scholarly journals Data Mining Algorithms for Pharmacovigilance

2019 ◽  
Vol 9 (1S) ◽  
pp. 81-85
Keyword(s):  
Data Mining ◽  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Classification Accuracy ◽  
Specific Drug ◽  
Data Mining Algorithms ◽  
Good Classification ◽  
Mining Algorithms ◽  
Aided Diagnosis

In this paper, various data mining algorithms for pharmacovigilance is analyzed and a decision support system for hospital is proposed.. Overall analysis of adverse events of a specific drug helps in finding the potential danger of using the specific drug. Decision support system with good classification accuracy to improve its use in hospital for computer aided diagnosis by doctors is also analyzed,

scholarly journals ПРОГНОЗУВАННЯ ТА ПРИЙНЯТТЯ РІШЕНЬ В УМОВАХ НЕВИЗНАЧЕНОСТІ В ПРОБЛЕМАХ СИСТЕМНИХ МЕДИЧНИХ ДОСЛІДЖЕНЬ

2017 ◽  
Author(s):  
V. P. Martsenyuk ◽  
I Ye. Andrushchak
Keyword(s):  
Data Mining ◽  
Support System ◽  
System Analysis ◽  
Medical System ◽  
Information Support ◽  
System Research ◽  
Data Mining Algorithms ◽  
Analysis Methods ◽  
Mining Algorithms ◽  
Information Support System

The work presents our results in field of application of system analysis methods to problem of medical research. We emphasize effects of uncertainty that should be taken into account in such complex processes. Medical system research requires information support system implementing data mining algorithms resulting in decision trees or IF-THEN rules. Besides that such system should be object-oriented and web-integrated.The aim of this study was to develop information support system based on data mining algorithms applied to system analysis method for medical system research. System analysis methods were used for qualitative analysis of diseases mathematical models. Algorithms such as decision tree induction and sequential covering algorithm were applied for data mining from learning data set.We observed the complex qualitative behavior of population and diseases models depending on parameters and controllers even without considering probabilistic nature of the most of quantities and parameters of information models.

Knowledge Discovery and Data Mining Applications in the Healthcare Industry

2015 ◽  
pp. 241-262
Author(s):  
Iman Barazandeh ◽  
Mohammad Reza Gholamian
Keyword(s):  
Data Mining ◽  
Decision Support ◽  
Decision Support System ◽  
Knowledge Discovery ◽  
Support System ◽  
Healthcare Industry ◽  
Actionable Knowledge ◽  
System View ◽  
Data Market

The healthcare industry is one of the most attractive domains to realize the actionable knowledge discovery objectives. This chapter studies recent researches on knowledge discovery and data mining applications in the healthcare industry and proposes a new classification of these applications. Studies show that knowledge discovery and data mining applications in the healthcare industry can be classified to three major classes, namely patient view, market view, and system view. Patient view includes papers that performed pure data mining on healthcare industry data. Market view includes papers that saw the patients as customers. System view includes papers that developed a decision support system. The goal of this classification is identifying research opportunities and gaps for researchers interested in this context.

A Decision Support System for Classification of Normal and Medical Renal Disease Using Ultrasound Images

2017 ◽  
Vol 8 (2) ◽  
pp. 52-69 ◽  
Author(s):  
Komal Sharma ◽  
Jitendra Virmani
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Renal Disease ◽  
Support System ◽  
Classification Accuracy ◽  
Feature Vector ◽  
Texture Feature ◽  
Renal Ultrasound ◽  
Ultrasound Images ◽  
Feature Vectors

Early detection of medical renal disease is important as the same may lead to chronic kidney disease which is an irreversible stage. The present work proposes an efficient decision support system for detection of medical renal disease using small feature space consisting of only second order GLCM statistical features computed from raw renal ultrasound images. The GLCM mean feature vector and GLCM range feature vector are computed for inter-pixel distance d varying from 1 to 10. These texture feature vectors are combined in various ways yielding GLCM ratio feature vector, GLCM additive feature vector and GLCM concatenated feature vector. The present work explores the potential of five texture feature vectors computed using GLCM statistics exhaustively for differential diagnosis between normal and MRD images using SVM classifier. The result of the study indicates that GLCM range feature vector computed with d = 1 yields the highest overall classification accuracy of 85.7% with individual classification accuracy values of 93.3% and 77.9% for normal and MRD classes respectively.

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