Nastaran Abbasi Hasanabadi, Farzad Firouzi Jahantigh, Payam Tabarsi,
Volume 13, Issue 6 (Feb & Mar 2020)
Abstract
Background and Aim: Despite the implementation of effective preventive and therapeutic programs, no significant success has been achieved in the reduction of tuberculosis. One of the reasons is the delay in diagnosis. Therefore, the creation of a diagnostic aid system can help to diagnose early Tuberculosis. The purpose of this research was to evaluate the role of the Naive Bayes algorithm as a tool for the diagnosis of pulmonary Tuberculosis.
Materials and Methods: In this practical study, the study population included Patients with TB symptoms, the study sample is recorded data of 582 individuals with primary Tuberculosis symptoms in Tehran's Masih Daneshvari Hospital. The data of samples were investigated in two classes of pulmonary Tuberculosis and non-Tuberculosis. A Naive Bayes algorithm for screening pulmonary Tuberculosis using primary symptoms of patients has been used in Python software version 3.7.
Results: Accuracy, sensitivity and specificity after the implementation of the Naive Bayes algorithm for diagnosis of pulmonary Tuberculosis were %95.89, %93.59 and %98.53, respectively, and the Area under curve was calculated %98.91.
Conclusion: The performance of a Naive Bayes model for diagnosis of pulmonary Tuberculosis is accurate. This system can be used to help the patient and manage illness in remote areas with limited access to laboratory resources and healthcare professional and cause to diagnose early Tuberculosis. It can also lead to timely and appropriate proceedings to control the transmission of TB to other people and to accelerate the recovery of the disease.
Setareh Talayeh, Farzad Firouzi Jahantigh, Fatemeh Bahman,
Volume 17, Issue 5 (12-2023)
Abstract
Background and Aim: The tourism industry plays a very important role in the economic cycle of society. Medical tourism, as one of the types of tourism industries, has a direct result in globalizing health care. Therefore, by strengthening the supply chain in this area, a very high added value can be achieved. For this reason, the present study provides a conceptual framework for predicting the demand for medical tourism supply chain by determining the relationship between medical tourism demand and economic, medical, and welfare-service components of Zahedan city.
Materials and Methods: The present study is a descriptive-analytical and applied research. Data were collected using a questionnaire and field and library methods. The statistical population of interest was specialist doctors in Zahedan city, and 97 people were selected using simple random sampling with Morgan’s table. The validity of the questionnaire was confirmed by experts and its reliability was obtained using Cronbach’s alpha coefficient with SPSS software more than 0.7. Data analysis was performed using the tangent sigmoid neural network algorithm, linear regression criteria, and mean square error. For this purpose, SPSS software was used to examine the correlation between the data, and MATLAB software was used to design the neural network.
Results: There was anerrore in The basis for the optimality of the answers, linear regression criteria and mean square error. The results showed that the values related to regression, education, and health were more than 0.8 and were 0.9033, 0.8818, and 0.9985, respectively. The highest priorities of the respondents related to medical equipment, education, and health were 0.5657, 0.5558, and 0.20726, respectively.
Conclusion: According to the results obtained from the proposed model, the neural network has a high accuracy in predicting the demand for medical tourism supply chain in terms of education, health, and welfare. It is also predicted that the demand for medical tourism has been constant during the one-year period of research and it is expected that medical tourism in Zahedan city will decrease in future. Therefore, it is recommended that officials pay attention to the development and improvement of medical tourism to promote it.
Mohammadreza Asghariyan, Farzad Firouzi Jahantigh,
Volume 18, Issue 4 (10-2024)
Abstract
Background and Aim: The emergency department of the hospital is considered one of its main entrances; which has provided health care and treatment for critical and non-critical patients and faces various health and treatment restrictions, but the main emphasis is always on resource limitations. Many simulation projects were implemented in hospitals and first in emergency departments with the aim of increasing productivity. The present research is a general description of the patient’s movement flow and length of stay in the emergency department of a selected specialized hospital in Zahedan city. The aim of the current research is to prevent care complications, reduce waiting time and patient stay in the emergency department, present a simulation model and improve it based on discrete-event simulation.
Materials and Methods: Using the data bank of the emergency department system based on the required data and also through the in-person observation of the data related to the duration of the patient’s stay in the emergency department, including the arrival time, waiting time, The type of services provided to the patient, the time of service and the time of departure were collected and checked and confirmed by experts related to this field so that it has the highest level of reliability with the facts. The data were designed in Excel software, and then data analysis and simulation model creation were done using Aren V14 software, and according to the results, the effect of the proposed solutions was evaluated.
Results: The findings of the present research showed that the longest queue created in the emergency department of the selected specialized hospital in Zahedan city is related to medical examination and additional tests. By implementing the simulation model and testing different solutions, solution 3, which means adding one nurse to nursing consultation and one person to radiology, has the most optimizing effect on the performance of the system at different levels of the patient admission process. and the cost of its implementation is more than solutions 1 and 2. This solution created a 14% decrease in the average length of stay and a 28% decrease in the average duration of additional tests.
Conclusion: The use of queuing models and simulation techniques improve the performance of the system and their implementation has significant effects on reducing the waiting time and length of stay of patients in the emergency department, increasing the quality level of the process of monitoring patients. It leads to optimal management of resources and increased productivity.
