Creating and Using the Knowledge Archive in the Internet Medical Consultant for Decision Support at the Point of Care

Nakic, Drasko; Loskovska, Suzana

doi:10.4018/jehmc.2012070106

Cited by 4 publications

(1 citation statement)

References 12 publications

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“…The researchers in the medical field identify and predict the diseases besides proffering effective care for patients (Palaniappan & Awang, 2008;Lemke & Mueller, 2003) with the aid of data mining techniques. The data mining techniques have been utilized by a wide variety of works in the literature to diagnose various diseases including: Diabetes, Hepatitis, Cancer, Heart diseases and the like (Gorunescu, 2008;Bellaachia & Guven, 2006;Nakić & Loškovska, 2012). Information associated with the disease, prevailing in the form of electronic clinical records, treatment information, gene expressions, images and more; were employed in all these works.…”

Section: Introductionmentioning

confidence: 99%

Heart Disease Diagnosis Using Fuzzy Supervised Learning Based on Dynamic Reduced Features

Moudani

Hussein

abdelRazzak

et al. 2014

International Journal of E-Health and Medical Communications

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International audienceThe health industry collects huge amounts of health data which, unfortunately, are not mined to discover hidden information. However, there is a lack of effective analytical tools to discover hidden relationships and trends in data. Information technologies can provide alternative approaches to the diagnosis of the heart attach disease. In this study, a proficient methodology for the extraction of significant patterns from the Coronary Heart Disease warehouses for heart attack prediction, which unfortunately continues to be a leading cause of mortality in the whole world, has been presented. For this purpose, we propose to develop an innovative fuzzy classification solution approach based on dynamic reduced sets of potential risk factors using the promising Rough Set theory which is a new mathematical approach to data analysis based on classification of objects. Therefore, we propose to validate the classification using Multi-classifier decision tree to identify the risky heart disease cases. This work is based on a dataset collected from several clinical institutions based on the medical profile of patient. Moreover, the experts’ knowledge in this field has been taken into consideration in order to define the disease, its risk factors, to follow up the issue results, and to establish significant knowledge relationships between medical factors related to Coronary Heart Disease. To identify cases of heart attack, experiments of several classification techniques have been performed leading to rank the suitable techniques. The reduction of potential risk factors contributes to enumerate dynamically one or more optimal subsets of the potential risk factors of high interest which implicitly leads to reduce the complexity of the classification problems while maintaining the prediction classification quality. The performance of the proposed model is analyzed and evaluated based on set of benchmark techniques applied in this classification problem

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Section: Introductionmentioning

confidence: 99%

Heart Disease Diagnosis Using Fuzzy Supervised Learning Based on Dynamic Reduced Features

Moudani

Hussein

abdelRazzak

et al. 2014

International Journal of E-Health and Medical Communications

View full text Add to dashboard Cite

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Heart Disease Diagnosis Using Fuzzy Supervised Learning Based on Dynamic Reduced Features

Moudani

Hussein

abdelRazzak

et al. 2019

Coronary and Cardiothoracic Critical Care

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The health industry collects huge amounts of health data which, unfortunately, are not mined to discover hidden information. However, there is a lack of effective analytical tools to discover hidden relationships and trends in data. Information technologies can provide alternative approaches to the diagnosis of the heart attach disease. In this study, a proficient methodology for the extraction of significant patterns from the Coronary Heart Disease warehouses for heart attack prediction, which unfortunately continues to be a leading cause of mortality in the whole world, has been presented. For this purpose, we propose to develop an innovative fuzzy classification solution approach based on dynamic reduced sets of potential risk factors using the promising Rough Set theory which is a new mathematical approach to data analysis based on classification of objects. Therefore, we propose to validate the classification using Multi-classifier decision tree to identify the risky heart disease cases. This work is based on a dataset collected from several clinical institutions based on the medical profile of patient. Moreover, the experts' knowledge in this field has been taken into consideration in order to define the disease, its risk factors, to follow up the issue results, and to establish significant knowledge relationships between medical factors related to Coronary Heart Disease. To identify cases of heart attack, experiments of several classification techniques have been performed leading to rank the suitable techniques. The reduction of potential risk factors contributes to enumerate dynamically one or more optimal subsets of the potential risk factors of high interest which implicitly leads to reduce the complexity of the classification problems while maintaining the prediction classification quality. The performance of the proposed model is analyzed and evaluated based on set of benchmark techniques applied in this classification problem.

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Combining Artificial Intelligence and NetMedicine for Ambient Assisted Living

Sernani

Claudi

Dragoni

2020

Natural Language Processing

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World population is shifting towards older ages: according to recent estimates there will be 1.5 billion people over 65 years old in 2050. Local governments, international institutions, care organizations and industry are fostering the research community to find solutions to face the unprecedented challenges raised by population ageing. A combination of Artificial Intelligence and NetMedicine could be ideal to face these challenges: they provide the means to develop an intelligent system and simultaneously to distribute it over a network, allowing the communication over the internet, if needed. Hence, the authors present a Multi-Agent Architecture for Ambient Assisted Living (AAL): it is the model for a system to manage a distributed sensor network composed by ambient and biometric sensors. The system should analyse data and pro-actively decide to trigger alarms if anomalies are detected. The authors tested the architecture implementing a prototypical Multi-Agent System (MAS), based on Belief-Desire-Intention (BDI) paradigm: the Virtual Carer.

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Creating and Using the Knowledge Archive in the Internet Medical Consultant for Decision Support at the Point of Care

Cited by 4 publications

References 12 publications

Heart Disease Diagnosis Using Fuzzy Supervised Learning Based on Dynamic Reduced Features

Heart Disease Diagnosis Using Fuzzy Supervised Learning Based on Dynamic Reduced Features

Heart Disease Diagnosis Using Fuzzy Supervised Learning Based on Dynamic Reduced Features

Combining Artificial Intelligence and NetMedicine for Ambient Assisted Living

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