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相关概念视频

Clinical Trials: Overview01:11

Clinical Trials: Overview

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Clinical development focuses on how the drug will interact with the human body and encompasses four key phases of clinical trials, each serving a specific purpose in assessing the safety and effectiveness of new drugs. These phases overlap and build upon one another. Phase I involves a small group of healthy volunteers (typically 20-80 individuals) or, in cases where significant toxicity is expected, patients with the targeted disease, such as cancer or AIDS. The volunteers are tested for...
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Clinical Trials01:16

Clinical Trials

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Clinical trials are prospective experimental studies conducted on humans to determine the safety and efficacy of treatments, drugs, diet methods, and medical devices. Using statistics in clinical trials enables researchers to derive reasonable and accurate conclusions from the collected data, allowing them to make wise decisions in uncertain situations. In medical research, statistical methods are crucial for preventing errors and bias.
There are four phases in a clinical trial. A phase one...
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Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack
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单臂试验临床试验协议:开发和评估机器学习阿片类药物预测和风险分层电子平台 (DEMONSTRATE)

Je-Won J Hong1, Debbie L Wilson2, Khoa Nguyen1

  • 1Department of Pharmacotherapy and Translational Research, College of Pharmacy, University of Florida, Gainesville, FL 32610, USA.

Journal of clinical medicine
|December 11, 2025
PubMed
概括
此摘要是机器生成的。

本研究评估了一种机器学习工具,用于预测阿片类药物过量使用风险,旨在通过临床决策支持改善患者安全并减少伤害. 过量预防警报系统指导初级保健提供者管理高风险患者.

关键词:
临床决策支持 临床决策支持机器学习是机器学习.过量服用阿片类药物过量服用主要护理是一级医疗保健.

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科学领域:

  • 临床信息学 临床信息学
  • 医疗保健中的机器学习
  • 公共卫生干预 公共卫生干预

背景情况:

  • 过量服用阿片类药物仍然是一个重大的公共卫生危机.
  • 临床决策支持 (CDS) 工具可以帮助风险分层.
  • DEMONSTRATE试验研究了一种新的机器学习 (ML) 方法来预测过量风险.

研究的目的:

  • 评估基于ML的CDS工具 (过量预防警报) 的可用性,可接受性,可行性和有效性.
  • 在三个月内识别高风险的阿片类药物过量患者.
  • 为减少阿片类药物相关危害的战略提供信息.

主要方法:

  • 一个单臂,在13个初级保健诊所进行前后实施研究.
  • 混合方法评估,包括定量指标和定性访谈.
  • 专注于年龄≥18岁的患者,最近的阿片类药物处方被ML算法确定为高风险.

主要成果:

  • 通过6个有利的患者结局 (例如,纳洛接入,没有过量事件) 的组合来衡量有效性.
  • 量化指标包括警报透率和采取的临床行动.
  • 通过PCP问卷和采访评估可用性和可接受性.

结论:

  • 该试验将为实施ML驱动的CDS工具提供现实世界的见解.
  • 这些发现将指导未来的策略,以减轻与阿片类药物相关的危害.
  • 这项研究支持将AI整合到针对阿片类药物安全的积极患者护理中.