医疗与医学对比 组织投诉:机器学习分析揭示了俄罗斯各城市患者评价的不同模式
Irina Evgenievna Kalabikhina1, Anton Vasilyevich Kolotusha1, Vadim Sergeevich Moshkin2
1Population Department, Faculty of Economics, Lomonosov Moscow State University, Moscow 119991, Russia.
Healthcare (Basel, Switzerland)
|October 29, 2025
概括
一个新的机器学习算法将患者评论分类为医疗和组织问题. 与其他城市不同的是,医疗投诉现在在莫斯科占主导地位,突出显示了不断变化的患者不满的趋势.
科学领域:
- 医疗保健质量监测 医疗保健质量监测
- 自然语言处理自然语言处理.
- 医疗保健中的机器学习
背景情况:
- 数字患者反为医疗保健质量评估提供了见解.
- 自动化患者审查分类对于识别不满驱动因素至关重要.
- 这项研究的重点是区分医疗与组织患者的担忧.
研究的目的:
- 开发一种机器学习算法,用于对负面患者评论进行分类.
- 将评论分类为医疗内容 (诊断,治疗,结果) 和组织支持 (物流,成本,通信).
- 分析每个类别在人口统计和地区的流行率.
主要方法:
- 从infodoctor.ru (2012年7月至2023年8月) 编制了18680个一星级患者评论的数据集.
- 使用专家注释用于培训数据.
- 训练了一种后勤回归模型,在分类评论方面达到88.5%的准确性.
主要成果:
- 自2021年以来,莫斯科出现了转变,医疗投诉现在超过了组织投诉.
- 圣彼得堡在顶级专业中有更多的医疗投诉,而其他城市则倾向于组织投诉.
- 在圣彼得堡,审查类型中的性别差异很明显.
结论:
- 开发的算法有效地自动监控患者的反.
- 使医疗管理人员能够区分临床和服务问题.
- 促进有针对性的改进,以提高患者满意度和医疗服务质量.
关键词:
俄罗斯俄罗斯俄罗斯俄罗斯俄罗斯俄罗斯医疗保健质量 医疗保健质量逻辑回归的逻辑回归机器学习是机器学习.自然语言处理自然语言处理.患者的评价 患者的评论患者的满意度 患者的满意度审查对分类进行审查.更多相关视频
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