2015
Fernandes, F.; Vicente, H.; Abelha, A.; Machado, J.; Novais, P.; Neves, J.
Artificial neural networks in diabetes control Proceedings Article
Em: pp. 362-370, Institute of Electrical and Electronics Engineers Inc., 2015, ISBN: 9781479985470, (cited By 53; Conference of Science and Information Conference, SAI 2015 ; Conference Date: 28 July 2015 Through 30 July 2015; Conference Code:117981).
Resumo | Links | BibTeX | Etiquetas: Clinical practices; Degree of confidence; Diabetes management; Diabetes mellitus; Diagnosis support systems; Formal framework; Knowledge representation and reasoning; Quality of information, Complex networks; Computation theory; Diagnosis; Health risks; Knowledge representation; Logic programming; Neural networks; Program diagnostics; Reconfigurable hardware, Computer circuits
@inproceedings{Fernandes2015362,
title = {Artificial neural networks in diabetes control},
author = {F. Fernandes and H. Vicente and A. Abelha and J. Machado and P. Novais and J. Neves},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84957802910&doi=10.1109%2fSAI.2015.7237169&partnerID=40&md5=0dc04aa71077b0100cbb96cd215d2473},
doi = {10.1109/SAI.2015.7237169},
isbn = {9781479985470},
year = {2015},
date = {2015-01-01},
journal = {Proceedings of the 2015 Science and Information Conference, SAI 2015},
pages = {362-370},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
abstract = {Diabetes Mellitus is now a prevalent disease in both developed and underdeveloped countries, being a major cause of morbidity and mortality. Overweight/obesity and hypertension are potentially modifiable risk factors for diabetes mellitus, and persist during the course of the disease. Despite the evidence from large controlled trials establishing the benefit of intensive diabetes management in reducing microvasculars and macrovasculars complications, high proportions of patients remain poorly controlled. Poor and inadequate glycemic control among patients with Type 2 diabetes constitutes a major public health problem and a risk factor for the development of diabetes complications. In clinical practice, optimal glycemic control is difficult to obtain on a long-term basis, once the reasons for feebly glycemic control are complex. Therefore, this work will focus on the development of a diagnosis support system, in terms of its knowledge representation and reasoning procedures, under a formal framework based on Logic Programming, complemented with an approach to computing centred on Artificial Neural Networks, to evaluate the Diabetes states and the Degree-of-Confidence that one has on such a happening. © 2015 IEEE.},
note = {cited By 53; Conference of Science and Information Conference, SAI 2015 ; Conference Date: 28 July 2015 Through 30 July 2015; Conference Code:117981},
keywords = {Clinical practices; Degree of confidence; Diabetes management; Diabetes mellitus; Diagnosis support systems; Formal framework; Knowledge representation and reasoning; Quality of information, Complex networks; Computation theory; Diagnosis; Health risks; Knowledge representation; Logic programming; Neural networks; Program diagnostics; Reconfigurable hardware, Computer circuits},
pubstate = {published},
tppubtype = {inproceedings}
}
Diabetes Mellitus is now a prevalent disease in both developed and underdeveloped countries, being a major cause of morbidity and mortality. Overweight/obesity and hypertension are potentially modifiable risk factors for diabetes mellitus, and persist during the course of the disease. Despite the evidence from large controlled trials establishing the benefit of intensive diabetes management in reducing microvasculars and macrovasculars complications, high proportions of patients remain poorly controlled. Poor and inadequate glycemic control among patients with Type 2 diabetes constitutes a major public health problem and a risk factor for the development of diabetes complications. In clinical practice, optimal glycemic control is difficult to obtain on a long-term basis, once the reasons for feebly glycemic control are complex. Therefore, this work will focus on the development of a diagnosis support system, in terms of its knowledge representation and reasoning procedures, under a formal framework based on Logic Programming, complemented with an approach to computing centred on Artificial Neural Networks, to evaluate the Diabetes states and the Degree-of-Confidence that one has on such a happening. © 2015 IEEE.