<p>Deep Learning-Based Method of Diagnosing Hyperlipidemia and Providing Diagnostic Markers Automatically</p>

Abstract

Introduction: The research of auxiliary diagnosis has always been one of the hotspots in the world. The implementation of auxiliary diagnosis support algorithm for medical text data faces challenges with interpretability and creditability. The improvement of clinical diagnostic techniques means not only the improvement of diagnostic accuracy but also the further study of diagnostic basis. Traditional research methods for diagnostic markers often require a large amount of time and economic costs. Research objects are often dozens of samples, and it is, therefore, difficult to synthesize large amounts of data. Therefore, the comprehensiveness and reliability of traditional methods have yet to be improved. Therefore, the establishment of a model that can automatically diagnose diseases and automatically provide a diagnostic basis at the same time has a positive effect on the improvement of medical diagnostic techniques. Methods: Here, we established an auxiliary diagnostic tool based on attention deep learning algorithm to diagnostic hyperlipemia and automatically predict the corresponding diagnostic markers using hematological parameters. In this paper, we not only demonstrated the ability of the proposed model to automatically diagnose diseases using text-based medical data, such as physiological parameters, but also demonstrated its ability to forecast disease diagnostic markers. Human physiological parameters are used as input to the model, and the doctor's diagnosis results as an output. Through the attention layer, the degree of attention of the model to different physiological parameters can be obtained, that is, the model provides a diagnostic basis. Results: It achieved 94% ACC, 97.48% AUC, 96% sensitivity and 92% specificity with the test dataset. All the above samples are drawn from clinical practice. Moreover, the model predicted the diagnostic markers of hyperlipidemia by the attention mechanism, and the results were fully agreeable to the golden criteria. Discussion: The auxiliary diagnosis system proposed in this paper not only achieves the accurate and robust performance, and can be used for the preliminary diagnosis of patients, but also showing its great potential to discover new diagnostic markers. Therefore, it not only can improve the efficiency of clinical diagnosis but also shorten the research period of researching a diagnosis basis to an extent. It has a positive significance to the development of the medical diagnosis level.

Machine-Learning-Based Prediction Modelling in Primary Care: State-of-the-Art Review

“…All three models outperformed the clinical standard Dutch Lipid score in both cohorts. Similar findings have been produced for hyperlipidemia, where Liu et al trained an LTSM network on 500 EHR samples [21]. The model achieved an ACC score of 0.94, an AUC score of 0.974, a sensitivity of 0.96, and a specificity of 0.92.…”

The relationship between high-density lipoprotein cholesterol (HDL-C) and glycosylated hemoglobin in diabetic patients aged 20 or above: a cross-sectional study

“…Traditionally, the theory that increasing HDL-C to higher levels is more conducive to reducing cardiovascular diseases and adverse events is being questioned by researchers [ 33 , 34 ]. Some scholars have even suggested that reducing HDL-C is more valuable in certain patients.…”

Artificial intelligence can use physiological parameters to optimize treatment strategies and predict clinical deterioration of sepsis in ICU

“…In order to further increase the auxiliary ability of the model to a certain extent, we also explored what physiological parameters affect the AI model to infer treatment strategies. An attention-based LSTM model is used to analyze the abovementioned influencing factors (Liu et al 2020). The patient's physiological parameters are input, and the model outputs the discrete actions of the doctor or the AI model for the VP dose.…”