用于可解释和高效医学时间序列处理的稀疏学习核

IF 18.8 1区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Sully F. Chen, Zhicheng Guo, Cheng Ding, Xiao Hu, Cynthia Rudin
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引用次数: 0

摘要

对医学时间序列信号进行快速、可靠和准确的解读,对于事关重大的临床决策至关重要。深度学习方法为医学信号处理提供了前所未有的性能,但也付出了代价:计算密集且缺乏可解释性。我们提出了稀疏混合学习核(SMoLK),这是一种用于医学时间序列处理的可解释架构。SMoLK 学习一组轻量级的灵活内核,形成单层稀疏神经网络,不仅提供可解释性,还提供效率、鲁棒性和对未知数据分布的泛化。我们引入了参数缩减技术,以缩小 SMoLK 网络的规模并保持性能。我们在许多消费类可穿戴设备常见的两个重要任务上测试了 SMoLK:光电血压计伪影检测和单导联心电图的心房颤动检测。我们发现,SMoLK 的性能可媲美更大数量级的模型。它特别适用于使用低功耗设备的实时应用,其可解释性有利于高风险情况。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Sparse learned kernels for interpretable and efficient medical time series processing

Sparse learned kernels for interpretable and efficient medical time series processing

Sparse learned kernels for interpretable and efficient medical time series processing
Rapid, reliable and accurate interpretation of medical time series signals is crucial for high-stakes clinical decision-making. Deep learning methods offered unprecedented performance in medical signal processing but at a cost: they were compute intensive and lacked interpretability. We propose sparse mixture of learned kernels (SMoLK), an interpretable architecture for medical time series processing. SMoLK learns a set of lightweight flexible kernels that form a single-layer sparse neural network, providing not only interpretability but also efficiency, robustness and generalization to unseen data distributions. We introduce parameter reduction techniques to reduce the size of SMoLK networks and maintain performance. We test SMoLK on two important tasks common to many consumer wearables: photoplethysmography artefact detection and atrial fibrillation detection from single-lead electrocardiograms. We find that SMoLK matches the performance of models orders of magnitude larger. It is particularly suited for real-time applications using low-power devices, and its interpretability benefits high-stakes situations. Deep learning excels in medical signal processing but lacks interpretability. An efficient, interpretable architecture that matches the performance of larger models at orders of magnitude fewer parameters in tasks common to wearable devices has been proposed.
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来源期刊
CiteScore
36.90
自引率
2.10%
发文量
127
期刊介绍: Nature Machine Intelligence is a distinguished publication that presents original research and reviews on various topics in machine learning, robotics, and AI. Our focus extends beyond these fields, exploring their profound impact on other scientific disciplines, as well as societal and industrial aspects. We recognize limitless possibilities wherein machine intelligence can augment human capabilities and knowledge in domains like scientific exploration, healthcare, medical diagnostics, and the creation of safe and sustainable cities, transportation, and agriculture. Simultaneously, we acknowledge the emergence of ethical, social, and legal concerns due to the rapid pace of advancements. To foster interdisciplinary discussions on these far-reaching implications, Nature Machine Intelligence serves as a platform for dialogue facilitated through Comments, News Features, News & Views articles, and Correspondence. Our goal is to encourage a comprehensive examination of these subjects. Similar to all Nature-branded journals, Nature Machine Intelligence operates under the guidance of a team of skilled editors. We adhere to a fair and rigorous peer-review process, ensuring high standards of copy-editing and production, swift publication, and editorial independence.
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