Providing a decision-making model for continuous monitoring of patient's hypertension using artificial neural network and quality control charts

Heart patients with hypertension and myocardial infarction and hemodialysis patients are at risk of lowering of blood pressure during hemodialysis. Therefore, continuous monitoring of blood pressure in health centers is a priority for deciding the type of therapeutic treatment in these patients. The aim of this study was to provide a decision-making model based on neural network and quality control charts for monitoring and continuous monitoring of blood pressure.
This was an applied-developmental study carried out in cross-sectional method during 2014 to 2015, on 175 patients with hypertension in Ali ibn Abi Talib Hospital of Zahedan. The actual behavior of the systolic and diastolic blood pressure of these patients under the online model and its expected behavior was studied under the Offline model and in the form of an artificial neural network. By evaluating the true behavior of patients with their modeled blood pressure, residual values are produced and introduced as inputs of EWMA quality control charts in two steps to determine the state of the patient out of control and determine the status of the patient.
Highest and lowest total rate of accuracy of the proposed model, in the state of the patient out of control stage were 98.75 and 95% in the status of the patient stage, respectively, 83.75% and 70%. Narrower control limits with a confidence level (95.45% confidence interval) in the state of the patient out of control stage and more open limits with a confidence level (99.73% confidence interval) in the status of the patient stage. The higher overall total accuracy rate and lower error rates are provided in the model.
According to the results of the research in regard to the field of statistical quality control, ease of visual comprehension, affordability and attention to the last critical condition of the patient, it is a model suitable for physicians and patients for healthcare decision-makings.