使用自动化文本分类来探索NICE对罕见疾病药物的评估中的不确定性
Lea Wiedmann1, Jack Blumenau2, Orlagh Carroll1
1Department of Health Services Research and Policy, Faculty of Public Health and Policy, London School of Hygiene & Tropical Medicine, UK.
International journal of technology assessment in health care
|January 5, 2024
概括
监督学习模型可以对罕见疾病治疗评估中的不确定性进行分类,但结果因模型而异. 这种方法可用于分析医疗技术评估.
科学领域:
- 卫生技术评估 卫生技术评估
- 药物经济学 药物经济学
- 医疗保健中的机器学习
背景情况:
- 罕见疾病治疗 (RDTs) 需要严格的评估.
- 识别证据中的不确定性对于RTD决策至关重要.
- 自动化文本分类可以帮助分析大量的评估文件.
研究的目的:
- 评估监督学习的可行性和有效性,用于RTD评估的文本分类.
- 确定R&DT评估的证据基础中的不确定性.
- 分析基于文本分类结果的评估差异.
主要方法:
- 分析了94个国家卫生和护理卓越研究所 (NICE) 对RDTs的评估 (2011年1月至2023年5月).
- 实现对二进制文本分类的天真贝叶斯,拉索和支持向量机模型 (不确定性与无不确定性).
- 测试假设与评估指南,先进治疗药物 (ATMP) 状态,疾病区域和年龄组相关.
主要成果:
- 拉索模型在分类不确定性方面取得了83.6%的准确性.
- 与技术评估 (TA) 指导相比,在高度专业化的技术 (HST) 评估中,不确定性明显更为普遍.
- 在不确定性和ATMP状态,非瘤学RDTs或儿科/成人适应症之间没有发现显著的关联.
结论:
- 监督学习模型在NICE RDT评估中对文本分类是可行的.
- 选择的分类模型显著影响下游分析结果.
- 可能需要进一步的研究来优化模型选择,以便进行一致和可靠的分析.
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