pubs2024

@inproceedings{Peixoto2017178,

title = {Continuous ambulatory peritoneal dialysis: Business intelligence applied to patient monitoring: CAPD study and statistics},

author = {C. Peixoto and C. Brito and M. Fontainhas and H. Peixoto and J. Machado and A. Abelha},

editor = {Younas M. Portela F. Awan I.},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85047407906&doi=10.1109%2fFiCloudW.2017.91&partnerID=40&md5=52837dd5542ae72ec58b161ef95de992},

doi = {10.1109/FiCloudW.2017.91},

isbn = {9781538632819},

year  = {2017},

date = {2017-01-01},

journal = {Proceedings - 2017 5th International Conference on Future Internet of Things and Cloud Workshops, W-FiCloud 2017},

volume = {2017-January},

pages = {178-185},

publisher = {Institute of Electrical and Electronics Engineers Inc.},

abstract = {Continuous Ambulatory Peritoneal Dialysis (CAPD) is one of the many treatments for patients with advanced kidney disease. It is a treatment that needs regular monitoring and understanding of all the factors of blood and urine samples of each patient to understand if the treatment is going well. This article will explore data information from patients undergoing CAPD procedure. This data information helps to comprehend how interoperability acts in a Health Information System since this data contains patients' personal information but also patients' blood and urine samples' results, meaning all the services must be connected. In this work, it is used Business Intelligence process to prove that all the information available can be useful to understand the treatment above-mentioned and also how can several factors influence or not the number of patients going through kidney failure and CAPD by the study of indicators. © 2017 IEEE.},

note = {cited By 0; Conference of 5th IEEE International Conference on Future Internet of Things and Cloud Workshops, W-FiCloud 2017 ; Conference Date: 21 August 2017 Through 23 August 2017; Conference Code:132622},

keywords = {Blood and urine samples; Data informations; Health information systems; Kidney; Kidney disease; Peritoneal dialysis; Personal information, Blood; Competitive intelligence; Dialysis; Information analysis; Information dissemination; Information systems; Information use; Internet of things; Interoperability; Microarrays; Patient monitoring, Patient treatment},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Peixoto2016528,

title = {Resurgery Clusters in Intensive Medicine},

author = {R. Peixoto and F. Portela and F. Pinto and M. F. Santos and J. Machado and A. Abelha and F. Rua},

editor = {Shakshuki E.},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84992435562&doi=10.1016%2fj.procs.2016.09.072&partnerID=40&md5=c079429a47641fbe04385e2e5678042a},

doi = {10.1016/j.procs.2016.09.072},

issn = {18770509},

year  = {2016},

date = {2016-01-01},

journal = {Procedia Computer Science},

volume = {58},

pages = {528-533},

publisher = {Elsevier B.V.},

abstract = {The field of critical care medicine is confronted every day with cases of surgical interventions. When Data Mining is properly applied in this field, it is possible through predictive models to identify if a patient, should or should not have surgery again upon the same problem. The goal of this work is to apply clustering techniques in collected data in order to categorize re-interventions in intensive care. By knowing the common characteristics of the re-intervention patients it will be possible to help the physician to predict a future resurgery. For this study various attributes were used related to the patient's health problems like heart problems or organ failure. For this study it was also considered important aspects such as age and what type of surgery the patient was submitted. Classes were created with the patients' age and the number of days after the first surgery. Another class was created where the type of surgery that the patient was operated upon was identified. This study comprised Davies Bouldin values between -0.977 and -0.416. The used variables, in addition to being provided by Hospital de Santo António in Porto, they are provided from the electronic medical record. © 2016 The Authors.},

note = {cited By 0; Conference of 7th International Conference on Emerging Ubiquitous Systems and Pervasive Networks, EUSPN 2016 / The 6th International Conference on Current and Future Trends of Information and Communication Technologies in Healthcare, ICTH-2016 / Affiliated Workshops, 2016 ; Conference Date: 19 September 2016 Through 22 September 2016; Conference Code:131700},

keywords = {Clustering; Clustering techniques; Critical care medicine; Electronic medical record; INTCare; Intervention; Predictive models; Surgical interventions, Data mining; Health care; Intensive care units; Medical computing; Medicine; Surgery, Patient treatment},

pubstate = {published},

tppubtype = {inproceedings}

}