Evaluation of the Length of Hospital Stay through Artificial Neural Networks based Systems

dc.contributor.authorAbelha, Vasco
dc.contributor.authorMarins, Fernando
dc.contributor.authorVicente, Henrique
dc.contributor.editorInformation Resources Management Association
dc.date.accessioned2020-01-09T12:07:46Z
dc.date.available2020-01-09T12:07:46Z
dc.date.issued2020
dc.description.abstractThe mentality of savings and eliminating any kind of outgoing costs is undermining our society and our way of living. Cutting funds from Education to Health is at best delaying the inevitable “Crash” that is foreshadowed. Regarding Health, a major concern, can be described as jeopardize the health of Patients – Reduce of the Length of Hospital. As we all know, Human Health is very sensitive and prune to drastic changes in short spaces of time. Factors like age, sex, their ambient context – house conditions, daily lives – should all be important when deciding how long a specific patient should remain safe in a hospital. In no way, ought this to be decided by the economic politics. Logic Programming was used for knowledge representation and reasoning, letting the modeling of the universe of discourse in terms of defective data, information and knowledge. Artificial Neural Networks and Genetic Algorithms were used in order to evaluate and predict how long should a patient remain in the hospital in order to minimize the collateral damage of our government approaches, not forgetting the use of Degree of Confidence to demonstrate how feasible the assessment is.por
dc.identifier.authoremailvascoabelha91@gmail.com
dc.identifier.authoremailf.abreu.marins@gmail.com
dc.identifier.authoremailhvicente@uevora.pt
dc.identifier.capitulo20 - Evaluation of the Length of Hospital Stay through Artificial Neural Networks Based Systems
dc.identifier.citationAbelha, V., Marins, F., & Vicente, H., Evaluation of the Length of Hospital Stay through Artificial Neural Networks based Systems. In Information Resources Management Association Ed., Hospital Management and Emergency Medicine: Breakthroughs in Research and Practice, pp. 391–403, IGI Global, Hershey, USA, 2020.por
dc.identifier.doi10.4018/978-1-7998-2451-0.ch020por
dc.identifier.isbn9781799824510 (paper)
dc.identifier.isbn9781799824527 (electronic)
dc.identifier.numpag13
dc.identifier.sharewithCQEpor
dc.identifier.urihttps://www.igi-global.com/chapter/evaluation-of-the-length-of-hospital-stay-through-artificial-neural-networks-based-systems/246257
dc.identifier.urihttp://hdl.handle.net/10174/26347
dc.language.isoengpor
dc.publisherIGI Globalpor
dc.rightsopenAccesspor
dc.subjectLength of Hospital Staypor
dc.subjectHealthcarepor
dc.subjectLogic Programmingpor
dc.subjectKnowledge Representation and Reasoningpor
dc.subjectArtificial Neural Networkspor
dc.subjectIncomplete Informationpor
dc.titleEvaluation of the Length of Hospital Stay through Artificial Neural Networks based Systemspor
dc.typebookPartpor

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