利用人工智能框架 (条件生成对抗网络) 来增强用于癌症疼痛管理的远程医疗战略
Marco Cascella1, Giuliana Scarpati2, Elena Giovanna Bignami3
1Department of Anesthesia and Critical Care, Istituto Nazionale Tumori-IRCCS, Fondazione Pascale, 80100, Naples, Italy. m.cascella@istitutotumori.na.it.
Journal of anesthesia, analgesia and critical care
|June 29, 2023
概括
生成对抗性网络 (GAN) 可以创建现实的合成患者数据,以改善癌症疼痛管理的远程医疗. 机器学习模型发现年轻患者和突破性疼痛患者需要更多的远程访问.
科学领域:
- 人工智能在医学中的应用
- 数字健康与远程医疗
背景情况:
- 人工智能 (AI) 在医疗保健,特别是远程医疗方面提供了变革性的潜力.
- 本研究探讨了通过远程医疗增强癌症疼痛管理的生成对抗网络 (GAN).
研究的目的:
- 调查条件GAN在生成用于癌症疼痛管理的合成患者数据中的实用性.
- 确定与癌症疼痛护理中远程医疗利用增加相关的因素.
主要方法:
- 使用条件生成对抗网络 (GAN) 来创建合成患者数据.
- 利用了226名患者和489次远程医疗访问的人口统计和临床数据.
- 四个机器学习算法评估了与更高数量的远程访问相关的变量.
主要成果:
- 合成数据集准确地反映了真实数据在各种患者和临床因素中的分布.
- 随机森林在预测远程医疗访问增加方面实现了0.8准确度.
- 年轻患者 (<45岁) 和突破性癌症疼痛患者预计需要更多的远程评估.
结论:
- 像GAN这样的AI技术对于通过解决远程医疗的知识差距来推进医疗保健至关重要.
- GAN可以加速将远程医疗整合到临床实践中,用于诸如癌症疼痛等疾病.
- 仔细考虑人工智能方法的局限性对于成功实施至关重要.
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