在使用机器学习的儿童和青少年中持续使用阿片类药物的预测
Dor Atias1, Aviv Tuttnauer2, Noam Shomron3
1School of Public Health, Faculty of Medical and Health Sciences, Tel Aviv University, Tel Aviv, Israel.
British journal of anaesthesia
|June 11, 2024
概括
机器学习有效预测儿童和青少年持续使用阿片类药物. 这种工具有助于临床医生识别面临风险的青少年,以预防阿片类药物滥用.
科学领域:
- 儿科药理学和数字健康.
- 人工智能在临床决策支持中的应用.
背景情况:
- 儿童群体中阿片类药物的滥用是一个重大问题,但仍未得到充分研究.
- 缺乏有效的工具来识别长期使用阿片类药物风险的儿童和青少年.
研究的目的:
- 开发和验证机器学习分类器,以区分儿科患者中偶尔和持续的阿片类药物使用者.
- 为临床医生创建一个可访问的在线工具,以评估儿科阿片类药物使用风险.
主要方法:
- 利用了29335名儿童患者 (19岁以下) 的数据,记录了阿片类药物购买情况.
- 应用机器学习模型,根据社会人口统计,病史和医疗保健利用数据预测持续使用阿片类药物 (1-3年).
- 使用分类指标,校准,决策曲线分析和SHAP分析评估模型性能.
主要成果:
- 一年预测模型在一个独立的测试组中实现了高性能 (ROC-AUC 0.792,灵敏度 0.772,特异性 0.703).
- SHAP分析确定了关键预测因素,包括诊断数量,医学成像,实验室测试和特定的阿片类型.
- 决策曲线分析证实了预测模型的临床实用性.
结论:
- 开发的机器学习模型显示了预测儿科患者持续使用阿片类药物的强大潜力.
- 在线风险预测工具可以增强临床采用,并支持青少年阿片类药物滥用的二次预防.
- 强调机器学习在识别面临风险的儿科患者群体以进行有针对性的干预方面的价值.
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