结核病治疗放弃预测的综合人工智能框架:一个多范式的方法
Frederico Guilherme Santana Da Silva Filho1, Igor Wenner Silva Falcão1, Tobias Moraes de Souza2
1Institute of Technology, Federal University of Pará, Belém 66075-110, PA, Brazil.
Journal of clinical medicine
|December 30, 2025
概括
一个集成的人工智能框架准确地识别了需要额外支持的结核病患者,改善了治疗坚持和减少药物耐药性. 这种人工智能模型平衡了预测能力和临床透明度,以获得更好的患者结果.
科学领域:
- 人工智能在医学中的应用
- 公共卫生信息学 公共卫生信息学
- 机器学习用于疾病管理管理
背景情况:
- 治疗坚持问题影响全球10-20%的结核病患者,助长药物耐药性和传播.
- 现有的人工智能 (AI) 模型往往缺乏临床使用所需的可解释性.
- 需要人工智能系统来识别需要增强支持的结核病患者,同时保持透明度.
研究的目的:
- 开发和验证一个综合的人工智能框架,用于识别可以从增强治疗支持中受益的结核病患者.
- 结合传统的机器学习,可解释的AI,深度强化学习和自然语言处理.
- 确保人工智能驱动的患者识别中的临床透明度和可解释性.
主要方法:
- 从圣保罗州监测数据 (2006-2016) 分析了103,846例肺结核病例.
- 开发一个集成的AI框架,使用传统的ML,可解释的AI,深度强化学习和NLP.
- 模型评估使用精度,回忆,F1得分和AUC-ROC,重点是保持可解释性得分高于0.90.
主要成果:
- 可解释的人工智能实现了与传统ML (F1分数:0.77) 相似的性能,具有最大的可解释性 (分数:0.95).
- 综合人工智能整体显著优于单个方法 (F1得分:0.82),显示6.5%的改善 (p < 0.001).
- 需要支持的关键预测因素包括物质使用障碍和艾滋病毒共感染,而不是人口统计数据.
结论:
- 开发的多范式人工智能系统提供了一种强大的方法来识别需要增强支持的结核病患者.
- 人工智能方法实现了高预测准确度,同时保持了完全的临床透明度.
- 这项研究表明,医疗人工智能的准确性-解释性权衡可以通过综合方法来解决.
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