Deep Learning-Based Method of Diagnosing Hyperlipidemia and Providing Diagnostic Markers Automatically

Liu, Yuliang; Zhang, Quan; Zhao, Geng; Liu, Guohua; Liu, Zhiang

In recent years, with the gradual awakening of global health awareness, human beings have shown an urgent need for further development of the medical level. [1] [2] [3] Artificial intelligence (AI) has great potential to promote the further development of medical diagnostic technology because of its excellent performance in the field of data processing beyond human experts. Despite it has an excellent performance in the field of automatic diagnosis using medical images, the interpretability and text-based medical data analyzability of AI still faces great challenges. 4, 5, 45 In order to solve the problems above, on the global scale, researchers have gradually integrated deep learning technology with a medical diagnosis. Edward Choi and his colleagues used a recurrent neural network to process electronic health records (EHR) for diagnosing heart failure onset. 6 Laila Rasmy et al used recurrent neural networks to predict the risk of heart failure based on a large number of mixed EHR data. 7 Sasank Chilamkurthy et al used natural language processing model to recognize noncontrast head CT scan to identify various head diseases, such as intracranial haemorrhages and cranial fractures et al. 8 Kang Zhang et al used transfer learning algorithm and Google's Inception-V3 model to rapidly diagnose many kinds of diseases of eye and children pulmonary diseases. 9 Michael A. Schwemmer et al used a deep neural network decoding framework to classify intracortical recording, and then controlled the motor to help patients complete corresponding actions, according to the classification results. 10 Although deep learning technology has shown a strong competitive advantage in the field of automatic diagnosis using medical images, it still faces many major challenges, such as processing medical text information. In the actual clinical diagnosis processing, in addition to the diseases that can be diagnosed by medical images, there are many diseases that need to be diagnosed by medical text data, such as hyperlipidemia, diabetes, etc. 11, 12 In order to realize the purpose of automatically diagnosing diseases using text-based medical data, long-short time memory (LSTM) neural network was proposed. 13, 14 The physiological parameters obtained in the clinic are usually a vector rather than image data. Sequential data also play an important role in clinical diagnosis. Convolutional neural network (CNN) is more suitable for processing image data because of its translation invariance. Because of the need to learn the interrelationship between different physiological parameters, LSTM is a good choice when processing sequence data.