| Summary: | This article analyzes the serum electrolyte disturbances of patients through deep learning algorithms. Among the 104 patients with electrolyte disturbances, 6 cases of serum potassium, sodium, chloride, calcium, phosphorus, and magnesium electrolyte disturbances have occurred, the proportion of occurrence The order is sodium > chlorine > calcium > potassium > phosphorus > magnesium. This paper proposes a deep learning algorithm for serum electrolyte disorder, and analyzes and implements the functions at various levels according to the characteristics of the Hadoop framework. The system includes electronic medical records shared storage, distributed realization of definite learning algorithms, classification and recognition of myocardial ischemia by deep learning, and Web system assisting doctor diagnosis, which lays the foundation for the construction of serum electrolyte disorder scientific research data center. In order to explore this relationship, without artificially extracting features, a deep learning model of convolution and long-term and short-term memory circulation neural network cascade was proposed to determine the positive or negative myocardial ischemia by classifying serum disorders. Conduct clinical experiments, including patients with suspected coronary heart disease and coronary angiography as the research object, taking coronary angiography results as the detection standard. Experimental results show that the model has an accuracy of 89.0% for detecting myocardial ischemia, a sensitivity of 91.7%, and a specificity of 81.5%. A linear combination model of CNN and LSTM is proposed to classify and recognize serum electrolyte disorders. Determine the learning theory to dynamically model the ST-T segment in the ECG to obtain serum electrolyte disturbance, which more vividly shows the changes in electrical information under myocardial ischemia. In order to reveal the relationship between serum electrolyte disturbance and myocardial ischemia, this paper builds a neural network model to learn and train serum electrolyte disturbance data to realize the classification of positive or negative myocardial ischemia. Tests on serum electrolyte disturbance data collected in clinical experiments show that this model can better achieve early detection of myocardial ischemia.
|
|---|
