机器学习用于心理健康:应用,挑战和临床医生的作用
Sorabh Singhal1, Danielle L Cooke2, Ricardo I Villareal2
1Department of Psychiatry, University of Colorado School of Medicine, 1890 N Revere Ct, F546 AHSB, Suite 4100, Rm 4102, Aurora, CO, USA. Sorabh.Singhal@cuanschutz.edu.
Current psychiatry reports
|November 10, 2024
概括
机器学习 (ML) 在精神病学中提供了进步,但在公平和隐私方面面临挑战. 临床医生的参与对于这些新技术的伦理应用和有效的患者护理至关重要.
科学领域:
- 精神病学是一个精神病学.
- 计算机科学 计算机科学
- 生物信息学是一种生物信息学.
背景情况:
- 机器学习 (ML) 涉及对数据的训练算法,以提高任务性能.
- 机器学习在精神病学中的整合正在迅速推进.
研究的目的:
- 评估当前的精神病学应用和机器学习 (ML) 的局限性.
- 通过ML强调临床医生在确保公平有效的患者护理方面的作用.
- 告知心理健康提供者关于临床医生参与ML技术的重要性.
主要方法:
- 对精神病学中ML应用的当前文献的综述.
- 分析挑战和局限性,包括健康公平,隐私和转化到实践.
- 强调临床医生在数据质量,偏差缓解和道德实施方面的重要作用.
主要成果:
- 精神病学中的ML应用通过EHR集成,疾病表型和远程监测取得了进展.
- 仍然存在重大挑战,包括健康公平,数据隐私,实际实施和验证.
- 临床医生对于确保数据质量,减轻偏见,促进透明度和指导道德,以患者为中心的使用至关重要.
结论:
- 临床医生的参与对于解决精神病学中的ML挑战至关重要.
- 伦理应用和以患者为中心的使用是提高ML在心理健康中的有效性的关键.
- 确保公平的护理需要临床医生积极参与ML工具的开发和部署.
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