Frontiers in Health Informatics
Volume:2 Issue: 4, 2013

cardiovascular diseases are among the most common diseases in all societies. The most important step in minimizing myocardial infarction and its complications is to minimize its risk factors. The amount of medical data is increasingly growing. Medical data mining has a great potential for transforming these data into information. Using data mining techniques to generate predictive models for identifying those at risk for reducing the effects of the disease is very helpful. The present study aimed to collect data related to risk factors of heart infarction from patients’ medical record and developed predicting models using data mining algorithm. The present work was an analytical study conducted on a database containing 350 records. Data were related to patients admitted to Shahid Rajaei specialized cardiovascular hospital, Iran, in 2011. Data were collected using a four-sectioned data collection form. Data analysis was performed using SPSS and Clementine version 12. Seven predictive algorithms and one algorithm-based model for predicting association rules were applied to the data. Accuracy, precision, sensitivity, specificity, as well as positive and negative predictive values were determined and the final model was obtained. Based on the association rules, five parameters, including hypertension, DLP, tobacco smoking, diabetes, and A+ blood group, were the most critical risk factors of myocardial infarction. Among the models, the neural network model was found to have the highest sensitivity, indicating its ability to successfully diagnose the disease. However, since we were not able to determine the rational of this model, and considering the fact that our major goal was to present a model with the highest precision which is also applicable for clinical purposes, the CHAID model with an overall precision of 93.4%was selected as the final model. Risk prediction models have great potentials in facilitating the management of a patient with a specific disease. Therefore, health interventions or change in their life style can be conducted based on these models for improving the health conditions of the individuals at risk.

Due to the information systems` objectives, and to avoid duplication and to help improve care quality and reduce cost, it is necessary to conduct continuous evaluation to determine how to achieve these goals. This study was performed using evaluation indices of hospital Information systems in selected hospitals of Iran in 2012. In this article organizational and server components of hospital information systems in selected hospitals are being assessed. This research is a descriptive cross – sectional study. The study population consisted of the information system of Shohaday Tajrish, Khatamolanbiya, Imam Khomeini and Milad Hospital. Data collecting tools were checklist of hospital information system Evaluation Index, which completed with direct observation and interviews with users. Data analyzed by statistics software SPSS, and presented as statistical tables and graphs. In the studied hospitals, although the most of the organizational components subgroups and hospital information system server components has been set up and used but pharmacy information system, decision support systems, communication services and telemedicine services hadn`t been set up fully in the hospitals. Currently most subtypes of organizational components and hospital information system server components were fully in the designed software and considering all fields in 5 hospitals.

Clinical Decision Support Systems (CDSSs) are a kind of Information Systems for improving clinical decisions. Different models of CDSSs can be used in alerts of critical values, reminders of overdue preventive health tasks, advice for drug prescribing, critiques of existing health care orders, and suggestions for various active care issues.There are different methods for developing these systems that Case Base Reasoning (CBR) is one of these methods. CBR provides subjective knowledge with applying previous cases. The underlying idea in CBR is the assumption that similar problems have similar solutions. CBR has some limitations as others methods that Many researches dedicated to solve the limitations. This study discuss about CBR and using it in medical sciences.