pubs2024

@inproceedings{Morgado2017433,

title = {A case-based approach to colorectal cancer detection},

author = {P. Morgado and H. Vicente and A. Abelha and J. Machado and J. Neves and J. Neves},

editor = {Joukov N. Kim K.},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85016116954&doi=10.1007%2f978-981-10-4154-9_50&partnerID=40&md5=cd3473ba9d78ff27021ae76a22930f53},

doi = {10.1007/978-981-10-4154-9_50},

issn = {18761100},

year  = {2017},

date = {2017-01-01},

journal = {Lecture Notes in Electrical Engineering},

volume = {424},

pages = {433-442},

publisher = {Springer Verlag},

abstract = {Colorectal cancer is one of the most common malignancies in developed countries. Although it is not well known what causes this type of cancer, studies have showed that there are certain risk factors associated that may increase the likelihood of developing such malignancy. These factors comprise, among others, individual’s age, lifestyle habits, personal disease history, and genetic syndromes. Despite its high mortality, colorectal cancer may be prevented with an early diagnosis. Thus, this work aims at the development of Artificial Intelligence based decision support system to assess the risk of developing colorectal cancer. The framework is built on top of a Logic Programming approach to Knowledge Representation and Reasoning, complemented with a Case-based approach to computing that caters for the handling of incomplete, unknown, or even self-contradictory data, information or knowledge. © Springer Nature Singapore Pte Ltd. 2017.},

note = {cited By 2; Conference of 8th International Conference on Information Science and Applications, ICISA 2017 ; Conference Date: 20 March 2017 Through 23 March 2017; Conference Code:190019},

keywords = {Artificial intelligence; Case based reasoning; Computation theory; Computer circuits; Decision support systems; Diagnosis; Genetic programming; Knowledge representation; Logic programming; Risk assessment, Case-based approach; Colorectal cancer; Developed countries; Early diagnosis; Knowledge representation and reasoning; Risk factors, Diseases},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Duarte2009115,

title = {Agent-based group decision support in medicine},

author = {J. Duarte and M. Miranda and A. Abelha and M. Santos and J. Machado and J. Neves and C. Alberto and M. Salazar and C. Quintas and A. Ferreira and J. Neves},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-77957293677&partnerID=40&md5=6d7d94cef7bf789231dbb1e484549eb0},

isbn = {9781601321091},

year  = {2009},

date = {2009-01-01},

journal = {Proceedings of the 2009 International Conference on Artificial Intelligence, ICAI 2009},

volume = {1},

pages = {115-121},

address = {Las Vegas, NV},

abstract = {Cancer staging is the process by which physicians evaluate the spread of cancer. This is important, once in a good cancer staging system, the stage of disease helps to determine prognosis and assists in selecting therapies. A combination of physical examination, blood tests, and medical imaging is used to determine the clinical stage; if tissue is obtained via biopsy or surgery, examination of the tissue under a microscope can provide pathologic staging. On the other hand, good patient education may help to reduce health service costs and improve the quality of life of people with chronic or terminal conditions. In this paper it is endorsed a theoretical based model to support the provision of computer based information on cancer patients, and the computational techniques used to implement it. One's goal is to develop an interactive agent based computational system which may provide physicians with the right information, on time, that is adapted to the situation and process-based aspects of the patients's illness and treatment.},

note = {cited By 2; Conference of 2009 International Conference on Artificial Intelligence, ICAI 2009 ; Conference Date: 13 July 2009 Through 16 July 2009; Conference Code:92509},

keywords = {Agent based; Blood test; Cancer patients; Cancer staging; Clinical intelligence; Computational system; Computational technique; Group decision supports; Health services; Interactive agents; Medical informatics; Patient education; Quality of life; Right information, Artificial intelligence; Decision support systems; Diagnosis; Disease control; Intelligent agents; Medical imaging; Patient treatment; Tissue, Diseases},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Miranda2009313,

title = {An agent-based architecture for cancer staging},

author = {M. Miranda and A. Abelha and M. Santos and J. Machado and J. Neves},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-74549205928&doi=10.5220%2f0001863503130316&partnerID=40&md5=0a0e694e2a6f23cab3c5503c39d39170},

doi = {10.5220/0001863503130316},

isbn = {9789898111845},

year  = {2009},

date = {2009-01-01},

journal = {ICEIS 2009 - 11th International Conference on Enterprise Information Systems, Proceedings},

volume = {AIDSS},

pages = {313-316},

publisher = {INSTICC Press},

address = {Milan},

abstract = {Cancer staging is the process by which physicians evaluate the spread of cancer. This is important, once in a good cancer staging system, the stage of disease helps to determine prognosis and assists in selecting ther apies. A combination of physical examination, blood tests, and medical imaging is used to determine the clinical stage; if tissue is obtained via biopsy or surgery, examination of the tissue under a microscope can provide pathologic staging. On the other hand, good patient education may help to reduce health service costs and improve the quality of life of people with chronic or terminal conditions. In this paper it is endorsed a theoretical based model to support the provision of computer based information on cancer patients, and the computational techniques used to implement it. One's goal is to develop an interactive agent based computa-tional system which may provide physicians with the right information, on time, that is adapted to the situation and process-based aspects of the patients's illness and treatment.},

note = {cited By 0; Conference of ICEIS 2009 - 11th International Conference on Enterprise Information Systems ; Conference Date: 6 May 2009 Through 10 May 2009; Conference Code:79065},

keywords = {Agent based architectures; Cancer patients; Cancer staging; Computational technique; Health services; Interactive agents; Patient education; Quality of life, Artificial intelligence; Decision support systems; Diagnosis; Information systems; Information use; Medical computing; Medical imaging; Patient treatment; Tissue, Diseases},

pubstate = {published},

tppubtype = {inproceedings}

}