Yu Zengchen, Wang Ke, Wan Zhibo, Xie Shuxuan, Lv Zhihan
College of Computer Science and Technology, Qingdao University, Ningxia Road, Qingdao, 266071 China.
Psychiatric Department, Qingdao Municipal Hospital, Zhuhai Road, Qingdao, 266071 China.
Cluster Comput. 2023;26(2):1231-1251. doi: 10.1007/s10586-022-03707-y. Epub 2022 Sep 13.
Due to its automatic feature learning ability and high performance, deep learning has gradually become the mainstream of artificial intelligence in recent years, playing a role in many fields. Especially in the medical field, the accuracy rate of deep learning even exceeds that of doctors. This paper introduces several deep learning algorithms: Artificial Neural Network (NN), FM-Deep Learning, Convolutional NN and Recurrent NN, and expounds their theory, development history and applications in disease prediction; we analyze the defects in the current disease prediction field and give some current solutions; our paper expounds the two major trends in the future disease prediction and medical field-integrating Digital Twins and promoting precision medicine. This study can better inspire relevant researchers, so that they can use this article to understand related disease prediction algorithms and then make better related research.
由于其自动特征学习能力和高性能，深度学习近年来逐渐成为人工智能的主流，在许多领域发挥着作用。特别是在医学领域，深度学习的准确率甚至超过了医生。本文介绍了几种深度学习算法：人工神经网络（NN）、因子分解机深度学习、卷积神经网络和循环神经网络，并阐述了它们的理论、发展历史以及在疾病预测中的应用；我们分析了当前疾病预测领域存在的缺陷并给出了一些当前的解决方案；本文阐述了未来疾病预测和医学领域的两大趋势——整合数字孪生和推动精准医学。本研究能够更好地启发相关研究人员，使他们能够利用本文了解相关疾病预测算法，进而开展更好的相关研究。
