使用医疗热线和二次护理的国家数据构建多种癌症风险队列
Hadi Modarres1, Dimitris Pipinis2, Divya Balasubramanian3
1NHS England, Data Science and Applied AI team, London, England, UK. hadi.modarres@nhs.net.
NPJ digital medicine
|August 27, 2025
概括
使用NHS 111呼叫数据识别高风险的癌症患者群可以改善早期诊断. 这项研究强调了医疗保健辅助信息在预测未来的癌症诊断和改善患者治疗结果方面的潜力.
科学领域:
- 癌症学
- 医疗信息学
- 公共卫生
背景情况:
- 早期诊断癌症可以显著改善患者的治疗结果.
- 选择了9个晚期诊断率高的癌症地点,没有国家查计划.
- 通过识别有风险的患者群体,可以加强现有的诊断途径.
研究的目的:
- 开发预测模型以识别患癌症风险较高的人.
- 利用医疗热线和二次护理数据进行癌症风险预测.
- 探索国家卫生服务 (NHS) 111呼叫数据在癌症诊断中的有用性.
主要方法:
- 使用来自英格兰111医疗热线和二次护理预约的数据.
- 根据诊断和生存率标准选择了9种癌症类型.
- 使用来自呼叫数据的特征重要性开发的预测模型.
主要成果:
- 在预测未来的癌症诊断方面,
- 预测模型的差异范围为卵巢癌的0. 69到食道癌的0. 83.
- 提出了一个构建高风险癌症队列的方法,考虑特征的重要性和潜在偏差.
结论:
- 对于早期癌症检测而言,NHS 111呼叫数据是一个宝贵的资源.
- 开发的方法提供了一种灵活的癌症风险分层方法.
- 这一策略可以帮助为症状和无症状患者量身定制干预措施.
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