2012
Vicente, H.; Dias, S.; Fernandes, A.; Abelha, A.; MacHado, J.; Neves, J.
Prediction of the quality of public water supply using artificial neural networks Journal Article
Em: Journal of Water Supply: Research and Technology - AQUA, vol. 61, não 7, pp. 446-459, 2012, ISSN: 00037214, (cited By 38).
Resumo | Links | BibTeX | Etiquetas: Alentejo; Portugal, artificial neural network; concentration (composition); monitoring; numerical model; physicochemical property; prediction; water quality; water supply, Chemical parameters; Formal model; Mean absolute deviations; Mean squared error; Network structures; Public water supply; Sampling frequencies; Surveillance program; Test sets; Training sets; Water quality parameters, Forecasting; Manganese; Neural networks; Nitrates; pH; Potassium; Sodium; Topology; Water quality; Water supply, Quality control
@article{Vicente2012446,
title = {Prediction of the quality of public water supply using artificial neural networks},
author = {H. Vicente and S. Dias and A. Fernandes and A. Abelha and J. MacHado and J. Neves},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84872189812&doi=10.2166%2faqua.2012.014&partnerID=40&md5=21439d46a149b88ee4c82a7bee0e14ce},
doi = {10.2166/aqua.2012.014},
issn = {00037214},
year = {2012},
date = {2012-01-01},
journal = {Journal of Water Supply: Research and Technology - AQUA},
volume = {61},
number = {7},
pages = {446-459},
abstract = {The Health Surveillance Program was established by the Regional Health Authority of Alentejo to control the quality of public water supply. This authority divides the water quality parameters into three distinct groups, namely P1(pH and conductivity), P2 (nitrate and manganese) and P3 (sodium and potassium), for which the sampling frequency is dissimilar. Thus, the development of formal models is essential to predict the chemical parameters included in group P2 and included in group P 3, for which the sampling frequency is lower, based on the chemical parameters included in group P1. In the present work, artificial neural networks (ANNs) were used to predict the concentration of nitrate, manganese, sodium and potassium from pH and conductivity. Different network structures have been elaborated and evaluated using the mean absolute deviation and the mean squared error. The ANN selected to predict the concentration of nitrate, sodium and potassium from pH and conductivity has a 2-18-14-3 topology while the network selected to predict the concentration of nitrate and manganese has a 2-19-10-2 topology. A good match between the observed and predicted values was observed with the R2 values varying in the range 0.9960-0.9989 for the training set and 0.9993-0.9952 for the test set. © IWA Publishing 2012.},
note = {cited By 38},
keywords = {Alentejo; Portugal, artificial neural network; concentration (composition); monitoring; numerical model; physicochemical property; prediction; water quality; water supply, Chemical parameters; Formal model; Mean absolute deviations; Mean squared error; Network structures; Public water supply; Sampling frequencies; Surveillance program; Test sets; Training sets; Water quality parameters, Forecasting; Manganese; Neural networks; Nitrates; pH; Potassium; Sodium; Topology; Water quality; Water supply, Quality control},
pubstate = {published},
tppubtype = {article}
}
The Health Surveillance Program was established by the Regional Health Authority of Alentejo to control the quality of public water supply. This authority divides the water quality parameters into three distinct groups, namely P1(pH and conductivity), P2 (nitrate and manganese) and P3 (sodium and potassium), for which the sampling frequency is dissimilar. Thus, the development of formal models is essential to predict the chemical parameters included in group P2 and included in group P 3, for which the sampling frequency is lower, based on the chemical parameters included in group P1. In the present work, artificial neural networks (ANNs) were used to predict the concentration of nitrate, manganese, sodium and potassium from pH and conductivity. Different network structures have been elaborated and evaluated using the mean absolute deviation and the mean squared error. The ANN selected to predict the concentration of nitrate, sodium and potassium from pH and conductivity has a 2-18-14-3 topology while the network selected to predict the concentration of nitrate and manganese has a 2-19-10-2 topology. A good match between the observed and predicted values was observed with the R2 values varying in the range 0.9960-0.9989 for the training set and 0.9993-0.9952 for the test set. © IWA Publishing 2012.
2010
Duarte, J.; Salazar, M.; Quintas, C.; Santos, M.; Neves, J.; Abelha, A.; Machado, J.
Data quality evaluation of electronic health records in the hospital admission process Proceedings Article
Em: pp. 201-206, Yamagata, 2010, ISBN: 9780769541471, (cited By 23; Conference of 9th IEEE/ACIS International Conference on Computer and Information Science, ICIS 2010 ; Conference Date: 18 August 2010 Through 20 August 2010; Conference Code:82351).
