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相关概念视频

Survival Tree01:19

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Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
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Deindividuation is a form of social influence on an individual’s behavior such that the individual engages in unusual or non-normal behavior while in a group setting. Why? Because in these group settings, the individual no longer sees themselves as an individual anymore, disinhibiting their behavior and personal restraint.
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The Stereotype Content Model (SCM) was first proposed by Susan Fiske and her colleagues (Fiske, Cuddy, Glick & Xu, 2002; see also Fiske, 2012 and Fiske, 2017). The SCM specifies that when someone encounters a new group, they will stereotype them based on two metrics: warmth—or that group’s perceived intent, and how likely they are to provide help or inflict harm—and competence—or their ability to carry out that objective. Depending on the warmth-competence...
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Updated: Sep 17, 2025

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使用深层嵌入式集群识别和描述自杀死者的亚型.

Anas Belouali1, Christopher Kitchen2, Ayah Zirikly3

  • 1Division of General Internal Medicine, Biomedical Informatics and Data Science (BIDS), Johns Hopkins School of Medicine, 2024 East Monument St. S 1-200, Baltimore, MD, 21205, USA. abeloua1@jhu.edu.

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|July 2, 2025
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概括
此摘要是机器生成的。

研究人员使用链接的健康和公共卫生数据确定了四个不同的自杀概况. 这些个人资料突出了人口和临床差异,为有针对性的自杀预防策略提供了信息.

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科学领域:

  • 公共卫生 公共卫生
  • 数据科学数据科学数据科学
  • 精神病学是一个精神病学.

背景情况:

  • 缺乏使用链接的临床和公共卫生数据对自杀死者的亚型进行的人口水平研究.
  • 识别不同的自杀概况对于制定有效的,有针对性的预防策略至关重要.

研究的目的:

  • 通过链接的临床和公共卫生数据,识别和描述不同类型的自杀死者.
  • 评估深层嵌入式聚类对自杀概况识别的实用性.

主要方法:

  • 马里兰州自杀数据仓库 (MSDW) 从2016-2019年进行的回顾性分析.
  • 包括848名自杀死亡者和4,161名意外死亡者,以及相关的电子健康记录和医院出院数据.
  • 应用深层嵌入式集群 (k=2-10) 与稳定性指标 (交叉验证的预测强度为0.94) 进行配置描述.

主要成果:

  • 确定了四种不同的自杀概况,具有显著的人口和临床差异.
  • 个人资料1 (23.2%):老年人具有较高的并发症.
  • 个人资料2 (19.2%):精神疾病,医疗保健利用率高,社会需求显著.
  • 个人资料3 (25.4%):年轻人,精神疾病,没有社会需求,高的医疗补助使用.
  • 个人资料4 (32.2%):临床参与较少,医疗保健访问较少.

结论:

  • 深层嵌入式集群有效地识别了有意义和稳定的自杀死者的个人资料.
  • 鉴定到的个人资料显示了人口统计,临床因素和医疗保健参与度的显著差异.
  • 这些独特的个人资料可以为定制的,人口层面的自杀预防策略的开发和部署提供信息.