship, and behavioral performance. The depression patients’ needs include: seeking suggestion on offline medical treatments, multi-faceted consultation, and inquiry about medication. [Limitations] We analyzed the differences in group characteristics by selecting keywords in each dimension based on part-of-speech and manual analysis. [Conclusions] The proposed method could help us understand patients and their needs, and then construct better online medical platforms.

Key wordsOnline Medical Care      Depression      Clustering      Visualization     
Received: 23 August 2021      Published: 07 January 2022
ZTFLH:  G353  
Fund:Guangzhou Science and Technology Plan Project(202002020036)
Corresponding Authors: Nie Hui,ORCID:0000-0001-8567-3084     E-mail: issnh@mail.sysu.edu.cn

Cite this article:

Nie Hui, Wu Xiaoyan, Lin Yun. Clustering and Characterizing Depression Patients Based on Online Medical Records. Data Analysis and Knowledge Discovery, 2022, 6(2/3): 222-232.

URL:

https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.2096-3467.2021.0883     OR     https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2022/V6/I2/3/222

Flow Chart of Research
Screenshot of the Consultation Records
Structure of LDA
Evaluation Results of Patient Clustering
病患群组 个案数占比 群组主题词云
C1 10.74% 倾向 轻生 悲观 提不起 念头 自残 失去 社交 不感兴趣 暴躁 压抑 哭泣 低落 胡思乱想 减退 烦躁不安 信心 易怒 自卑 脾气 自杀 没意思 绝望 心烦 高兴 厌世 恐惧 不安 下降 动力
C2 17.58% 感觉 入睡 有时 失眠 睡不着 症状 睡眠 气短 头疼 难受 乏力 心跳 出汗 容易 紧张 出现 身体 害怕 小时 头痛 胸闷 有点 没有 孩子 晚上 有时候 脑袋 睡着 头晕 觉得
C3 19.27% 不想 觉得 感觉 别人 有时候 喜欢 事情 没有 容易 害怕 开心 活着 孩子 每天 心里 有时 难过 讨厌 莫名其妙 想着 愿意 生气 说话 干什么 没意思 情绪 知道 难受 意义 世界
C4 22.10% 孩子 工作 父母 觉得 学校 老公 学习 妈妈 家人 母亲 情绪 生活 没有 上学 父亲 同学 知道 别人 不想 愿意 老师 问题 喜欢 手机 宝宝 分手 家庭 丈夫 离婚 患者
C5 12.09% 服用 mg 一片 毫克 舍曲林 奥氮 效果 服药 氮平 停药 一粒 黛力 半片 盐酸 帕罗西汀 西酞普兰 草酸 副作用 文拉法 阿立 一天 丙戊酸 碳酸锂 复发 药物 劳拉西泮 克隆 来士普 百忧解 胶囊
C6 18.22% 检查 治疗 医生 孩子 医院 药物 是否 患者 咨询 症状 复发 大夫 住院 测试 吃药 服药 病情 一下 发病 主任 需要 停药 问题 服用 手术 诊断 怀孕 用药 月份 就诊
Depression Patient Group Generated Based on K-means Clustering
Distribution of Depression Patient Groups
Co-word Network of Depression Patient Groups
特征维度 关键词个数 关键词示例
情绪与心理 32 害怕、焦虑、紧张、头痛、恐惧
家庭社会角色与关系 26 孩子、父母、老公、学校、母亲
行为表现 32 工作、失眠、学习、自杀、自残
Characteristic Dimensions and Keywords of Patient
Analysis of Differences in Emotional Cognition of Patient Groups
Analysis of Differences in Interpersonal Environment of Patient Groups
Analysis of Differences in Behavior of Patient Groups
就诊诉求 主题 主题词 主题解释
药物相关 T1 是否、吃药、需要、抑郁症、严重 吃药
T2 咨询、药物、药、副作用、用药 药物副作用
T3 吃、药、好、改善、效果 药物效果
就诊相关 T4 门诊、就诊、是否、需要、预约 是否门诊就诊
T5 医院、看、检查、就诊、科 医院检查科室
T6 抑郁症、是否、是不是、确诊、医生 确诊是否抑郁症
T7 想、知道、情况、这种、问题 知道情况
治疗相关 T8 抑郁、情绪、现在、是不是、心理咨询 缓解情绪
T9 希望、医生、帮助、治疗、建议 治疗建议
T10 控制、病情、调整、心理、疏导 控制病情
其他 T11 医生、问题、希望、帮、建议 其他
Patient’s Demand Model Based on LDA
Differences in Demand Distribution among Patient Groups
相关系数 C1 C2 C3 C4 C5 C6
C1 1.000
C2 0.555 1.000
C3 .900** 0.527 1.000
C4 .827** .791** .818** 1.000
C5 -0.109 0.500 -0.118 0.082 1.000
C6 0.527 .836** 0.509 .736** 0.555 1.000
Results of Spearman Correlation
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