可解释性增强机器学习模型用于用心理测试报告对智力障碍和注意力缺陷/多动障碍进行分类
Tong Min Kim1, Young-Hoon Kim2, Sung-Hee Song3
1Department of Medical Informatics, College of Medicine, The Catholic University of Korea, Seoul, Korea.
Journal of Korean medical science
|March 25, 2025
概括
可解释的AI模型从心理报告中准确地分类智力障碍 (ID) 和注意力缺陷/多动症障碍 (ADHD),改善医生诊断洞察力.
科学领域:
- 人工智能在医学中的应用
- 自然语言处理 (NLP) 是一种自然语言处理.
背景情况:
- 心理测试报告对于诊断智力障碍 (ID) 和注意力缺陷/多动症障碍 (ADHD) 至关重要.
- 当前的报告往往是无结构的,主观的,容易出现人为错误,导致诊断挑战.
- 医生可能不会审查完整的报告,报告量往往跟踪诊断需求.
研究的目的:
- 从患者报告中开发可解释的预测模型来对ID和ADHD进行分类.
- 用NLP和AI来解决传统报告分析的局限性.
主要方法:
- 利用NLP分析了1,475份患者报告,包括医生诊断.
- 在特征选择中采用SHAP和变量的重要性,确保模型可解释性.
- 开发了一个基于n-gram特征的搜索系统,从模型输出中重建人类可读的文本.
主要成果:
- 在对ID和ADHD进行分类时,实现了0.92的最大模型准确度.
- 从四个不同的模型中成功恢复了80个人类可读的文本.
- 即使报告数据有限,也证明了准确的分类.
结论:
- 开发的模型准确地分类ID和ADHD,提供可解释的预测.
- 增强的模型可解释性有助于医生理解并支持基于证据的决策.
- 这种方法提供了一个可扩展的解决方案,以提高诊断效率和准确性.
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