基于传统方法和机器学习的临床模型预测首次脑卒中:现状与前景

Clinical Prediction Models Based on Traditional Methods and Machine Learning for Predicting First Stroke: Status and Prospects

  • 摘要: 脑卒中是全球第3大致死疾病和第4大致残疾病,其较高的致残率和漫长的康复期不仅严重影响患者生存质量,还给家庭和社会带来巨大负担。一级预防是卒中防控的核心,通过早期干预危险因素可有效降低其发病率,因此脑卒中首发风险预测模型的构建具有重要临床价值。近年来,大数据与人工智能技术的发展为脑卒中风险的预测开辟了新路径。本文综述了传统方法与机器学习模型在脑卒中首发风险预测中的研究现状,并从3个方面展望了其未来发展趋势:首先,应注重技术创新,通过引入深度学习、大模型等先进算法,进一步提升预测模型的精确度;其次,需丰富数据类型和优化模型架构,以构建更加全面且精准的预测模型;最后,尤其强调模型在真实世界中的临床验证,一方面可增强模型的鲁棒性和普适性,另一方面可促进医生对预测模型的理解,这对预测模型的应用与推广至关重要。

     

    Abstract: Stroke ranks as the third leading cause of death and the fourth leading cause of disability worldwide. Its high disability rate and prolonged recovery period not only severely impact patients' quality of life but also impose a significant burden on families and society. Primary prevention is the cornerstone of stroke control, as early intervention on risk factors can effectively reduce its incidence. Therefore, the development of predictive models for first-ever stroke risk holds substantial clinical value. In recent years, advancements in big data and artificial intelligence technologies have opened new avenues for stroke risk prediction. This article reviews the current research status of traditional methods and machine learning models in predicting firstever stroke risk and outlines future development trends from three perspectives: First, emphasis should be placed on technological innovation by incorporating advanced algorithms such as deep learning and large models to further enhance the accuracy of predictive models. Second, there is a need to diversify data types and optimize model architectures to construct more comprehensive and precise predictive models. Lastly, particular attention should be given to the clinical validation of models in real-world settings. This not only enhances the robustness and generalizability of the models but also promotes physicians' understanding of predictive models, which is crucial for their application and dissemination.

     

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