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Designing Expert System for Detecting of High Risk population by Pathological Report

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Year: 2016
COI code: ACPLMED18_038
Paper Language: English

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Authors Designing Expert System for Detecting of High Risk population by Pathological Report

  Elham Maserat - Department of Health information Technology, Faculty of Management and medical informatics, Tabriz University of Medical Sciences, Tabriz 51368, Iran
  Reza Safdari - Professor of Allied Medical Sciences School, Tehran University of Medical Sciences, Tehran, Iran
  Mohammad reza zali - Head of Research institute of Gastroenterology and Liver Diseases, Shahid Beheshti University of Medical Science, Tehran, Iran

Abstract:

Introduction: Colorectal cancer (CRC) is the most common malignancy and major cause of morbidity and mortality throughout the world. CRC is the sixth leading cause of cancer death in Iran. Determining of risk groups of CRC by computer based system facilitate decision making and estimate cancer statue by pathological report. We aimed to present expert system for detecting of high risk population by pathological Report. Methods: Three courtiers (Canada, Australia and United States) were selected from 25 countries that are member in the international Cancer Screening Network (ICSN). National guidelines of colorectal cancer screening were approved in the next step. The third phase was estimating of survival rate of covered populations. The Naive Bayes classifier was selected as one of the data mining technique for estimating. Finally intelligent hybrid decision support system was implemented. Programming language of designed web base system is Javascript. An integrated development environment (IDE) and database of system are respectively Jetbrain webstorm and MySQL. Results: Expert system was detected heretical cancers by pathologic reports. Computer based system was surveyed MSI and IHC by pathological guidelines. System was detected 312 cases with HNPCC (Hereditary nonpolyposis colorectal cancer) and 92 cases with FAP (FamilialAdenomatous Polyposis). The high risk group consists of 8 subgroups (FAP, AFAP, Suspected FAP, Suspected FAP, HNPCC, Suspected HNPCC, MYH, IBD). If a suspected HNPCC patient have a mutation that is identified by molecular genetic testing, intelligent clinical changes a suspected HNPCC patient status to HNPCC patient by result of pathological testing . Patient with abnormal IHC and high MSI was introducing as a HNPCC patient by guidelines of expert system. Expert clinical decision support system enables to update all of information and consensus in real time. Conclusions: Expert system has key role as an effective approach for cancer management and early detection. This system can be used for screening program and risk assessment plans.

Keywords:

Expert System, High Risk population, Pathological Report

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COI code: ACPLMED18_038

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Maserat, Elham; Reza Safdari & Mohammad reza zali, 2016, Designing Expert System for Detecting of High Risk population by Pathological Report, 18th Annual Conference and the International Congress of Pathology and Laboratory Medicine, تهران, انجمن آسيب شناسي ايران, https://www.civilica.com/Paper-ACPLMED18-ACPLMED18_038.htmlInside the text, wherever referred to or an achievement of this article is mentioned, after mentioning the article, inside the parental, the following specifications are written.
First Time: (Maserat, Elham; Reza Safdari & Mohammad reza zali, 2016)
Second and more: (Maserat; Safdari & zali, 2016)
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Type: Medical University
Paper No.: 2416
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