List of Articles Zainabolhoda Heshmati

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  1 - Optimized Modeling for satisfaction in the relationship between a physician and patient based on machine learnin methods
  Fatemeh Saghafi mojtaba shadmehr Zainabolhoda Heshmati Hadi Veisi
  Health has always been one of the most important concerns of human. The goal in this research is to know what factors cause and affect patient satisfaction in the relationship between a physician and patient. Since this relationship is a form of healthcare service, the More

  Health has always been one of the most important concerns of human. The goal in this research is to know what factors cause and affect patient satisfaction in the relationship between a physician and patient. Since this relationship is a form of healthcare service, the SERVQUAL service quality assessment method has been used as a framework. However the questions have been reviewed based on the previous literature and the experts’ views, leading to a questionnaire designed for the healthcare domain. Data collection has been performed using the questionnaires on subjects selected amongst clients of Rhinoplasty Centers in Tehran. To analyze the data, three machine-learning approaches have been implemented namely Decision Tree, Support Vector Machine and Artificial Neural Networks. A number of possible factors affecting the patient-physician relationship have been used as input and patient satisfaction has been taken as output. Comparing the results of these three methods, Artificial Neural Networks method is shown to have better performance, which has therefore been used for prioritizing the effective factors in this relationship. The results indicate that reaching the information which the patient expects their physician to give is the most effective characteristic in patient satisfaction. The rank of gained features were compared with similar researches. The outcome was very similar and approved the results. Manuscript profile
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  2 - An optimised hybrid method for ambulance dispatch based on complex networks and Artificial Intelligence
  Zainabolhoda Heshmati Mehdi Teimouri mehdi zarkeshzade Hadi Zare
  Nowadays, monitoring people’s health and helping those in need of medical attention is one of the most important responsibilities of governments. Emergency Medical Services (EMS) are therefore setup to provide timely care to victims of sudden and life-threatening injuri More

  Nowadays, monitoring people’s health and helping those in need of medical attention is one of the most important responsibilities of governments. Emergency Medical Services (EMS) are therefore setup to provide timely care to victims of sudden and life-threatening injuries or emergencies in order to prevent needless mortality or long-term morbidity. Providing rapid EMS with minimum response time will help improve survival rates. However, factors such as limited ambulance resources, large coverage area and high call-rates have caused delays in EMS, which have lead to lower survival rates. In this research the problem of ambulance dispatching with regards to lowering the response time is discussed. Response time is the most important factor in evaluating the performance of various EMS, which is directly proportional to mortality and survival rates. The most common method to reduce response time in ambulance dispatching is to choose the nearest available ambulance unit, i.e. the nearest neighbor (NN). Other methods use call-priority, first-in first-out (FIFO), preparedness and hybrid algorithms. One of the latest methods used in ambulance dispatching is based on complex networks. This method will apply a higher priority to the call that is more centrally located with respect to other calls. A hybrid algorithm has been proposed in this research which exceed the previous methods in terms of response time reduction.The proposed hybrid approach is based on complex network analysis and a search algorithm, which relies on the important parameters of dispatch, yet presents fewer constraints and overcomes some of the difficulties of the previous methods. In addition, prioritizing emergency calls is also considered. To evaluate these proposed algorithms, a combination of various parameters and operating environments has been simulated. The results of the simulations show improvements in response time reduction compared to previous methods. Manuscript profile
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  3 - Optimum modeling of patient satisfaction with the doctor based on machine learning methods
  - شادمهر Zainabolhoda Heshmati Fatemeh saghafi Hadi Veisi
  20.1001.1.27170411.1394.7.24.5.8
  The patient-centered approach in the field of health has recently been proposed in the field of the medical system of our country, but until now there is no published scientific research on the factors of patient satisfaction with doctors. The present article aims to co More

  The patient-centered approach in the field of health has recently been proposed in the field of the medical system of our country, but until now there is no published scientific research on the factors of patient satisfaction with doctors. The present article aims to cover the stated gap with a scientific evaluation based on the real information obtained from the field study. A questionnaire was designed for the health sector and was approved by the opinion of experts. In order to get the opinions of patients, a questionnaire was distributed among 500 people who underwent rhinoplasty in Tehran, and 395 questionnaires were collected. Three methods of decision tree, support vector machine and neural networks were used for data analysis. The analysis of the results according to the accuracy criteria showed that the most efficient method, in priority, the importance of the factors affecting the patient's satisfaction; Neural network method. The results of the analysis with this method indicate that the most effective feature in the patient's satisfaction with the doctor is the information that the patient expects the doctor to provide. The results of ranking factors in comparison with other studies that only used statistical methods for analysis showed that the results were relatively similar and confirmed each other. But the strengths of the neural network method in modeling is the strength of this method compared to the mentioned studies. Manuscript profile
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  4 - Providing an optimal method for dispatching an ambulance based on complex networks and artificial intelligence
  مهدی زرکش زاده Zainabolhoda Heshmati Hadi Zare Mehdi Teimouri
  20.1001.1.27170411.1394.7.24.4.7
  The goal of emergency medical services is to reduce deaths and complications caused by diseases and injuries. Rapid dispatch of emergency services and reduced response time lead to increased survival rates. Response time is one of the important criteria for measuring th More

  The goal of emergency medical services is to reduce deaths and complications caused by diseases and injuries. Rapid dispatch of emergency services and reduced response time lead to increased survival rates. Response time is one of the important criteria for measuring the efficiency of emergency medical services. The usual method of sending ambulances is to send the nearest available unit, which pays attention to efficiency in the short term. One of the methods that has been recently mentioned in the field of ambulance dispatch is based on the analysis of complex networks. The purpose of this method is to send an ambulance to a call that is more central than other calls, which leads to better efficiency in the long run. Other methods in dispatching an ambulance are based on finding the best suitable route for service cars, and the time complexity of these methods is very high. In this article, using a hybrid approach and applying centrality criteria from complex network analysis and search methods based on artificial intelligence, an optimal and innovative method is presented to reduce the response time of emergency services. In addition, in the proposed method, the emergency priority of calls is also considered, which is an important variable in decisions. The proposed method has less limitations than the previous methods and the extensive simulation results confirm the significant improvement of this method compared to the previous methods such as the centrality method and the nearest neighbor method. Manuscript profile