人工智能在自杀研究中的生态瞬时评估数据中的应用：系统综述

The Application of AI to Ecological Momentary Assessment Data in Suicide Research: Systematic Review.

作者信息

Melia Ruth, Musacchio Schafer Katherine, Rogers Megan L, Wilson-Lemoine Emma, Joiner Thomas Ellis

机构信息

Health Research Institute, University of Limerick, Limerick, Ireland.

Psychology Department, Florida State University, Tallahassee, FL, United States.

出版信息

J Med Internet Res. 2025 Apr 17;27:e63192. doi: 10.2196/63192.

DOI:10.2196/63192

PMID:40245396

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC12046261/

Abstract

BACKGROUND

Ecological momentary assessment (EMA) captures dynamic processes suitable to the study of suicidal ideation and behaviors. Artificial intelligence (AI) has increasingly been applied to EMA data in the study of suicidal processes.

OBJECTIVE

This review aims to (1) synthesize empirical research applying AI strategies to EMA data in the study of suicidal ideation and behaviors; (2) identify methodologies and data collection procedures used, suicide outcomes studied, AI applied, and results reported; and (3) develop a standardized reporting framework for researchers applying AI to EMA data in the future.

METHODS

PsycINFO, PubMed, Scopus, and Embase were searched for published articles applying AI to EMA data in the investigation of suicide outcomes. The PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines were used to identify studies while minimizing bias. Quality appraisal was performed using CREMAS (adapted STROBE [Strengthening the Reporting of Observational Studies in Epidemiology] Checklist for Reporting Ecological Momentary Assessment Studies).

RESULTS

In total, 1201 records were identified across databases. After a full-text review, 12 (1%) articles, comprising 4398 participants, were included. In the application of AI to EMA data to predict suicidal ideation, studies reported mean area under the curve (0.74-0.86), sensitivity (0.64-0.81), specificity (0.73-0.86), and positive predictive values (0.72-0.77). Studies met between 4 and 13 of the 16 recommended CREMAS reporting standards, with an average of 7 items met across studies. Studies performed poorly in reporting EMA training procedures and treatment of missing data.

CONCLUSIONS

Findings indicate the promise of AI applied to self-report EMA in the prediction of near-term suicidal ideation. The application of AI to EMA data within suicide research is a burgeoning area hampered by variations in data collection and reporting procedures. The development of an adapted reporting framework by the research team aims to address this.

TRIAL REGISTRATION

Open Science Framework (OSF); https://doi.org/10.17605/OSF.IO/NZWUJ and PROSPERO CRD42023440218; https://www.crd.york.ac.uk/PROSPERO/view/CRD42023440218.

摘要

背景

生态瞬时评估（EMA）能够捕捉适合自杀意念和行为研究的动态过程。在自杀过程的研究中，人工智能（AI）已越来越多地应用于EMA数据。

目的

本综述旨在（1）综合将AI策略应用于EMA数据以研究自杀意念和行为的实证研究；（2）确定所使用的方法和数据收集程序、所研究的自杀结局、应用的AI以及报告的结果；（3）为未来将AI应用于EMA数据的研究人员制定一个标准化的报告框架。

方法

在PsycINFO、PubMed、Scopus和Embase中检索已发表的文章，这些文章在自杀结局调查中将AI应用于EMA数据。采用PRISMA（系统评价和Meta分析的首选报告项目）指南来识别研究，同时尽量减少偏倚。使用CREMAS（改编自STROBE[加强流行病学观察性研究报告]清单的生态瞬时评估研究报告清单）进行质量评估。

结果

各数据库共识别出1201条记录。经过全文审查，纳入了12篇（1%）文章，共4398名参与者。在将AI应用于EMA数据以预测自杀意念的研究中，报告的曲线下面积均值为（0.74 - 0.86），灵敏度为（0.64 - 0.81），特异度为（0.73 - 0.86），阳性预测值为（0.72 - 0.77）。研究符合16项推荐的CREMAS报告标准中的4至13项，各研究平均符合7项。研究在报告EMA训练程序和缺失数据处理方面表现不佳。