A framework for decision making on teleexpertise with traceability of the reasoning

Doumbouya, Mamadou Bilo; Kamsu-Foguem, Bernard; Kenfack, Hugues; Foguem, Clovis

doi:10.1016/j.irbm.2014.09.002

Cited by 22 publications

(33 citation statements)

References 31 publications

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“…If the significant potentials of telemedicine to assist in the management guidance and outpatient care are to be realized, then research strategies need to be undertaken to improve disease diagnosis and monitoring. However, the modalities of such an approach should be clarified so as to allow greater security, flexibility and ergonomic functionalities [41] in responding to the pragmatic needs of medical procedures [42] and efficient use of experienced health care providers [43][44][45][46][47][48][49][50][51].…”

Section: Resultsmentioning

confidence: 99%

Knowledge-based modelling applied to synucleinopathies

Kamsu-Foguem

Tiako

Mutafungwa

et al. 2015

European Geriatric Medicine

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International audienceThe adoption of telemedicine technologies has enabled collaborative programs involving a variety of links among distributed medical structures and health officials and professionals. The use for telemedicine for transmission of medical data and the possibility for several distant physicians to share their knowledge on given medical cases provides clear benefits, but also raises several unsolved conceptual and technical challenges. The seamless exchange and access of medical information between medical structures, health professionals, and patients is a prerequisite for the harmonious development of this new medical practice. This paper proposes a new approach of semantic interoperability for enabling mutual understanding of terminologies and concepts used. The proposed semantic interoperability approach is based on conceptual graph to support collaborative activities by describing how different health specialists can apply appropriate strategies to eliminate differential medical diagnosis. Intelligent analysis strategies are used to narrow down and pinpoint medical disorders. The model proposed is fully verified by a case study in the context of elderly patients and specifically dealing with synucleinopathies, a group of neurodegenerative diseases that include Parkinson's disease (PD), dementia with Lewy bodies (DLB), pure autonomic failure (PAF) and multiple system atrophy (MSA)

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Section: Resultsmentioning

confidence: 99%

Knowledge-based modelling applied to synucleinopathies

Kamsu-Foguem

Tiako

Mutafungwa

et al. 2015

European Geriatric Medicine

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“…To control BSL, type2 diabetes patients are recommended to follow routine exercise and have healthy diet. Sometimes small amount of medication is also suggested to some patients 9,11 . Pregnant women are general candidate of gestational diabetes.…”

Section: Fig 1: Number Of People Affected With Diabetesmentioning

confidence: 99%

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2018

IJPSR

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Diabetes is one of the deadliest and non-contagious diseases that can adversely affect several parts of human body. Early prognosis of diabetes can inkling the grievous complications and help to save human life. Several researchers have used different data mining (Iterative Dichotomiser 3, Random Forest, Support Vector Machine, k-Nearest Neighbour, C4.5) and soft computing (Genetic Algorithm, Ant Colony Optimization, Particle Swarm optimization, Artificial Bee Colony) techniques to prospect diabetes in human beings. In last 10 years, C4.5 was the most preferred choice for mining diabetic patients. Likewise, in soft computing, maximum number of researchers have used genetic algorithm. Furthermore, the usage of pre-processing techniques is significantly increasing in diabetes diagnosis. It is also observed that rate of accuracy achieved in diagnosing diabetes using traditional data mining lies in 68.5%-95.3%. Likewise, the range for soft computing and their hybridized use lies in 74%-100%. In addition, rate of accuracy achieved using GA based hybridized approach is better than the accuracy obtained using PSO as well as ABC. Most of the researchers have used textual and numeric data for diabetes diagnosis. Few researchers have used images for the same. However, no significant research is found where diabetes has been diagnosed using audio or sound. Moreover, the diagnostic results obtained using image based data are not as good as obtained using textual or discrete data. Therefore, an attention is still obligatory to develop smart diabetes diagnostic system that can effectively work on different types of data like text, images as well as sound. INTRODUCTION: Diabetes is a persistent metabolic and pancreas affected human disorder. The devastating rise in obesity and sedentary life style has made it as a universal epidemic 1, 2. Numbers of individuals are getting affected by different types of diabetes. Human body changes most of the food into glucose and it is the primary source of energy in the body. Pancreas assists in digestion and releases a hormone called insulin that transforms glucose into body cells.

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“…These works differ in terms of telemedicine acts covered and methods used to practice telemedicine. Authors in [6] suggest a different method (conceptual graphs and argumentative logic) and focus on tele-expertise act. They propose a framework for the decision making process to ensure traceability in teleexpertise.…”

Section: State Of the Artmentioning

confidence: 99%

Telemedicine framework using case-based reasoning with evidences

Sene

Kamsu-Foguem

Rumeau

2015

Computer Methods and Programs in Biomedicine

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Telemedicine is the medical practice of information exchanged from one location to another through electronic communications to improve the delivery of health care services. This research article describes a telemedicine framework with knowledge engineering using taxonomic reasoning of ontology modeling and semantic similarity. In addition to being a precious support in the procedure of medical decision-making, this framework can be used to strengthen significant collaborations and traceability that are important for the development of official deployment of telemedicine applications. Adequate mechanisms for information management with traceability of the reasoning process are also essential in the fields of epidemiology and public health. In this paper we enrich the case-based reasoning process by taking into account former evidence-based knowledge. We use the regular four steps approach and implement an additional (iii) step: (i) establish diagnosis, (ii) retrieve treatment, (iii) apply evidence, (iv) adaptation, (v) retain. Each step is performed using tools from knowledge engineering and information processing (natural language processing, ontology, indexation, algorithm, etc.). The case representation is done by the taxonomy component of a medical ontology model. The proposed approach is illustrated with an example from the oncology domain. Medical ontology allows a good and efficient modeling of the patient and his treatment. We are pointing up the role of evidences and specialist's opinions in effectiveness and safety of care.

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A framework for decision making on teleexpertise with traceability of the reasoning

Cited by 22 publications

References 31 publications

Knowledge-based modelling applied to synucleinopathies

Knowledge-based modelling applied to synucleinopathies

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Telemedicine framework using case-based reasoning with evidences

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