Background: This study aimed to assess the performance of the Acute Physiology and Chronic Health Evaluation (APACHE) II, APACHE IV, Simplified Acute Physiologic Score (SAPS) II, and Sequential Organ Failure Assessment (SOFA) scores in predicting mortality rate in poisoning patients admitted to an intensive care unit (ICU).Methods: This cross-sectional study was performed on all admitted patients in the poisoning ICU of Imam Reza Hospital, Mashhad City, Iran. All patients were evaluated for three consecutive days since admission time and then every two days until discharge from ICU or death. The scoring systems mentioned above were calculated and analyzed by MedCalc statistical software version 18.9.1 and SPSS version 16.Results: Overall, 150 patients were studied, out of whom 67% (101) were male. Their mean±SD age was 41.6±18.9 years. In their whole hospitalization period, APACHE II (79.5%), SAPS II (78.7%), APACHE IV (78.4%), and SOFA (72.9%) were the most precise measures. On the first day of admission APACHE II (77.4%), on the second day, APACHE II (83.1%), on the third day, APACHE II (90.7%), and on the fifth day, SOFA (81.6%) were the most precise measures.Conclusion: All four systems have acceptable discriminatory power for poisoned patients. However, it seems that APACHE II can be used for mortality prediction, especially in the early days of admission. 

Background A disease severity classification system is widely used to predict the survival of patients admitted to the intensive care unit with different diagnoses. In the present study, conventional severity classification systems were compared with artificial intelligence predictive models (Artificial Neural Network and Decision Tree) in terms of the prediction of the survival rate of the patients admitted to the intensive care unit. Methods This retrospective cohort study was performed on the data of the patients admitted to the ICU of Ghaemshahr’s Razi Teaching Care Center from March 20th, 2017, to September 22nd, 2019. The required data for calculating conventional severity classification models (SOFA, SAPS II, APACHE II, and APACHE IV) were collected from the patients’ medical records. Subsequently, the score of each model was calculated. Artificial intelligence predictive models (Artificial Neural Network and Decision Tree) were developed in the next step. Lastly, the performance of each model in predicting the survival of the patients admitted to the intensive care unit was evaluated using the criteria of sensitivity, specificity, accuracy, F-measure, and area under the ROC curve. Also, each model was validated externally. The R program, version 4.1, was used to create the artificial intelligence models, and SPSS Statistics Software, version 21, was utilized to perform statistical analysis. Results The area under the ROC curve of SOFA, SAPS II, APACHE II, APACHE IV, multilayer perceptron artificial neural network, and CART decision tree were 76.0, 77.1, 80.3, 78.5, 84.1, and 80.0, respectively. Conclusion The results showed that although the APACHE II model had better results than other conventional models in predicting the survival rate of the patients admitted to the intensive care unit, the other conventional models provided acceptable results too. Moreover, the findings showed that the artificial neural network model had the best performance among all the studied models, indicating the discrimination power of this model in predicting patient survival compared to the other models.

Introduction: Lung cancer is the most common cancer in terms of prevalence and mortality. The cancer can be detected once it is reached to a stage that is visible in the CT imaging. Eighty six percent of the patients with lung cancer because they are late understand their disease, surgery has little effect on their improvement. Therefore, the existence of an intelligent system that can detect lung cancer in the early stages is necessary. Methods: In this study, a lung cancer dataset of UCI database was used. This dataset consists of 32 samples, 57 variables and 3 classes (each class including 10, 9 and 13 samples). The data were normalized within the range 0 to 1.Then, to increase the detection speed, the dimensions of the data were reduced by using the Principal Components Analysis (PCA). Then, using a multilayer perceptron neural network, a model for classification and prediction of lung cancer was developed. Finally, the performance of the model was measured using sensitivity, specificity, positive predictive value and negative predictive value. It should be noted that all analyzes were done using Weka software. Results: After developing and evaluating an artificial neural network model, the developed model had a sensitivity of 66.7%, a 98.5% specificity, a positive predictive value of 75%, and a negative predictive value of 97.7%. Conclusion:In intelligent diagnostic systems, in addition to high accuracy of diagnosis, the speed of diagnosis and decision making is also important. Therefore, researchers increased the speed of the prediction model by reducing 57 variables to 8 variables using PCA. Also, the high sensitivity and high specificity of developed model demonstrates high power of artificial neural network model in detecting lung cancer.

Introduction: The Disease Registry records and maintains the necessary data for each individual patient according to the purpose for which it was designed. These data are widely used to calculate statistical indicators, resource management, resource allocation, and clinical research. One of the main challenges in using disease registries is their low quality data. Incompleteness, inaccuracy and untimeliness are some of the problems with the quality of the data in the disease registries. In this study, the researchers reviewed the views of some medical specialists on ways to improve the quality of data in the diseases registries.In this study, researchers looked at the views of some medical specialist who were in charge of maintaining a disease registry on ways to improve the quality of data in disease registries. Methods:The qualitative method of in-depth interviews was used to explore the views of medical specialist. They were specialist doctors who were responsible for keeping a disease registry in their specialty. All interviews were conducted by one of the researchers. All interviews were conducted by one of the researchers and the interviews were digitally recorded during the interview. After the interview, the sounds recorded by the researcher were written in the form of text and the themes discussed in them were coded and analyzed. Eventually the coding was reviewed by another researcher, and the opposing opinions of the researchers were resolved through the discussion. Results: The number of medical specialists interviewed was 6, which all of them was internist. The minimum and maximum interview time was 15 and 35 minutes respectively. The most important solutions to improve the quality of data in the diseases registries are: utilization of software systems (suggested by all doctors), utilization of coded data like icd-10 code (suggested by 5 doctors) and connecting disease registries to hospital information systems and other health information system (suggested by 3 doctors). Conclusion: According to the views of the medical specialists who participated in present study, the use of IT-based methods as well as information management methods is the best way to improve the quality of data in the registry.