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相关概念视频

Pharmacovigilance01:19

Pharmacovigilance

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Post-marketing surveillance is a critical component of pharmaceutical regulation, often uncovering unanticipated adverse drug reactions (ADRs) once a drug is widely used over an extended period.
This process, termed pharmacovigilance, aims to detect, evaluate, and minimize harmful effects related to medication use. The data collection for pharmacovigilance depends on spontaneous reporting systems, where healthcare professionals or patients voluntarily report suspected ADRs.
In some cases, there...
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Stereotype Content Model02:16

Stereotype Content Model

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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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相关实验视频

Updated: Jan 9, 2026

Design and Analysis for Fall Detection System Simplification
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一个基于机器学习的框架,用于识别来自社交媒体数据的消费者产品损伤.

Harmya Bhatt1, Souvik Das2, Yi Jade Han3

  • 1Department of Computer Science, Purdue University, West Lafayette, IN, USA.

Injury
|December 9, 2025
PubMed
概括

本研究引入了一种机器学习框架,使用社交媒体数据快速检测与产品相关的伤害,改善消费者安全监督,并使更快的干预成为可能.

关键词:
伤害监测伤害监测伤害监测伤害监测伤害监测自然语言处理自然语言处理.产品安全 产品安全社交媒体分析文本挖掘 (Text Mining) 是一种文字挖掘方式.

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Integrating Computerized Linguistic and Social Network Analyses to Capture Addiction Recovery Capital in an Online Community
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Integrating Computerized Linguistic and Social Network Analyses to Capture Addiction Recovery Capital in an Online Community

Published on: May 31, 2019

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相关实验视频

Last Updated: Jan 9, 2026

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Integrating Computerized Linguistic and Social Network Analyses to Capture Addiction Recovery Capital in an Online Community
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Integrating Computerized Linguistic and Social Network Analyses to Capture Addiction Recovery Capital in an Online Community

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科学领域:

  • 消费者产品安全 产品安全
  • 公共卫生监督 公共卫生监督
  • 机器学习应用 机器学习应用

背景情况:

  • 每年都会发生数以百万计的与产品相关的伤害,传统的监控方法导致识别伤害模式的延迟.
  • 目前依赖医院数据的方法很慢,阻碍了如产品召回等及时的预防行动.
  • 这种延迟导致了与消费者产品相关的持续伤害.

研究的目的:

  • 开发和评估用于实时伤害监测的机器学习 (ML) 框架.
  • 从社交媒体中提取产品损害细节,以便快速识别趋势并进行干预.
  • 提高消费者产品安全监测的速度和有效性.

主要方法:

  • 提出了一个两阶段的ML框架,利用社交媒体帖子 (Reddit) 和国家电子伤害监控系统 (NEISS) 数据.
  • 第1阶段将职位分类为受伤相关的职位或不使用在不同数据集上训练的ML模型.
  • 第二阶段使用ML模型预测受伤的身体部位和受伤诊断代码.

主要成果:

  • 第1阶段的模型 (LSTM,GRU) 在分类受伤相关职位方面获得了72%的F1得分.
  • 第二阶段模型 (SGD) 显示,预测受伤的身体部位的F1得分为86%.
  • 第二阶段的模型还在伤害诊断代码预测方面获得了76%的F1分数.

结论:

  • ML框架显示了伤害监测的有希望的准确性.
  • 通过这种框架进行社交媒体数据分析,可以识别新出现的与产品相关的伤害趋势.
  • 拟议的系统可以显著增强消费产品的现有公共卫生监测工作.