A Survey From Distributed Machine Learning to Distributed Deep Learning

Artificial intelligence has achieved significant success in handling complex tasks in recent years. This success is due to advances in machine learning algorithms and hardware acceleration. In order to obtain more accurate results and solve more complex problems, algorithms must be trained with more data. This huge amount of data could be time-consuming to process and require a great deal of computation. This solution could be achieved by distributing the data and algorithm across several machines, which is known as distributed machine learning. There has been considerable effort put into distributed machine learning algorithms, and different methods have been proposed so far. In this article, we present a comprehensive summary of the current state-of-the-art in the field through the review of these algorithms. We divide this algorithms in classification and clustering (traditional machine learning), deep learning and deep reinforcement learning groups. Distributed deep learning has gained more attention in recent years and most of studies worked on this algorithms. As a result, most of the articles we discussed here belong to this category. Based on our investigation of algorithms, we highlight limitations that should be addressed in future research

Discovering the Symptom Patterns of COVID-19 from Recovered and Deceased Patients Using Apriori Association Rule Mining

The COVID-19 pandemic has a devastating impact globally, claiming millions of lives and causing significant social and economic disruptions. In order to optimize decision-making and allocate limited resources, it is essential to identify COVID-19 symptoms and determine the severity of each case. Machine learning algorithms offer a potent tool in the medical field, particularly in mining clinical datasets for useful information and guiding scientific decisions. Association rule mining is a machine learning technique for extracting hidden patterns from data. This paper presents an application of association rule mining based Apriori algorithm to discover symptom patterns from COVID-19 patients. The study, using 2875 records of patient, identified the most common symptoms as apnea (72%), cough (64%), fever (59%), weakness (18%), myalgia (14.5%), and sore throat (12%). The proposed method provides clinicians with valuable insight into disease that can assist them in managing and treating it effectively

The relationship between psychological empowerment and job burnout: a model with a mediating role of self-efficacy in nurses

Background: The present study aimed to investigate the relationship between psychological empowerment and job burnout with a mediating role of self-efficacy in nurses of private hospitals in Shiraz. Methods: The present study was applied in terms of aim. The statistical population included nurses of private hospitals in Shiraz. According to the statistics of the Deputy of Shiraz University, their number was 750 people. Using Cochran's formula, the sample size was determined at 256 people. To collect data, Maslach job burnout questionnaire, Spreitzer psychological empowerment questionnaire and Sherer & Adams self-efficacy questionnaire and structural equation method were used to analyze the data. Results: The absolute value of the path coefficient to explain the relationship between psychological empowerment and job burnout was -0.545 and t-statistic is higher than 1.96. There was a negative relationship between psychological empowerment and job burnout. Also, psychological empowerment with a mediating role of self-efficacy has a negative path coefficient was -0.704 and t-statistic is higher than 1.96. Self-efficacy increased the effect of psychological empowerment on job burnout. Conclusion: Since perfectionist people and those extremely involved at work suffer from job burnout emotionally and self-efficacy relationship in line with psychological empowerment and inverse relationship with job burnout, it is necessary to take special measures for psychological empowerment to prevent job burnout by managers to increase the efficiency of nurses

Investigating the Relationship between Psychological Empowerment and Job Burnout with a Mediating Role of Emotional Intelligence in Nurses of Private Hospitals in Shiraz

Introduction: Employee burnout is one of the most important issues in today’s organizations because worn-out or depleted employees cannot effectively achieve organizational goals. The present study aimed to investigate the relationship between psychological empowerment and job burnout with a mediating role of emotional intelligence in nurses of private hospitals in Shiraz.Methods: The present study used a cross-sectional analytical method on 256 nurses from private hospitals in Shiraz; according to the statistics of the Vice-Chancellor of Shiraz University, the total number of working nurses was around 750. To collect data, Maslach’s job burnout, Spreitzer’s psychological empowerment, and Bar-On’s emotional intelligence questionnaires were used, and the structural equation method was used to analyze the data.Results: The absolute value of the path coefficient explaining the relationship between psychological empowerment and job burnout was greater than 0.3 (0.545), and the t-statistic was higher than 1.96. Therefore, a negative relationship exists between psychological empowerment and job burnout, so with increased psychological empowerment, job burnout decreases. Also, Emotional intelligence increases the effect of psychological empowerment on job burnout. Thus, job burnout decreases with increased psychological empowerment and emotional intelligence.Conclusion: According to the results of this study, there are mutual effects between psychological empowerment, emotional intelligence, and job burnout, so it is expected that managers can promote psychological empowerment and reduce job burnout by strengthening emotional intelligence and improving the efficiency of nurses

