一个基于人工智能的智能手机应用程序用于评估使用合成数据在劳动人口中滥用阿片类药物的风险:试点开发研究研究
A B M Rezbaul Islam1, Khalid M Khan2, Amanda Scarbrough2
1Department of Computer Science, Sam Houston State University, Huntsville, TX, United States.
JMIR formative research
|May 30, 2023
概括
这项研究开发了一个智能手机应用程序,使用机器学习来识别高风险职业中阿片类药物使用障碍 (OUD) 的风险因素. 该应用程序使用合成数据成功预测了OUD风险,为预防和缓解提供了有前途的工具.
科学领域:
- 移动健康 (mHealth) 服务提供商
- 医疗保健中的人工智能 (AI)
- 职业健康 职业健康 职业健康
背景情况:
- 阿片类药物使用障碍 (OUD) 是美国的一个重大危机,影响超过1000万个人.
- 运输,建筑和医疗保健等体力苛刻的行业有高比例的OUD.
- OUD导致成本增加,缺勤率增加,劳动力生产率降低.
研究的目的:
- 开发一个智能手机应用程序来跟踪与OUD相关的工作相关的风险因素.
- 专注于易患OUD的高风险职业群体.
- 利用移动卫生工具进行临床以外的干预.
主要方法:
- 通过文献审查和风险评估问题的专家小组评估开发了一个智能手机应用程序.
- 使用合成数据进行培训和测试,避免使用人类参与者的数据.
- 采用一个纯粹的贝叶斯机器学习算法来预测OUD风险.
主要成果:
- 开发的智能手机应用程序是功能性的,并使用合成数据成功预测了OUD风险.
- 天真贝叶斯算法在风险预测方面表现出有效性.
- 该研究为未来使用人类参与者数据进行测试建立了平台.
结论:
- 移动健康应用程序显示出疾病预测和预防的前景,包括OUD.
- 该应用程序通过REST API和云加密来确保用户的隐私和准确性.
- 为高风险劳动力提供量身定制的缓解策略,可能有助于减少阿片类药物危机.
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