人工智能在脑卒中疾病预测研究中的应用

Application of Artificial Intelligence in Stroke Prediction Research

摘要: 基于人工智能(AI)的决策支持系统可以让医生快速评估脑卒中患者数据，并在初始阶段更准确地预测脑卒中可能引发的危重疾病。为了提高脑卒中疾病预测的准确性，多种基于人工智能的决策支持系统被提出，本文中，我们首先对脑卒中分类、症状及危险因素等情况进行了简单介绍，然后对人工智能在脑卒中疾病预测研究中的应用做了详细的说明，同时通过比较不同机器学习方法的优劣性，并列举国内外脑卒中预测的具体研究案例，对各种基于人工智能的决策支持系统进行了批判性分析，为预测脑卒中提供了相应的研究方法。本文进一步扩展讨论了深度学习方法(CNN、RNN/LSTM、GNN)的应用，并提出了模型可解释性(XAI)和隐私保护计算(联邦学习)在临床转化中的关键意义。

Abstract: AI-based decision support systems enable doctors to quickly evaluate stroke patient data and more accurately predict critical illnesses that may be caused by stroke in the initial stage. To improve the accuracy of stroke prediction, a variety of AI-based decision support systems have been proposed. In this paper, we first briefly introduce the classification, symptoms, and risk factors of stroke, then elaborate on the application of AI in stroke prediction research. Meanwhile, by comparing the advantages and disadvantages of different machine learning methods and listing specific domestic and international research cases on stroke prediction, we conduct a critical analysis of various AI-based decision support systems and provide corresponding research methods for stroke prediction. This paper further expands the discussion on the application of deep learning methods (CNN, RNN/LSTM, GNN) and points out the key significance of model interpretability (XAI) and privacy-preserving computing (federated learning) in clinical translation.