The Emergency Medical Technicians' Performance in Securing Delivery Patients, Including Nervous System Trauma in the city of Iranshahr during 2016-2017

سابقه و هدف: مراقبت هاي پيش بيمارستاني، اولين و مهم ترين بخش در مواجهه با بيماران ترومايي مي باشندکه نقش مهمي در کاهش مرگ و مير و بهبود پيامدها دراين بيماران دارند. اين مطالعه با هدف تعيين عملکرد پرسنل اورژانس پيش بيمارستاني در تحويل ايمن بيماران با تروماي سيستم عصبي شهر ايرانشهر در سال97-1396 انجام شد. روش بررسي: پژوهش حاضر يک مطالعه توصيفي از نوع مقطعي بود که در سال 97-1396 انجام شد. تعداد 139 پرسنل اورژانس پيش بيمارستاني ايرانشهر به صورت سرشماري وارد مطالعه شدند. جمع آوري داده ها با بررسي برگ مأموريت، مصاحبه با پرسنل، مشاهده و بررسي پروسيجرهاي درماني انجام شده براي بيمار منتقل شده به اورژانس انجام شد. ابزار جمع آوري داده ها محقق ساخته و شامل پرسشنامه جمعيت شناختي و چک ليست ارزيابي عملکرد پرسنل بودند، که پس از روان سنجي مورد استفاده قرار گرفتند. تحليل داده ها با استفاده از آناليز توصيفي و تحليلي انجام شد. نتايج: براساس نتايج مطالعه، ميانگين نمره عملکرد پرسنل 3/3±32/17به دست آمد. بالاترين نمره ميانگين عملکرد در حيطه مداخلات مرتبط با تعيين نوع تروما، بررسي علائم حياتي اوليه و بررسي سطح هوشياري10/0±99/0 و کمترين ميانگين نمره عملکرد مربوط به مداخلات مرتبط با باز بودن راه هوايي و ايست قلبي19/0±42/0 بود. نتيجه گيري: طبق نتايج اين پژوهش پرسنل اورژانس پيش بيمارستاني در برخي حيطه هاي مراقبت از بيماران با تروماي سيستم عصبي، از سطح عملکرد پاييني برخوردار بودند. در نتيجه برگزاري دوره هاي آموزشي و باليني مداوم مي تواند تا حدود زيادي نقايص موجود در مراقبت هاي پيش بيمارستاني ارايه شده براي بيماران ترومايي را کاهش داده و پيامدهاي باليني را در اين بيماران بهبود بخشد. How to cite this article: Borhanzehi KH, Ebrahimi Rigi Tanha Z, Dadpisheh S, Yazdan Parast E, Rigi A, Ebrahimi Rigi Tanha H. The Emergency Medical Technicians' Performance in Securing Delivery Patients, Including Nervous System Trauma in the city of Iranshahr during 2016-2017. J Saf Promot Inj Prev. 2021; 8(4):219-26.Background and Objectives: Prehospital care is the first and most important part of dealing with trauma patients that play an essential role in reducing mortality and improving these outcomes. This study aimed to evaluate the emergency medical technicians' performance in securing delivery patients, including nervous system trauma in the city of Iranshahr during 2016-2017. Materials and Methods: The present study was a descriptive cross-sectional study conducted in 2016-2017. A total of 139 prehospital emergency personnel in the Iranshahr participated in this study. Data collected through checking the mission sheet, interview with staff, observing and examining the patient's treatment procedures for the patient transferred to the emergency room. The data collection tools were researcher-made and included a demographic questionnaire and a checklist of staff performance evaluation, which use after psychometrics. Data analysis performed using descriptive and analytical statistical methods. Results: The study results showed the average performance score was 17.32 ± 3.3. The highest mean score of performance in the field of interventions related to determining the type of trauma, assessment of first vital signs and level of consciousness 0.99 ± 0.10, and the lowest mean score of performance about interventions related to airway openness and cardiac arrest were 0.42 ± 0.19. Conclusion: According to this study results, prehospital emergency personnel had low-performance levels in some care areas for nervous system trauma patients. As a result, holding continuous training and clinical courses can significantly reduce the weaknesses in prehospital care provided for trauma patients and improve clinical outcomes.   How to cite this article: Borhanzehi KH, Ebrahimi Rigi Tanha Z, Dadpisheh S, Yazdan Parast E, Rigi A, Ebrahimi Rigi Tanha H. The Emergency Medical Technicians' Performance in Securing Delivery Patients, Including Nervous System Trauma in the city of Iranshahr during 2016-2017. J Saf Promot Inj Prev. 2021; 8(4):219-26

