通过可解释的机器学习揭示 2 型糖尿病的诊断信息

IF 8.1 1区 计算机科学 0 COMPUTER SCIENCE, INFORMATION SYSTEMS Information Sciences Pub Date : 2024-10-22 DOI:10.1016/j.ins.2024.121582
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引用次数: 0

摘要

疾病预测模型的可解释性往往对其在医疗从业者中的可信度和可用性至关重要。现有的可解释人工智能方法提高了模型的透明度,但在识别精确的特定疾病原始信息方面存在不足。在这项工作中,开发了一种基于可解释深度学习的算法--数据空间地标提炼器,它不仅增强了全局可解释性和局部可解释性,还揭示了数据分布的内在信息。利用所提出的方法,以两家医院的电子病历为基础,构建了具有高可解释性的 2 型糖尿病诊断模型。此外,有效的诊断信息直接来自模型的内部参数,与当前的临床知识非常吻合。与传统的可解释机器学习方法相比,所提出的方法提供了更精确、更具体的可解释性,提高了临床医师对机器学习支持的诊断模型的信任度。
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Unveiling diagnostic information for type 2 diabetes through interpretable machine learning
The interpretability of disease prediction models is often crucial for their trustworthiness and usability among medical practitioners. Existing methods in interpretable artificial intelligence improve model transparency but fall short in identifying precise, disease-specific primal information. In this work, an interpretable deep learning-based algorithm called the data space landmark refiner was developed, which not only enhances both global interpretability and local interpretability but also reveals the intrinsic information of the data distribution. Using the proposed method, a type 2 diabetes mellitus diagnostic model with high interpretability was constructed on the basis of the electronic health records from two hospitals. Moreover, effective diagnostic information was directly derived from the model’s internal parameters, demonstrating strong alignment with current clinical knowledge. Compared with conventional interpretable machine learning approaches, the proposed method offered more precise and specific interpretability, increasing clinical practitioners’ trust in machine learning-supported diagnostic models.
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来源期刊
Information Sciences
Information Sciences 工程技术-计算机:信息系统
CiteScore
14.00
自引率
17.30%
发文量
1322
审稿时长
10.4 months
期刊介绍: Informatics and Computer Science Intelligent Systems Applications is an esteemed international journal that focuses on publishing original and creative research findings in the field of information sciences. We also feature a limited number of timely tutorial and surveying contributions. Our journal aims to cater to a diverse audience, including researchers, developers, managers, strategic planners, graduate students, and anyone interested in staying up-to-date with cutting-edge research in information science, knowledge engineering, and intelligent systems. While readers are expected to share a common interest in information science, they come from varying backgrounds such as engineering, mathematics, statistics, physics, computer science, cell biology, molecular biology, management science, cognitive science, neurobiology, behavioral sciences, and biochemistry.
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