使用机器学习预测儿科患者手术后出院时间.
Marco Cascella1, Cosimo Guerra1, Atanas G Atanasov2,3,4
1Anesthesia and Pain Medicine, Department of Medicine, Surgery and Dentistry "Scuola Medica Salernitana", University of Salerno, Baronissi, 84081, Italy.
Translational medicine @ UniSa
|March 28, 2025
概括
机器学习模型可以准确预测桃体切除术后儿童出院时间,识别术后恶心和吐 (PONV) 等关键因素,以改善医院资源管理.
科学领域:
- 儿科手术 儿科手术
- 医疗信息学 医疗信息学
- 医疗保健中的机器学习
背景情况:
- 儿童桃体切除和腺切除术后的长时间住院增加了成本,并影响了患者的护理.
- 确定延长住院时间的决定因素对于优化术后管理和资源配置至关重要.
研究的目的:
- 应用机器学习 (ML) 来预测儿科患者手术后出院时间.
- 确定影响桃体切除和/或腺切除后住院时间的关键变量.
主要方法:
- 分析了423名接受桃体切除/桃体切除手术的儿科患者的数据.
- 数据预处理包括处理缺失值,异常值检测和变量转换.
- 四个ML算法 (随机森林,物流回归,RUSBoost,AdaBoost) 被训练并使用10倍交叉验证进行评估.
主要成果:
- 手术后的恶心和吐 (PONV),德克斯梅德托米丁输液,芬太尼使用,排泄疼痛和输出时间是延迟排泄的重要预测因素.
- 在预测放电时间方面,AdaBoost模型实现了高准确性和可靠性.
结论:
- ML模型有效地预测儿科出院时间,并确定长时间住院的关键因素.
- 这些预测性见解可以改进儿科手术中术后护理策略和资源管理.
- 将ML模型集成到临床实践中可以支持实时决策.
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