应用自然语言处理来预测巴西医疗技术评估报告的最终建议
Marilia Mastrocolla de Almeida Cardoso1,2, Juliana Machado-Rugolo1,2, Lehana Thabane3,4,5
1Health Technology Assessment Unit, Hospital das Clínicas da Faculdade de Medicina de Botucatu, Botucatu, Brazil.
概括
自然语言处理 (NLP) 模型可以预测健康技术评估 (HTA) 的结果. 这种方法可以通过分析报告中的语言标记来提高HTA的效率,帮助医疗保健决策.
科学领域:
- 医疗技术评估 (HTA) 是指对医疗技术进行评估.
- 自然语言处理 (NLP) 是一种自然语言处理.
- 医疗保健中的机器学习
背景情况:
- 卫生技术评估 (HTA) 对全球医疗保健决策至关重要.
- 鉴定影响HTA结果的关键因素是必不可少的,因为机构的工作量很高.
- 本研究的重点是预测巴西的HTA评估结果.
研究的目的:
- 预测巴西卫生技术整合委员会 (CONITEC) 对卫生技术评估的批准状态.
- 为了利用自然语言处理 (NLP) 来分析HTA报告摘要.
- 识别与评估结果相关的语言模式.
主要方法:
- 使用了CONITEC官方报告摘要,从2012年到2022年.
- 应用NLP技术,包括用于文本数据分析的标记化.
- 评估的机器学习模型:物流回归,SVM,随机森林,神经网络和XGBoost.
- 采用k-模式集群,将评估分类为批准和拒绝的组.
主要成果:
- 神经网络模型实现了最高的性能指标 (精度为0.769,ROC AUC为0.871).
- 确定了语言标记,例如引用国际HTA机构和政府的要求.
- 批准的评估主要涉及政府发起的药物评估,而被拒绝的评估通常涉及行业要求的药物评估.
结论:
- NLP模型可以有效地预测健康技术整合的结果.
- 这种预测能力可以通过提供初步见解来提高HTA系统的效率.
- 未来的研究可以将这种方法扩展到其他国际机构的HTA报告.
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