电子病历中的情感词汇与患有抑郁症的重返工作岗位计划使用者的休假时间之间的关系。
Relationship between emotional words in electronic medical records and leave periods of users of a return-to-work program with depression.
作者信息
Kutsuna Ichiro, Hoshino Aiko, Morisugi Ami, Mori Yukari, Shirato Aki, Takeda Mirai, Isaji Hikari, Suwa Mami
机构信息
Department of Health Science, Graduate School of Medicine, Nagoya University, Nagoya, Japan.
Mental Clinic Anser, Medical Corporation Seiseikai, Aichi, Japan.
出版信息
Br J Occup Ther. 2022 Dec;85(12):993-1001. doi: 10.1177/03080226221107773. Epub 2022 Jun 15.
INTRODUCTION
We attempted to score data extracted from written medical records containing assessment results using natural language processing, and to clarify the relationship between duration of sick leave and the use of emotional words among return-to-work (RTW) program users on sick leave due to mental health problems.
METHOD
Participants were users of an RTW program. We extracted textual data from their electronic medical records, and gave all words a score based on the following two considerations: positivity score (the degree of positive emotion a word has) and emotion score with respect to seven emotions (sadness, anxiety, anger, disgust, trust, surprise, and joy), with the score for each emotion measured for each word. We analyzed relationships between duration of sick leave and each score.
RESULTS
Forty-two users participated. The results showed that high positive scores (β = -0.42, < 0.00) and high sadness scores (β = -0.60, < 0.00) were related to a shorter duration of sick leave, and high anger score (β = 0.52, < 0.00) was related to a longer duration of sick leave.
CONCLUSION
Professional assessments based on occupational therapy and natural language processing of medical records may predict the appropriate timing of RTW.
引言
我们试图使用自然语言处理技术对从包含评估结果的书面医疗记录中提取的数据进行评分，并阐明病假时长与因心理健康问题而休病假的复工计划使用者中情感词汇使用之间的关系。
方法
参与者为复工计划的使用者。我们从他们的电子医疗记录中提取文本数据，并基于以下两个因素为所有词汇打分：积极性得分（一个词汇所具有的积极情感程度）以及针对七种情感（悲伤、焦虑、愤怒、厌恶、信任、惊讶和喜悦）的情感得分，每个词汇的每种情感得分都进行了测量。我们分析了病假时长与每种得分之间的关系。
结果
42名使用者参与了研究。结果显示，高积极性得分（β = -0.42，P < 0.00）和高悲伤得分（β = -0.60，P < 0.00）与较短的病假时长相关，而高愤怒得分（β = 0.52，P < 0.00）与较长的病假时长相关。
结论
基于职业治疗和医疗记录自然语言处理的专业评估可能预测复工的合适时机。
Zotero 插件