在西班牙一个地区的精神分裂患者再入院的预测建模,结合粒子群优化和机器学习算法
Susel Góngora Alonso1, Isabel Herrera Montano1, Isabel De la Torre Díez1
1Department of Signal Theory and Communications, and Telematics Engineering, University of Valladolid, Paseo de Belén, 15, 47011 Valladolid, Spain.
Biomimetics (Basel, Switzerland)
|December 27, 2024
概括
这项研究开发了对精神分裂症患者再入院风险的预测模型. 随机森林算法与粒子群优化 (PSO) 显示出高精度,有助于患者护理和降低医疗保健成本.
科学领域:
- 医疗信息学 医疗信息学
- 计算精神病学是一种计算精神病学.
- 医疗保健服务研究 医疗服务研究
背景情况:
- 再入院是护理质量和患者结果的关键指标.
- 高回收率增加了医疗保健成本,并对患者的生活质量产生了负面影响.
- 医院再入院的预测模型可以指导治疗选择和预防策略.
研究的目的:
- 开发用于诊断为精神分裂症的患者再入院风险的预测模型.
- 通过将粒子群优化 (PSO) 算法与机器学习分类算法相结合来提高预测准确性.
主要方法:
- 利用了来自西班牙11家公立医院 (2005-2015) 的6089名精神分裂症患者再入院记录的数据库.
- 采用机器学习分类算法与粒子群优化 (PSO) 算法集成.
- 使用AUC,回忆,精度和F1分数等指标评估模型性能.
主要成果:
- 随机森林算法与PSO相结合,表现出卓越的性能.
- 实现曲线下的面积 (AUC) 为0.860.
- 报告召回率为0.959,准确率为0.844,F1得分为0.907.
结论:
- 开发的预测模型为改善精神分裂症患者护理做出了重大贡献.
- 这些模型可以促进实施有针对性的预防措施.
- 该研究强调了通过改进再接收风险预测来降低医疗保健系统成本的潜力.
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