Resumo | Links | BibTeX | Etiquetas: Candidate solution; Complex entities; Critical problems; Data quality; Electronic health record; Extended logic programming; Hospital admissions; Hospital settings; Interoperations; Logic programs; Quality of information; Virtual worlds, Health; Hospitals; Information science; Logic programming; Records management; Social networking (online); Virtual reality, Quality control
@inproceedings{Duarte2010201,
title = {Data quality evaluation of electronic health records in the hospital admission process},
author = {J. Duarte and M. Salazar and C. Quintas and M. Santos and J. Neves and A. Abelha and J. Machado},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-78649311213&doi=10.1109%2fICIS.2010.97&partnerID=40&md5=39a72e5691605095d85a94bfad5f005f},
doi = {10.1109/ICIS.2010.97},
isbn = {9780769541471},
year = {2010},
date = {2010-01-01},
journal = {Proceedings - 9th IEEE/ACIS International Conference on Computer and Information Science, ICIS 2010},
pages = {201-206},
address = {Yamagata},
abstract = {Data Quality Evaluation is a critical problem, specially in Healthcare, where people may take decisions based on confident, acceptable and secure information. In this paper we show how data quality can be evaluated from electronic health records, in particular in a hospital setting. We construct a dynamic virtual world of complex entities or agents, driven by one criterion alone, intelligence, for the provision of healthcare. This virtual world will witness the emergence and will be based on a versatile and powerful paradigm, where the candidate solutions (here understood as agents) are seen as evolutionary logic programs or theories, being the test if a solution is optimal based on a measure of the quality-of-information that stems from them. © 2010 IEEE.},
note = {cited By 23; Conference of 9th IEEE/ACIS International Conference on Computer and Information Science, ICIS 2010 ; Conference Date: 18 August 2010 Through 20 August 2010; Conference Code:82351},
keywords = {Candidate solution; Complex entities; Critical problems; Data quality; Electronic health record; Extended logic programming; Hospital admissions; Hospital settings; Interoperations; Logic programs; Quality of information; Virtual worlds, Health; Hospitals; Information science; Logic programming; Records management; Social networking (online); Virtual reality, Quality control},
pubstate = {published},
tppubtype = {inproceedings}
}
Data Quality Evaluation is a critical problem, specially in Healthcare, where people may take decisions based on confident, acceptable and secure information. In this paper we show how data quality can be evaluated from electronic health records, in particular in a hospital setting. We construct a dynamic virtual world of complex entities or agents, driven by one criterion alone, intelligence, for the provision of healthcare. This virtual world will witness the emergence and will be based on a versatile and powerful paradigm, where the candidate solutions (here understood as agents) are seen as evolutionary logic programs or theories, being the test if a solution is optimal based on a measure of the quality-of-information that stems from them. © 2010 IEEE.
Ribeiro, J.; Abelha, A.; Machado, J.; Marques, A.; Neves, J.
The inference process with quality evaluation in healthcare environments Proceedings Article
Em: pp. 183-188, Yamagata, 2010, ISBN: 9780769541471, (cited By 1; Conference of 9th IEEE/ACIS International Conference on Computer and Information Science, ICIS 2010 ; Conference Date: 18 August 2010 Through 20 August 2010; Conference Code:82351).
Resumo | Links | BibTeX | Etiquetas: Computer software selection and evaluation; Decision making; Decision support systems; Decision theory; Information science; Intelligent systems; Knowledge representation; Logic programming, Quality control
@inproceedings{Ribeiro2010183,
title = {The inference process with quality evaluation in healthcare environments},
author = {J. Ribeiro and A. Abelha and J. Machado and A. Marques and J. Neves},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-78649268622&doi=10.1109%2fICIS.2010.160&partnerID=40&md5=ea6e75d4229d65da60c4f998202906a8},
doi = {10.1109/ICIS.2010.160},
isbn = {9780769541471},
year = {2010},
date = {2010-01-01},
journal = {Proceedings - 9th IEEE/ACIS International Conference on Computer and Information Science, ICIS 2010},
pages = {183-188},
address = {Yamagata},
abstract = {Intelligent Systems require the ability to reason with incomplete information, because in the real world complete information is hard to obtain, even in the most controlled situation. In recent years, many formalisms have been proposed tacking the matter of uncertain, incomplete in logic programs and databases. However, qualitative models and qualitative reasoning have been around in Artificial Intelligence research for some time, in particular due the growing need to offer support in decision-making processes. The evaluation of knowledge that stems out from logic programs becomes a point of research. The Quality-of-Information concept demonstrated their applicability in many dynamic environments and for decision making purposes. In this paper we present an illustrative example of the inference process in decisions in healthcare environments. Under the Extended Logic Programming paradigm to knowledge representation and reasoning, we present the evolutive perspective of the inference process to achieve logical programs (or theories) corresponding to the best theorems to solve a problem or take a decision. For the evaluation of the best theories we use a quantification of the quality-of-information that stems out from a logic program. © 2010 IEEE.},
note = {cited By 1; Conference of 9th IEEE/ACIS International Conference on Computer and Information Science, ICIS 2010 ; Conference Date: 18 August 2010 Through 20 August 2010; Conference Code:82351},
keywords = {Computer software selection and evaluation; Decision making; Decision support systems; Decision theory; Information science; Intelligent systems; Knowledge representation; Logic programming, Quality control},
pubstate = {published},
tppubtype = {inproceedings}
}
Intelligent Systems require the ability to reason with incomplete information, because in the real world complete information is hard to obtain, even in the most controlled situation. In recent years, many formalisms have been proposed tacking the matter of uncertain, incomplete in logic programs and databases. However, qualitative models and qualitative reasoning have been around in Artificial Intelligence research for some time, in particular due the growing need to offer support in decision-making processes. The evaluation of knowledge that stems out from logic programs becomes a point of research. The Quality-of-Information concept demonstrated their applicability in many dynamic environments and for decision making purposes. In this paper we present an illustrative example of the inference process in decisions in healthcare environments. Under the Extended Logic Programming paradigm to knowledge representation and reasoning, we present the evolutive perspective of the inference process to achieve logical programs (or theories) corresponding to the best theorems to solve a problem or take a decision. For the evaluation of the best theories we use a quantification of the quality-of-information that stems out from a logic program. © 2010 IEEE.