Estimation of echocardiogram parameters with the aid of impedance cardiography and artificial neural networks

Dynamic Monitoring of Serum Protein in Acute Respiratory Distress Syndrome Based on Artificial Neural Network

Acute respiratory distress syndrome (ARDS) is one of the more serious diseases in human lung disease. Reducing its incidence rate is an important task in current clinical research. Dynamic monitoring of serum protein in patients will help to achieve the early diagnosis and treatment of ARDS. In this study, a protein monitoring model based on artificial neural network is proposed. First, surface enhanced laser desorption ionization time-of-flight mass spectrometry is used for protein detection, and then BP neural network is used for protein classification and content analysis. In the experimental analysis, serum samples from patients with acute respiratory distress syndrome in our hospital from November 2020 to August 2021 were selected for experimental testing. The experimental results show that the serum protein monitoring model based on BP neural network has low error and high convergence ability and can monitor individual protein in protein monitoring, and the area under the ROC curve in diagnostic performance reaches 0.854. The above results show that the artificial neural network has a good effect on the dynamic monitoring of serum protein in acute respiratory distress syndrome, and the diagnostic performance evaluation can reach 0.854, which has the ability to significantly improve the clinical diagnosis and treatment of acute respiratory distress syndrome.

Application of Optimized GA-BPNN Algorithm in English Teaching Quality Evaluation System

The assessment of teaching quality is a very complex and fuzzy nonlinear process, which involves many factors and variables, so the establishment of the mathematical model is complicated, and the traditional evaluation method of teaching quality is no longer fully competent. In order to evaluate teaching quality effectively and accurately, an optimized GA-BPNN algorithm based on genetic algorithm (GA) and backpropagation neural network (BPNN) is proposed. Firstly, an index system of teaching quality evaluation is established, and a questionnaire is designed according to the index system to collect data. Then, an English teaching quality evaluation system is established by optimizing model parameters. The simulation shows that the average evaluation accuracy of the GA-BPNN algorithm is 98.56%, which is 13.23% and 5.85% higher than those of the BPNN model and the optimized BPNN model, respectively. The comparison results show that the GA-BPNN algorithm in teaching quality evaluation can make reasonable and scientific results.

Cardiovascular Diseases Diagnosis by Impedance Cardiography

Cardiovascular disease (CVD) represents the leading cause of mortality worldwide. In order to diagnose CVDs, there are a range of detection methods, among them, the impedance cardiography technique (ICG). It is a non-invasive and low-cost method. In this paper, we highlight recent advances and developments of the CDVs diagnosis mainly by the ICG method. We considered papers published during the last five years (from 2017 until 2022). Based on this study, we expressed the need for an ICG database for the different CDVs.

A Deep Learning-Based Unsupervised Method to Impute Missing Values in Patient Records for Improved Management of Cardiovascular Patients

Physicians increasingly depend on electronic health records (EHRs) to manage their patients. However, many patient records have substantial missing values that pose a fundamental challenge to their clinical use. To address this prevailing challenge, we propose an unsupervised deep learning-based method that can facilitate physicians' use of EHRs to improve their management of cardiovascular patients. By building on the deep autoencoder framework, we develop a novel method to impute missing values in patient records. To demonstrate its clinical applicability and values, we use data from cardiovascular patients and evaluate the proposed method's imputation effectiveness and predictive efficacy, in comparison with six prevalent benchmark techniques. The proposed method can impute missing values and predict important patient outcomes more effectively than all the benchmark techniques. This study reinforces the importance of adequately addressing missing values in patient records. It further illustrates how effective imputations can enable greater predictive efficacy with regard to important patient outcomes, which are crucial to the use of EHRs and health analytics for improved patient management. Supported by the complete data imputed by the proposed method, physicians can make timely patient outcome estimations (predictions) and therapeutic treatment assessments.