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Study of Telemental Health Services for Veterans from Viewpoints of Experts
Maryam Ahmadi, Tayebeh Noori, Kambiz Bahaadin Beigy , Esmaeil Mehraeen,
Volume 10, Issue 1 (4-2016)
Abstract

Background and Aim: For more than forty years, telemental health services have been used as a successful mean in various fields such as treatment and preventive interventions. This study was aimed to determine the viewpoints of health information management (HIM), and mental health professionals about telemental health services for veterans with mental disorders.

Materials and Methods: This cross-sectional survey was conducted in 2013. The study population consisted of two groups: the first group included mental health professionals working in psychiatric hospitals in Tehran and the second group comprised HIM professionals. The data were collected using a questionnaire that its validity was confirmed by experts and the reliability was estimated through test-retest method. The data analysis was performed using SPSS 17.0 and descriptive statistics.

Results: The findings showed that from the mental health experts' viewpoints, highest impact of the use of telemental health services related to medical travel costs with average 4.37 and the lowest impact replacement with the face to face treatment with average 2.68.

Conclusion: In general, the groups participating in this study stated that in situations where access to care was difficult, telemental health services could be used as a reliable alternative for the war disabled care needs. Therefore, it is suggested that Iranian foundation of Martyrs and Veterans Affairs and the health system administrators take more serious measures for the implementation of telemedicine for veterans.


Prediction of Sepsis Due to Acinetobacter Infection in Neonates Admitted to NICU
Niloofar Mohammadzadeh, Ziba Mosayebi, Hamid Beigy, Mohammad Shojaeinia,
Volume 14, Issue 6 (Feb & Mar 2021)
Abstract
Background and Aim: Sepsis is the most important disease in the first 28 days of life and one of the main causes of infant mortality in the intensive care unit. Its definitive diagnosis is possible by performing blood culture. Neonatal sepsis can be a clinical sign of nosocomial infections that are often resistant to antibiotics. Therefore, the purpose of this study was to create and evaluate a hospital sepsis prediction model and present its results to health care providers.
Materials and Methods: In this descriptive-applied study, the research population includes neonates admitted to the intensive care unit of Valiasr Hospital in Tehran and the research sample is the data of 4196 neonates admitted to this ward from 2016 to August, 2020. The initial features for creating a predictive model of sepsis were prepared by examining the relevant information sources and under the supervision of professors and officials of Valiasr Hospital's mother and fetus research center and its validity was confirmed by 5 neonatal professors of this hospital. In this research, machine learning algorithms have been used to create a sepsis prediction model.
Results: Accuracy and AUROC(area under the ROC curve) parameters were used to evaluate the generated models. The highest values of Accuracy and AUROC are related to Adaptive Boosting and random forest algorithms, respectively.
Conclusion: Learning curves show that using different training examples and more complex selection of combination features improves the performance of the models. Further research is needed to evaluate the clinical effectiveness of machine learning models in a trial.

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