Clinical Manifestations of Wegener Granulomatosis in Iranian Ethnicities Using the K-Means Algorithm: A Descriptive Study.

Introduction Wegener granulomatosis (WG) appears with clinical symptoms, including recurrent respiratory infection, renal manifestations, and nonspecific systemic symptoms. Objective To study the clinical manifestations of WG in Iranian ethnicities, and data on 164 patients were recorded from 2013 to 2018. Methods The data included demographics, symptoms, and the Birmingham Vasculitis Activity Score (BVAS). The symptoms involved the following sites: the nose, sinus, glottis, ears, lungs, kidneys, eyes, central nervous system, mucous membranes, skin, heart, stomach, intestine, as well as general symptoms. The clinical manifestations of nine ethnicities were analyzed. Results In total, 48% of the patients were male and 51% were female, with a median age of 51 years. The BVAS was of 15.4, the sites most involved were the sinus ( n = 155), nose ( n = 126), lungs ( n = 125), and ears ( n = 107). Gastrointestinal ( n = 14) and cardiac ( n = 7) involvement were less common. Among the patients, 48.17% were Persian, 13.41% were Azari, 11.17% were Gilaki, 11.17% were Kurd, and 10.9% were Lor. Conclusion Our findings indicated that the sinus, nose, lungs, and ears were the sites most involved, and gastrointestinal and cardiac involvement were less common. In the present study, involvement of the upper and lower respiratory tract was higher than that reported in Western and Asian case series. Moreover, we report for the first time that, in all patients with ear involvement, the left ear was the first to be affected. The clinical manifestations among Iranian ethnicities were not different, and the Gilaki ethnicity had the highest BVAS, mostly because the weather was humid; therefore, in Iran, in areas with humidity, the rate of the disease was higher.

Predicting the COVID-19 mortality among Iranian patients using tree-based models: A cross-sectional study.

Background and Aims: To explore the use of different machine learning models in prediction of COVID-19 mortality in hospitalized patients. Materials and Methods: A total of 44,112 patients from six academic hospitals who were admitted for COVID-19 between March 2020 and August 2021 were included in this study. Variables were obtained from their electronic medical records. Random forest-recursive feature elimination was used to select key features. Decision tree, random forest, LightGBM, and XGBoost model were developed. Sensitivity, specificity, accuracy, F-1 score, and receiver operating characteristic (ROC)-AUC were used to compare the prediction performance of different models. Results: Random forest-recursive feature elimination selected following features to include in the prediction model: Age, sex, hypertension, malignancy, pneumonia, cardiac problem, cough, dyspnea, and respiratory system disease. XGBoost and LightGBM showed the best performance with an ROC-AUC of 0.83 [0.822-0.842] and 0.83 [0.816-0.837] and sensitivity of 0.77. Conclusion: XGBoost, LightGBM, and random forest have a relatively high predictive performance in prediction of mortality in COVID-19 patients and can be applied in hospital settings, however, future research are needed to externally confirm the validation of these models.

A Multi Agent Based Approach for Prehospital Emergency Management.

OBJECTIVE: To demonstrate an architecture to automate the prehospital emergency process to categorize the specialized care according to the situation at the right time for reducing the patient mortality and morbidity. METHODS: Prehospital emergency process were analyzed using existing prehospital management systems, frameworks and the extracted process were modeled using sequence diagram in Rational Rose software. System main agents were identified and modeled via component diagram, considering the main system actors and by logically dividing business functionalities, finally the conceptual architecture for prehospital emergency management was proposed. The proposed architecture was simulated using Anylogic simulation software. Anylogic Agent Model, State Chart and Process Model were used to model the system. RESULTS: Multi agent systems (MAS) had a great success in distributed, complex and dynamic problem solving environments, and utilizing autonomous agents provides intelligent decision making capabilities.  The proposed architecture presents prehospital management operations. The main identified agents are: EMS Center, Ambulance, Traffic Station, Healthcare Provider, Patient, Consultation Center, National Medical Record System and quality of service monitoring agent. CONCLUSION: In a critical condition like prehospital emergency we are coping with sophisticated processes like ambulance navigation health care provider and service assignment, consultation, recalling patients past medical history through a centralized EHR system and monitoring healthcare quality in a real-time manner. The main advantage of our work has been the multi agent system utilization. Our Future work will include proposed architecture implementation and evaluation of its impact on patient quality care improvement.