通过过程挖掘和深度学习的协同作用改善心脏病-再住院预测:一种创新的方法
Eleonora Spizzi1,2, Damiano Quadraro2, Federico Esposti2
1Politecnico di Milano, Italy.
Studies in health technology and informatics
|October 23, 2023
概括
医院现在可以使用一种新的过程挖掘-深度学习模型在六个月内预测患者的再入院情况. 这种方法有助于资源规划,并识别高风险再入院的心脏病患者.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的人工智能
- 心脏病学 心脏病学
背景情况:
- 重新住院造成重大财务负担,并影响医院护理质量评估.
- 准确预测患者再入院是医疗保健提供者的关键需求.
- 心脏病患者是重收预测至关重要的关键人群.
研究的目的:
- 开发一种创新的模型,用于预测心脏病患者的6个月再入院情况.
- 利用过程挖掘和深度学习来提高预测准确度.
- 支持医院资源规划和识别有重新入院风险的患者.
主要方法:
- 使用的过程挖掘技术来分析患者的历史数据.
- 开发了一个深度学习模型,与处理采矿输出集成.
- 通过使用圣拉法利医院心脏病专业的患者记录来训练和验证该模型.
主要成果:
- 过程挖掘-深度学习模型证明了准确预测6个月再入院的情况.
- 该模型有效地确定了导致心脏病患者再入院的关键因素.
- 获得的洞察力可以为处于风险的个人提供有针对性的干预措施.
结论:
- 开发的模型为预测心脏病患者再入院提供了一个强大的工具.
- 这种预测能力可以优化医院资源配置,改善患者护理管理.
- 进一步的研究可以探索不同医学专业的更广泛的应用.
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