Kalantari P, Sepehri H, Akbari Mt, Osati Ashtiani Z, Behjati F,
Volume 59, Issue 3 (8 2001)
Abstract
In this study, chromosome analyses were performed on 70 infertile Azoospermic and Oligospermic (<20 million/ml) men, and also cultures of peripheral blood lymphocytes by high resolution banding method were analysed as well. It is revealed 8 (11.43 percent) men with chromosomal abnormality. There were 31.4 percent patients with azoospermia and 68.6 percent with oligospermia from several thousands to 20×10^6 million/ml and their duration of infertility was at least 2 years. All patients with numerical chromosome anomalies had azoospermia and the most frequent anomaly was 47, XXY chromosomal constitution (klinfelter's syndrome), found in 8.57 percent of patients. We found that chromosomal anomalies found in this study were sex chromosome anomalies and an increased rate of numerical chromosomal abnormalities was among men with azoospermia. As a conclusion, we suggest that all men with azoospermia be considered for cytogenetical evaluation.
Mohammad Mehdi Sepehri , Parisa Rahnama , Pejman Shadpour , Babak Teimourpour ,
Volume 67, Issue 6 (9-2009)
Abstract
Background: Data mining as a multidisciplinary field is rooted in the fields such as statistics, mathematics, computer science and artificial intelligence and has been gaining momentum in scientific, managerial, and executive applications in health care. Data mining can be defined as the automated extraction of valuable, practical and hidden knowledge and information from large data. Applying data mining in medical records and data is of utmost importance for health care givers and providers and brings vital and valuable outcomes. Data mining can help doctors come up with better recommendations and plans for treatment which actually in many respects have significant impact on patients’ life and satisfaction In this paper we have proposed and utilized data mining methods to extract hidden information in medical records of pelvis stone patients with ureteral stone. We have tried to design a decision support system model to be applicable for selecting type of treatment for these groups of patients.
Methods: We gathered needed information from Shahid Hashemi Nejad hospital. In this research we have used decision tree as a data mining tool, for selecting suitable treatment for patients with ureteral stone. This model can predict probability of success of each treatment.
Results: In this research we extracted effective attributes in selecting type of treatment for patients with ureteral stone.
Conclusions: By using this model we can have eight percent improvement in number of patients who have stone free output after treating. In fact, this model has a better functionality than expert system of hospitals.
