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The Effects of Implementing Clinical Decision Support Systems on the Diagnosis, Treatment and Management of Cancers: A Systematic Review
Saman Mohammadpour, Reza Rabiei, Elham Shabahrami, Kamyar Fathisalari, Maryam Khakzad, Mostafa Langarizadeh,
Volume 16, Issue 2 (5-2022)
Abstract
Background and Aim: Cancer is the second leading cause of death in the world, which leads to the death of more than 10 million people in the world every year. Its early diagnosis, management and proper treatment play an important role in reducing complications and mortality. One of the support tools in early diagnosis, treatment and management of this disease are Clinical Decision Support System (CDSS), which are divided into two groups, rule-based and non-rule-based. Rule-based decision support systems are created based on clinical guidelines, while non-rule-based decision support systems use machine learning. In this research, the effects of decision support systems, rule-based and non-rule-based, on cancer diagnosis, treatment and management were measured.
Materials and Methods: The present study was conducted using a systematic review method, which was conducted by searching the Web of Science, Scopus, IEEE and PubMED databases until 12/31/2021. After removing duplicates and evaluating the characteristics of the inclusion and exclusion criteria, studies related to the goal were selected. The selection of articles was based on the title, abstract and full text The data collection tool was the data extraction form, which included year of study, type of study, system of body, organ of body, the service provided by the decision support system, type of decision support system, effect, effect index and the score of effect index. Narrative synthesis were used for data analysis.
Results: Out of 768 articles, 16 articles related to the objectives of the study were identified. Studies were presented in two categories of clinical decision-support systems: Rule-based and non-Rule based. The effects evaluated in the clinical decision support systems were Rule-based, dose adjustment, symptoms, adherence to treatment guidelines, care time, smoking, need for chemotherapy and pain management, all of which except pain management were significant and positive. The effects evaluated were in the category of non-Rule based clinical decision support systems, diagnostic and therapeutic decisions, controlling neutropenia, all of which were significant and positive except controlling neutropenia.
Conclusion: The results obtained for the effectiveness of both Rule-based and non-Rule-based decision support systems indicated different benefits of these two categories. Therefore, using their combination in the field of cancer can bring very useful results.

Investigating the Relationship between Factors Affecting Hip Fracture and Its Consequences in Imam Khomeini Hospital Complex during 2017-2020
Mahya Jafarnejad, Esmaeil Mohammadnejad, Leila Sayadi, Shima Haghani, Reza Ghanei Gheshlagh, Afzal Shamsi,
Volume 16, Issue 3 (8-2022)
Abstract
Background and Aim: Fractures and dislocations of the femur are a common and disruptive public health problem worldwide. One of the most common ways is to identify the factors associated with common outcomes that increase mortality, length of hospital stay, and postoperative complications. By identifying these factors, the adverse outcomes of elderly hip fractures can be prevented. Therefore, the aim of this study was to determine the relationship between factors affecting hip fracture and its consequences. 
Materials and Methods: The present study was a descriptive cross-sectional observational study. The study population in this study included patients with hip fractures. Patients’ information was examined between 2017-2020 years. Data were analyzed by SPSS applying descriptive statistics, Fisher Exact test, chi-square, independent t-tests, and analytical regression.
Results: The results showed that in this study, the majority of patients with hip fracture had an underlying disease (73.9%). The most common underlying diseases in patients included high blood pressure (20.7%), diabetes (13.2%) and heart diseases (10.5%). The most common causes of death was include old age (40.4%), prevalence of covid-19 (20.2%), heart attack (11.7%), pulmonary embolism (10.6%) and surgical wound infection (10.6%). The most common cause of hip fracture was osteoporosis (26.3%) and falling from a height (24.7%). Also level of education (P=0.0001), causes of fracture (P=0.001), type of anesthesia (P=0.001), history of hospitalization in special wards (P=0.001), readmission (P=0.0001), age (P=0.001) and level of self-care (P=0.001) were significantly associated with elderly mortality. There was a significant relation between type of surgery (P=0.038), history of hospitalization in intensive care units (P=0.001), history of blood transfusion (P=0.021) and level of self-care (P=0.001) with length of hospital stay of fractured elderly hip.
Conclusion: It can be concluded that by identifying the factors affecting the length of hospital stay, surgical wound infection and mortality of the patients with hip fractures, witness better surgery results, shorter hospital stay, less postoperative complications and reduced death. Some factors such as osteoporosis are preventable, which can be prevented with timely education.

The Impact of COVID-19 on Outcome Indicators of Hospitals of Shahid Beheshti University of Medical Sciences: An Interrupted Time Series Analysis
Mohammad Jalali, Ehsan Zarei, Ali Maher, Soheila Khodakarim,
Volume 16, Issue 5 (12-2022)
Abstract
Background and Aim:  With the outbreak of the COVID-19 pandemic, the performance of hospitals were affected, and changes were made in the utilization of hospital services. Analyzing hospital performance data during the COVID-19 pandemic can provide insights into service utilization patterns and care outcomes for managers and policymakers. This study was conducted to investigate the impact of COVID-19 on selected outcome indicators in the hospitals of Shahid Beheshti University of Medical Sciences, Tehran.
Materials and Methods: This research was descriptive-analytical and of the time series analysis type. Six outcome indicators were considered: hospitalization rate, bed occupancy rate, the average length of stay, emergency visits, laboratory tests, and imaging requests. Related data from 12 affiliated hospitals from 2017-2019 (pre-COVID) and 2020 (post-COVID) were obtained from the hospital's intelligent management system. The data were analyzed using R software's interrupted time series analysis method.
Results: The hospitalization rate (P=0.015), bed occupancy rate (P=0.04), and the number of laboratory tests (P=0.003) significantly increased immediately after the outbreak of the pandemic. In contrast, emergency visits (P=0.034) have significantly decreased. The bed occupancy rate and the number of imaging requests showed no significant change. The decrease in emergency room visits within one year after the pandemic was significant, but the changes in other outcome indicators were non-significant (P>0.05).
Conclusion: Understanding the changes and impact of a major event on hospital outcome indicators is necessary for decision-makers to effectively plan for resource allocation and effective pandemic response. The outbreak of COVID-19 has caused a change in performance and hospital outcomes by affecting the supply and demand of services. In a year after the pandemic's beginning, except for emergency visits, the other indicators have not experienced significant changes. Preservation of essential services such as emergency room visits is recommended in the strategy of rapid response to an epidemic outbreak and public campaigns to encourage people to seek medical care if needed in future waves of the pandemic.

Predicting Kidney Transplantation Outcomes Using Machine Learning Techniques
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.
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