通过捕捉疾病严重程度,相互作用和进展来增强个性化医疗保健
Yanchao Tan1, Zihao Zhou1, Leisheng Yu2
1College of Computer and Data Science, Fuzhou University, Fuzhou, China.
概括
这项研究引入了一种新的AI方法,用于使用电子健康记录 (EHR) 进行个性化诊断预测. 该方法通过考虑个体患者的疾病严重程度,相互作用和进展来提高准确性.
科学领域:
- 医疗保健中的人工智能
- 医疗信息学 医疗信息学
- 计算健康 计算健康
背景情况:
- 从电子健康记录 (EHR) 进行个性化诊断预测至关重要,但具有挑战性.
- 现有的AI模型往往忽视了疾病的异质性,包括不同严重程度,复杂的相互作用和患者的动态进展.
研究的目的:
- 为个性化诊断预测开发人工智能模型,以考虑疾病严重程度,相互作用和进展.
- 通过解决当前基于EHR的AI方法的局限性,提高诊断预测的准确性.
主要方法:
- 基于严重程度的嵌入,用于个性化疾病表示.
- 基于超图的聚合以捕捉在访问层面上更高阶的疾病相互作用.
- 神经普通微分方程用于在患者层面上建模连续时间疾病进展.
主要成果:
- 与最先进的方法相比,诊断预测的显著性能提高.
- 在两个真实世界EHR数据集上,平均准确度增加了10.70%.
- 提出的方法有效地捕捉了疾病的严重程度,复杂的相互作用和动态进展.
结论:
- 开发的AI模型提供了一种更个性化,更准确的方法,使用EHR数据进行诊断预测.
- 解决疾病异质性是推动人工智能在个性化医疗保健中的应用的关键.
- 这项工作为利用复杂的EHR数据提供了一个强大的框架,以改善患者的治疗结果.
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