使用先进的技术和统计方法来预测和预防自杀
Evan M Kleiman1, Catherine R Glenn2, Richard T Liu3,4
1Department of Psychology, Rutgers, The State University of New Jersey, Piscataway, NJ, USA.
Nature reviews psychology
|August 17, 2023
概括
像可穿戴设备和机器学习这样的先进技术在预测和预防自杀行为方面表现有前途. 然而,仍需要大量的研究才能充分发挥其在预防自杀方面的潜力.
科学领域:
- 精神病学是一个精神病学.
- 数字健康数字健康
- 公共卫生 公共卫生
背景情况:
- 分析表明,在预测和预防自杀行为方面取得的成功有限.
- 技术和统计方面的进步被提出为潜在的解决方案.
- 需要对这些先进方法的前景采取平衡的视角.
研究的目的:
- 对自杀评估和预防的先进技术和统计方法的研究进行审查和综合.
- 弥合悲观的元分析和对新技术的乐观评价之间的差距.
- 提供关于自杀研究当前状态和未来方向的平衡概述.
主要方法:
- 对文献的审查,重点是评估和预防自杀的想法和行为.
- 讨论技术进步,包括智能手机,可穿戴设备和数字表型.
- 探索统计方法,如机器学习.
主要成果:
- 生态瞬间评估,可穿戴设备和数字表型化显示出改善自杀风险预测的潜力.
- 机器学习为分析与自杀行为相关的复杂数据提供了新的途径.
- 当前的研究强调了这些先进方法的承诺和局限性.
结论:
- 先进的技术和统计方法对提高自杀预测和预防具有重大前景.
- 进一步的研究和开发对于克服现有挑战和充分利用这些工具至关重要.
- 综合技术和传统方法的综合方法对于推进自杀预防科学至关重要.
相关概念视频
Introduction To Survival Analysis
277
Survival analysis is a statistical method used to study time-to-event data, where the "event" might represent outcomes like death, disease relapse, system failure, or recovery. A unique feature of survival data is censoring, which occurs when the event of interest has not been observed for some individuals during the study period. This requires specialized techniques to handle incomplete data effectively.
The primary goal of survival analysis is to estimate survival time—the time...
The primary goal of survival analysis is to estimate survival time—the time...
277
Cancer Survival Analysis
383
Cancer survival analysis focuses on quantifying and interpreting the time from a key starting point, such as diagnosis or the initiation of treatment, to a specific endpoint, such as remission or death. This analysis provides critical insights into treatment effectiveness and factors that influence patient outcomes, helping to shape clinical decisions and guide prognostic evaluations. A cornerstone of oncology research, survival analysis tackles the challenges of skewed, non-normally...
383
Stress Prevention and Stress Management Techniques VI
50
Adopting a healthier lifestyle often requires overcoming significant challenges, but leveraging psychological, social, and cultural resources can facilitate meaningful change. Effective self-change hinges on understanding and applying key tools such as motivation and goal setting, which help sustain efforts toward long-term health benefits.
Motivation and Self-Determination
Motivation, the driving force behind behavior, plays a pivotal role at every stage of the change process. The research...
Motivation and Self-Determination
Motivation, the driving force behind behavior, plays a pivotal role at every stage of the change process. The research...
50
Steps in Outbreak Investigation
152
In the ever-evolving field of public health, statistical analysis serves as a cornerstone for understanding and managing disease outbreaks. By leveraging various statistical tools, health professionals can predict potential outbreaks, analyze ongoing situations, and devise effective responses to mitigate impact. For that to happen, there are a few possible stages of the analysis:
152
Statistical Package for the Social Sciences (SPSS)
387
The Statistical Package for the Social Sciences, or SPSS, is a data management and analysis software suite. Developed by SPSS Inc. in 1968 and acquired by IBM in 2009, this tool was initially designed for social science data analysis, evolving to serve a wider range of disciplines. It was later renamed to Statistical Product and Service Solutions.
SPSS streamlines the process from data preparation to analysis and reporting. It is characterized by its user-friendly interface, which conceals...
SPSS streamlines the process from data preparation to analysis and reporting. It is characterized by its user-friendly interface, which conceals...
387
Statistical Methods for Analyzing Epidemiological Data
408
Epidemiological data primarily involves information on specific populations' occurrence, distribution, and determinants of health and diseases. This data is crucial for understanding disease patterns and impacts, aiding public health decision-making and disease prevention strategies. The analysis of epidemiological data employs various statistical methods to interpret health-related data effectively. Here are some commonly used methods:
408


