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 Table of Contents  
EDITORIAL
Year : 2016  |  Volume : 14  |  Issue : 1  |  Page : 7

The future medicine


Gastroenterology and Hepatology (Tropical Medicine), Faculty of Medicine, Al-Azhar University, Assiut, Egypt; Liver Transplant Department at UCLA, Los Angeles, California, USA

Date of Submission10-Nov-2015
Date of Acceptance12-Nov-2015
Date of Web Publication18-Apr-2016

Correspondence Address:
Abd E. M. Ali Hussein
Visiting Assistant Project Scientist, Liver Surgery and Transplant, UCLA, USA

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Source of Support: None, Conflict of Interest: None


DOI: 10.4103/1687-1693.180463

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How to cite this article:
Ali Hussein AE. The future medicine. Al-Azhar Assiut Med J 2016;14:7

How to cite this URL:
Ali Hussein AE. The future medicine. Al-Azhar Assiut Med J [serial online] 2016 [cited 2017 Jun 24];14:7. Available from: http://www.azmj.eg.net/text.asp?2016/14/1/7/180463

Several aspects of the management of patients with gastrointestinal and liver diseases have been investigated in the recent literature. Considering the peculiar pathophysiology of the disease progression or regression, multiple factors should be considered to reach the right diagnosis and correct management. Although our publications and scientific studies started in 2009 at the Kobe International Frontier Medical Center (KIFMC), Port Island, Japan for gastrointestinal, liver surgeries/endoscopy and liver transplant, here we present our experience in gastrointestinal tract, hepatology and liver transplantation using the novel computational analysis of data mining.

Analysing of our data by using data mining-computed program sheds light on the significant factors for each disease condition in the fields of hepatology and gastroenterology. Thus, the major challenge of biomedical data mining is to make these systems useful to biomedical researchers.

The decision tree analysis of all our publications in the American Journal of the Medical Sciences, USA; the European Journal of Gastroenterology and Hepatology, UK; Medicine, Baltimore, USA; and the Global Journal of Computer Sciences and Technology, USA was carried out using the Intelligent Miner software (Rapid Miner, Berlin, Germany). The best computational intelligent program of data mining software can automatically search a dataset to find the optimal classification variables, leading to the building of a decision tree algorithm. Briefly, all items derived from the patients were evaluated to determine which variables and cutoff points might produce the most dependent and independent factors for morbidity and mortality in each group or subgroup. Furthermore, the use of data mining can reform the overall quality of the present healthcare system [1],[2],[3],[4],[5],[6],[7],[8],[9].

Here, our group presented a manuscript stating that two-dimensional ultrasound can give predictive information regarding the treatment outcome before interferon therapy for hepatitis C virus-4 using data mining algorithms. Ultimately, such a study may give such a useful prediction before the therapy; furthermore, it would be repeated for the combination regimen of sofosbuvir/ribavirin with or without interferon therapy for those with hepatitis C virus G4; in addition, it can used for all new therapies.

I invite all readers to read the manuscript regarding the use of data mining technology, and also our previous publications. It is our belief that readers will come to know more about the new era of statistical computational analysis using data mining technology, and its advantages over the traditional mathematical analysis using Rapid I data mining.

 
  References Top

1.
Abd Elrazek MA, Mahfouz H, Afifi M, Nafady M, Fathy Ael W, El Azeem KA, et al. Detection of risky esophageal varices by two-dimensional ultrasound: when to perform endoscopy. Am J Med Sci 2014; 347 (1):28-33.  Back to cited text no. 1
    
2.
AEM Aly AbdElrazek, HM Mahfouz. Prediction analysis of esophageal variceal degrees using data mining: is validated in clinical medicine? Global J Comput Sci Tech 2013; 13:1–5.  Back to cited text no. 2
    
3.
Data mining for the masses, USA, 2008.  Back to cited text no. 3
    
4.
Bellazzi R, Zupan B. Predictive data mining in clinical medicine: current issues and guidelines. Int J Med Inform 2008; 77 (2):81–97.  Back to cited text no. 4
    
5.
Svenstrup D, Jørgensen HL, Winther O. Rare disease diagnosis: a review of web search, social media and large-scale data-mining approaches. Rare Dis 2015; 3 (1):e1083145.  Back to cited text no. 5
    
6.
Abd Elrazek AE, Bilasy SE, Elbanna AE, Elsherif AE. Prior to the oral therapy, what do we know about HCV-4 in Egypt: a randomized survey of prevalence and risks using data mining computed analysis. Medicine (Baltimore) 2014; 93 (28):e204  Back to cited text no. 6
    
7.
Abd Elrazek AE, Mahfouz HM, Metwally AM, El-Shamy AM. Mortality prediction of nonalcoholic patients presenting with upper gastrointestinal bleeding using data mining. Eur J Gastroenterol Hepatol 2014; 26 (2):187-191.  Back to cited text no. 7
    
8.
Abd Elrazek AE. Criticism of: diagnostic accuracy of abdominal ultrasound in the screening of esophageal varices in patients with cirrhosis. Eur J Gastroenterol Hepatol 2015; 27 (1):106–107.  Back to cited text no. 8
    
9.
Ali Hussein AE, Mahfouz H, Elazeem KA, Fakhry M, Elrazek EA, Foad M, et al, The Value of U/S to Determine Priority for Upper Gastrointestinal Endoscopy in Emergency Room. Medicine (Baltimore) 2015;94(49):e2241.  Back to cited text no. 9
    




 

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