Boggs Jennifer M, Kafka Julie M
Kaiser Permanente Colorado, Institute for Health Research, Aurora, CO USA.
Department of Health Behavior, Gillings School of Global Public Health at University of North Carolina Chapel Hill, Chapel Hill, NC USA.
Curr Epidemiol Rep. 2022;9(3):126-134. doi: 10.1007/s40471-022-00293-w. Epub 2022 Jul 26.
Applying text mining to suicide research holds a great deal of promise. In this manuscript, literature from 2019 to 2021 is critically reviewed for text mining projects that use electronic health records, social media data, and death records.
Text mining has helped identify risk factors for suicide in general and specific populations (e.g., older adults), has been combined with structured variables in EHRs to predict suicide risk, and has been used to track trends in social media suicidal discourse following population level events (e.g., COVID-19, celebrity suicides).
Future research should utilize text mining along with data linkage methods to capture more complete information on risk factors and outcomes across data sources (e.g., combining death records and EHRs), evaluate effectiveness of NLP-based intervention programs that use suicide risk prediction, establish standards for reporting accuracy of text mining programs to enable comparison across studies, and incorporate implementation science to understand feasibility, acceptability, and technical considerations.
将文本挖掘应用于自杀研究具有很大的前景。在本手稿中，对2019年至2021年期间使用电子健康记录、社交媒体数据和死亡记录的文本挖掘项目的文献进行了批判性综述。
文本挖掘有助于识别一般人群和特定人群（如老年人）的自杀风险因素，已与电子健康记录中的结构化变量相结合以预测自杀风险，并已用于跟踪人群层面事件（如COVID-19、名人自杀）后社交媒体上自杀话语的趋势。
未来的研究应将文本挖掘与数据链接方法结合使用，以获取跨数据源（如结合死亡记录和电子健康记录）的风险因素和结果的更完整信息，评估使用自杀风险预测的基于自然语言处理的干预项目的有效性，建立文本挖掘项目报告准确性标准以实现跨研究比较，并纳入实施科学以了解可行性、可接受性和技术考虑因素。
