{"title":"用于可解释和高效医学时间序列处理的稀疏学习核","authors":"Sully F. Chen, Zhicheng Guo, Cheng Ding, Xiao Hu, Cynthia Rudin","doi":"10.1038/s42256-024-00898-4","DOIUrl":null,"url":null,"abstract":"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.","PeriodicalId":48533,"journal":{"name":"Nature Machine Intelligence","volume":"6 10","pages":"1132-1144"},"PeriodicalIF":18.8000,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Sparse learned kernels for interpretable and efficient medical time series processing\",\"authors\":\"Sully F. Chen, Zhicheng Guo, Cheng Ding, Xiao Hu, Cynthia Rudin\",\"doi\":\"10.1038/s42256-024-00898-4\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"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.\",\"PeriodicalId\":48533,\"journal\":{\"name\":\"Nature Machine Intelligence\",\"volume\":\"6 10\",\"pages\":\"1132-1144\"},\"PeriodicalIF\":18.8000,\"publicationDate\":\"2024-09-18\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Nature Machine Intelligence\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://www.nature.com/articles/s42256-024-00898-4\",\"RegionNum\":1,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Nature Machine Intelligence","FirstCategoryId":"94","ListUrlMain":"https://www.nature.com/articles/s42256-024-00898-4","RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
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.
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.
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