在健康索赔中检测阿片类药物使用障碍数据与积极的未标记的学习数据
IEEE journal of biomedical and health informatics
|March 3, 2025
概括
精确的阿片类药物使用障碍 (OUD) 检测至关重要. 使用PULSNAR的机器学习揭示了OUD患病率为5.08%,远高于1.35%的诊断,突出显著的不足诊断.
科学领域:
- 医疗信息学 医疗信息学
- 机器学习 机器学习
- 流行病学 流行病学
背景情况:
- 准确估计行为健康状况,如阿片类药物使用障碍 (OUD) 对公共健康至关重要.
- 医疗记录中的不足诊断和不足编码阻碍了真正的流行率估计和风险人群的识别.
- 机器学习 (ML) 提供了OUD预测的潜力,但面临着未被诊断的病例的偏见.
研究的目的:
- 用一种新的机器学习方法估计阿片类药物使用障碍 (OUD) 的真实人口患病率.
- 为应对电子健康记录中低诊断和低编码的挑战.
- 识别那些可能被传统诊断方法遗漏的危险患者.
主要方法:
- 使用积极的未标记的学习选择不是随机的 (PULSNAR),一种积极和未标记的 (PU) 学习技术.
- 应用PULSNAR对3,342,044名商业保险美国患者的大量数据集,至少有一份阿片类药物处方.
- 在2-5年的观察期内,估计了个别患者OUD的概率和整体人口的患病率.
主要成果:
- 普尔斯纳估计,研究人口中的OUD累积患病率为5.08%.
- 这与记录OUD诊断的1.35%的患者形成鲜明对比,这表明诊断不足 (73.5%的病例).
- 估计的患病率与其他流行病学研究的发现一致.
结论:
- 通过计算未被诊断的病例,PULSNAR方法提供了更准确的OUD流行率估计.
- 研究结果强调了实际和诊断的OUD之间的巨大差距,强调了需要改进检测和编码的必要性.
- 这种方法可以加强对需要治疗的个体的识别,并改善对OUD的公共卫生监测.
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