使用药房查询数据库开发机器学习模型,用于预测高等教学医院的剂量相关查询
Jungwon Cho1, Ah Ra Lee2, Dongjun Koo3
1College of Pharmacy & Research Institute of Pharmaceutical Sciences, Seoul National University, Seoul, Republic of Korea; Department of Pharmacy, Seoul National University Bundang Hospital, Gyeonggi-do, Republic of Korea.
International journal of medical informatics
|March 7, 2024
概括
机器学习模型可以使用药房调查数据预测与剂量相关的药物问题. 这项技术有助于药剂师识别潜在的药物错误,并提高患者的安全.
科学领域:
- 药理学 药理学是指药理学的学科.
- 医疗信息学 医疗信息学
- 机器学习 机器学习
背景情况:
- 与毒品相关的问题 (DRP) 构成严重的医疗保健风险.
- 药剂师在识别和解决DRP方面至关重要,以提高患者安全.
- 建立了一项药房调查计划,以记录医生订单审查并防止潜在的DRPs.
研究的目的:
- 开发机器学习模型来预测与剂量相关的查询.
- 使用药房查询数据库,包括处方和患者数据.
主要方法:
- 对20393个药房查询的回顾性分析 (2018年1月至2023年2月).
- 数据包括处方细节,患者人口统计和功能.
- 开发和评估了六种机器学习模型,使用ROC AUC和精度.
主要成果:
- CatBoost 模型表现出卓越的性能 (精度:0.79,灵敏度:0.92).
- 关键的预测特征包括药物成分,单位和功能.
- 较低的功能和特定药物 (如甲福明) 与剂量相关的调查有关.
结论:
- 机器学习模型有效地从药房数据中预测与剂量相关的查询.
- 对于更广泛的医疗保健应用,需要外部验证.
- 这些发现支持将机器学习纳入药剂师的临床决策.
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