Discovering the symptom patterns of COVID-19 from recovered and deceased patients using Apriori association rule mining

The COVID-19 pandemic has a devastating impact globally, claiming millions of lives and causing significant social and economic disruptions. In order to optimize decision-making and allocate limited resources, it is essential to identify COVID-19 symptoms and determine the severity of each case. Machine learning algorithms offer a potent tool in the medical field, particularly in mining clinical datasets for useful information and guiding scientific decisions. Association rule mining is a machine learning technique for extracting hidden patterns from data. This paper presents an application of association rule mining based Apriori algorithm to discover symptom patterns from COVID-19 patients. The study, using 2875 patient's records, identified the most common signs and symptoms as apnea (72%), cough (64%), fever (59%), weakness (18%), myalgia (14.5%), and sore throat (12%). The proposed method provides clinicians with valuable insight into disease that can assist them in managing and treating it effectively

Colonization and Antibiotic Resistance of Nasal Staphylococcus Aureus among Healthcare Workers in Southwestern Iran: Occurrence of OS-MRSA

Background & Objectives: Staphylococcus spp. is a resident flora of the skin and mucosa of humans that can colonize the anterior nares of individuals. This cross-sectional study was conducted to determine the rate and antibiotic resistance pattern of nasal Staphylococcus aureus (S. aureus) carriers among the staff of Fasa hospital, southern Iran. Materials & Methods: Nasal swab samples were collected from 117 hospital staff working in 12 wards. Microbiological culture method was applied for S. aureus identification. The isolates were confirmed by tuf gene identification using PCR assay. Five isolates were randomly sequenced and phylogenetically analysed  using MEGA software. The antimicrobial resistance pattern of the isolates was evaluated using the disc diffusion assay and the amplification of the methicillin resistance (mecA) gene. Results: The prevalence of S. aureus nasal carriers included 10.26% (n=12). The nasal carriers were identified in the wards of surgery ICU, gynecologic surgery, NICU, pediatric, internal surgery, and emergency. Among them, gynecologic surgery staff had the highest rate of nasal colonization (33.33%). Phylogenetic analysis showed that of five isolates, four had high similarities with each other. Also, the highest rate of resistance was related to penicillin (83.3%), followed by cefazolin (75%), and cephalexin (75%). However, the highest level of susceptibility (100%) was found for vancomycin, cefoxitin, and oxacillin. Furthermore, the methicillin resistance gene (mecA) was highly detected (75%) from the isolates, elucidating oxacillin-susceptible or cefoxitin-susceptible mecA-positive S. aureus (OS-MRSA). Conclusions: The high rates of OS-MRSA can lead to antibiotic resistance among health care workers tremendously. Moreover, the high similarity probability in phylogenetic analysis shows the possibility of cross-infection between these health care workers, warning to exert effective strategies to control infection spread, especially in the surgery ward

Prevalence of and Factors Associated with Migraine in Medical Students at BandarAbbas, Southern Iran, in 2012

Background: Migraine is one of the most common etiologies for headache. This very common neurological disorder has a significant impact on patients' quality of life. The aim of the current study is to evaluate the prevalence of migraine among medical students in the Hormozgan University of Medical Sciences (HUMS). Methods: A total of 350 medical students were enrolled in our descriptive study. Data were collected using the standard questionnaire of the International Headache Association. The data were analyzed by SPSS 20.0 software using descriptive statistics, Chi-Square, and Independent Samples T-Test. A P-value of ≥0.05 was considered statistically significant, since most public health professionals use this value as a standard. Results: Among the medical students in our study, 24.6% had experienced frequent, severe headaches. The underlying causes of the headaches were diagnosed in 19.8% of the students. The prevalence of migraine in our study was 16.3% (mean age=21.28±2.71years). The prevalence varied by gender, and it was greater among male students. Conclusion: Our findings indicated that there was a high prevalence of migraine among the medical students in our study, and these findings were consistent with those of previous studies in Iran and other countries