CardioAI:一种基于人工智能的多式系统,用于支持症状监测和预测癌症治疗引起的心脏毒性风险
Siyi Wu1, Weidan Cao2, Shihan Fu1
1Northeastern University, Boston, Massachusetts, USA.
概括
癌症治疗可能会导致心脏损伤 (心脏毒性),这很难在早期发现. 一个新的AI系统CardioAI使用可穿戴设备和语音助理的患者数据来帮助临床医生管理这种风险.
科学领域:
- 心脏病学 心脏病学
- 在瘤学瘤学.
- 人工智能的人工智能
背景情况:
- 癌症治疗虽然可以延长生命,但存在治疗引起的心脏毒性的重大风险.
- 由于微妙的症状和高临床工作量,早期发现心脏毒性是具有挑战性的.
- 现有的临床工作流程缺乏针对主动心脏毒性管理的专用资源.
研究的目的:
- 解决治疗诱导心脏毒性的管理方面的挑战.
- 为临床医生开发基于人工智能的决策支持系统.
- 整合患者生成的数据,以提高心脏毒性风险评估.
主要方法:
- 与11名临床医生一起进行参与式设计研究,以了解决策过程.
- 开发一种多式人工智能系统 (CardioAI).
- 从可穿戴设备和语音助理集成数据.
- 小规模的启发性评估与四名专家.
主要成果:
- 临床医生反为AI系统的设计提供了信息.
- 建议使用 CardioAI 作为用于心脏毒性风险建模的多式联络系统.
- 该系统旨在通过提供风险见解来支持临床决策.
结论:
- 拟议的 CardioAI 系统为管理治疗诱导的心脏毒性提供了一种新的方法.
- 从可穿戴设备和语音助理的多式联机数据集成显示出有希望的结果.
- 讨论了系统设计和实施的未来考虑因素.
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