使用机器学习模型预测内部医学的门诊预约没有出现
Felipe Ocampo Osorio1,2,3,4, Santiago Pedroza Gomez1,2, David Esteban Rebellón Sanchez1,2
1Unidad de Inteligencia Artificial, Fundación Valle del Lili, Cali, Valle del Cauca, Colombia.
PeerJ. Computer science
|June 26, 2025
概括
机器学习模型可以预测患者在内科没有出现,识别高风险个体. 这通过优化预约安排和资源分配来提高医疗保健效率和患者护理.
科学领域:
- 医疗保健信息学 医疗保健信息学
- 机器学习在医学中的应用
- 临床操作 临床操作
背景情况:
- 患者在医疗预约中缺席导致医疗保健提供显著延迟和运营效率低下.
- 解决没有出现的情况在内部医学中至关重要,该医学管理复杂的慢性疾病.
研究的目的:
- 开发和评估机器学习模型,用于预测内部医学部门的患者缺勤风险.
- 确定影响医疗预约不出现的关键因素.
主要方法:
- 对机构数据的统计分析,以确定缺勤预测因素.
- 评估七个机器学习模型,包括数据处理和类失衡技术 (SMOTE).
- 使用Bagging RandomForest进行超参数优化和模型选择,并使用SHAP进行解释性.
主要成果:
- 用SMOTE进行优化和平衡的Bagging RandomForest模型实现了84.80%的预测准确度.
- 关键预测因素包括以前的缺席,预约时间和诊断的疾病.
- SHAP分析提供了可解释性,突出了有影响力的变量.
结论:
- 机器学习有效地预测内部医学患者缺勤风险.
- 这种方法可以进行主动干预,优化资源配置,提高护理质量.
- 该方法支持减少运营效率低下和改善患者的治疗结果.
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