Soraya Sayar, Sara Noruzi, Seyed Mohammad Hossein Javadi, Mohammad Sabzi Khoshnami, Sanaz Dehghani, Maryam Pour Hosein, Mahnaz Zamyadi,
Volume 17, Issue 6 (2-2024)
Abstract
Background and Aim: Considering the role of social workers in the health system, the success and stability of organ transplantation, prevention of re-hospitalization of patients and reduction of imposed costs, this study aims to design a protocol for the specialized interventions of social workers in Iran’s medical centers in the transplant process and to create uniformity in practice. Comprehensive services have been provided to patients and their caregivers.
Materials and Methods: The study was conducted in two phases of resource review and qualitative. In the resource review phase, reliable databases were examined, and in the qualitative phase, in order to collect information from Delphi techniques and focused group discussion with the presence of fifteen officials and social workers working in the country’s hospitals and experts and experts. The areas of health and treatment assistants of the Ministry of Health, university professors, medical staff and experts in the field of transplantation and organ donation were carried out. The protocol was sent to eight experienced experts for final review and evaluation, and they were asked to review the protocol in terms of the goal and scope of interventions, stakeholders, development steps, clarity of presentation, accessibility and non-dependence in writing according to the checklist to evaluate.
Results: In this study, the work process of social workers was drawn in three stages before, during and after transplantation. Also, various roles were considered for social workers in three stages of work, including the role of defender, supporter, case manager, resource mobilizer, trainer, consultant, evaluator and guidance. Interventions such as finding informational support, facilitating the patient hospitalization process, providing psychosocial support to the family with the aim of empowering them for post-transplant care, providing counseling to family members to deal with stress and improving mental health of the patient, providing economic support, Accomodation conditions are provided through hospital companions and communication and interaction with the treatment team, including the doctor, in order to respond to the needs of the family, facilitate treatment and on time discharging.
Conclusion: the protocol of specialized interventions of social workers in the transplantation process created a new step and a different look at psychosocial support in transplantation and coordination teams, so that social workers in the field of organ donation and transplantation and working with caregivers, families and the survivors of the patients arrived.
Miss Fariba Moalem Borazjani, Azita Yazdani, Reza Safdari, Seyed Mansoor Gatmiri,
Volume 17, Issue 6 (2-2024)
Abstract
Background and Aim: Kidney failure is a common and increasing problem in Iran and worldwide. Kidney transplantation is recognized as a preferred treatment method for patients with end-stage renal disease (ESRD). Machine learning, as one of the most valuable branches of artificial intelligence in the field of predicting patient outcomes or predicting various conditions in patients, has significant applications. The purpose of this research was to predict kidney transplant outcomes in patients using machine learning.
Materials and Methods: Since CRISP is one of the strongest methodologies for implementing data mining projects, it was chosen as the working method. In order to identify the factors affecting the prediction of kidney transplant outcomes, a researcher-created checklist was sent to some of nephrologists nationwide to determine the importance of each factor. The results were analyzed and examined. Then, using Python language and different algorithms such as random forest, SVM, KNN, deep learning, and XGBoost the data was modeled.
Results: The final model was multilabel, capable of predicting various kidney transplant outcomes, including rejection probability, diabetic reactions, malignant reactions, and patient rehospitalization. After modeling the input data features, the model was able to predict the four kidney transplant outcomes such as rejection, diabetes, malignancy and readmission with an error rate of less than 0.01.
Conclusion: The high level of accuracy and precision of the random forest model demonstrates its strong predictive power for forecasting kidney transplant outcomes. In this study, the most influential factors contributing to patient susceptibility to the mentioned outcomes were identified. Using this machine learning-based system, it is possible to predict the probability of these outcomes occurring for new cases.
