Um modelo proativo de antecipação de ações de times de resposta rápida baseado em análise preditiva
Resumo
Um Time de Resposta Rápida busca prevenir mortes de pacientes que tenham piora clínica fora de ambientes de Unidades de Tratamento Intensivo em hospitais. O modelo Predictvs busca antecipar ações dos times de resposta rápida, através da análise dos sinais vitais dos pacientes com o uso de escores de alerta precoce. A contribuição científica do modelo é dada em virtude da possibilidade de efetuar a predição de possíveis situações de colapso dos pacientes através do monitoramento e análise dos sinais vitais. A avaliação do Predictvs foi efetuada com a utilização de cenários e a sua análise apresentou resultados que motivam a continuidade da pesquisa.
Referências
Ameyed D., Miraoui M., Tadj C. (2015). “A Survey of Prediction Approach in Pervasive Computing”. International Journal of Scientific & Engineering Research.
Berwick D. et al. (2006). “The 100,000 Lives Campaign: Setting a goal and a deadline for improving health care quality”. The Journal of the American Medical Association, JAMA, 295:324–327.
Bailey, T. C. et al. (2013). “A Trial of a Real-Time Alert for Clinical Deterioration in Patients Hospitalized on General Medical Wards”. Society of Hospital Medicine. DOI 10.1002/jhm.2009.
Badriyah, T. et al. (2013). “Decision-tree early warning score (DTEWS) validates the design of the National Early Warning Score (NEWS)”. Resuscitation. Elsevier Ireland.
Brady, P. W. et al. (2013). “Improving Situation Awareness to Reduce Unrecognized Clinical Deterioration and Serious Safety Events”. Pediatrics. 131;e298 DOI: 10.1542/peds.2012-1364.
Burbey I. and Martin T. (2012). “A survey on predicting personal mobility”. International Journal of Pervasive Computing and Communications. Emerald.
Costa, C. A, Yamin, A. Geyer, C. (2008). “Towards a General Software Infrastructure for Ubiquitous Computing”. IEEE Pervasive Computing. V. 7, p. 64-73.
Elgazzar K., Aboelfotoh M., Martin P. (2012). “Ubiquitous Health Monitoring Using Mobile Web Service”. Procedia Computer Science. V. 10, n. 0, p. 332 – 339.
Gelogo, Y. E., and Kim, H. K. (2013). “Unified Ubiquitous Healthcare System Architecture with Collaborative Model”. International Journal of Multimedia and Ubiquitous Engineering, 8(3).
Gonçalves P. et al. (2012). “Reduced frequency of cardiopulmonary arrests by rapid response teams”. Einstein. SciELO.
Hendrich, A. L. (2007). “Predicting Patient Falls”. American Journal of Nursing. 107(11): 50-58.
Kirkland, L. et al. (2013). “A Clinical Deterioration Prediction Tool for Internal Medicine Patients”. American Journal of Medical Quality. 28(2) 135–142.
Kitchenham, B. (2004). “Procedures for Performing Systematic Reviews”. Joint Technical Report. Keele University.
Kyriacos, U. et al. (2014). “Monitoring Vital Signs: Development of a Modified Early Warning Scoring (Mews) System for General Wards in a Developing Country”. PLoS ONE. 9(1): e87073. doi:10.1371/journal.pone.0087073
Konig I., Voigtmann C., Klein B., David K. (2011). “Enhancing alignment based context prediction by using multiple context sources: experiment and analysis”. In Modeling and Using Context. Springer-Verlag, p. 159–172.
McCannon C., Hackbarth A., Griffin A. (2007). “Miles to Go: An Introduction to the 5 Million Lives Campaign”. The Joint Commission Journal on Quality and Patient Safety, Volume 33, Number 8, pp. 477-484(8).
Morgan R. J. M, Williams F., Wright M. M. (1997). “An early warning scoring system for detecting developing critical illness”. Clinical Intensive Care. 8: 100.
Offner P., Heit J., Roberts R. (2007). “Implementation of a Rapid Response Team Decreases Cardiac Arrest Outside of the Intensive Care Unit”. Journal of Trauma-Injury Infection & Critical Care. OVID.
Patterson D., Liao L., Fox D., Kautz H. (2003). “Inferring High-Level Behavior from Low-Level Sensors”. In Ubiquitous Computing, p. 73–89.
Rocha, C., Costa, C. A., Righi, R. R. (2015). Um modelo para monitoramento de sinais vitais do coração baseado em ciência da situação e computação ubíqua. SBCUP 2015.
Rosa J. H., Barbosa J. L.V., Ribeiro G. D. (2015). “ORACON: An Adaptive Model for Contexts Prediction”. Expert Systems With Applications, Elsevier.
Satyanarayanan M. (2001). “Pervasive computing: vision and challenges”. IEEE Personal Communications. IEEE, 2001.
Sigg S. (2008). “Development of a Novel Context Prediction Algorithm and Analysis of Context Prediction Schemes”. Kassel University Press.
Watcher R., Pronovost P. (2006). “The 100,000 Lives Campaign: A Scientific and Policy Review”. Journal on Quality and Patient Safety.
Weiser M. (1991). “The computer for the twenty-first century”. ACM SIGMOBILE Mobile Computing and Communications Review - Special issue dedicated to Mark Weiser. ACM Digital Library.
Weiss G., Hirsh H. (1998). “Learning to Predict Rare Events in Categorical Time-Series Data”. In Proceedings of the Fourth International Conference on Knowledge Discovery and Data Mining, AAAI Press, Menlo Park, CA.
Berwick D. et al. (2006). “The 100,000 Lives Campaign: Setting a goal and a deadline for improving health care quality”. The Journal of the American Medical Association, JAMA, 295:324–327.
Bailey, T. C. et al. (2013). “A Trial of a Real-Time Alert for Clinical Deterioration in Patients Hospitalized on General Medical Wards”. Society of Hospital Medicine. DOI 10.1002/jhm.2009.
Badriyah, T. et al. (2013). “Decision-tree early warning score (DTEWS) validates the design of the National Early Warning Score (NEWS)”. Resuscitation. Elsevier Ireland.
Brady, P. W. et al. (2013). “Improving Situation Awareness to Reduce Unrecognized Clinical Deterioration and Serious Safety Events”. Pediatrics. 131;e298 DOI: 10.1542/peds.2012-1364.
Burbey I. and Martin T. (2012). “A survey on predicting personal mobility”. International Journal of Pervasive Computing and Communications. Emerald.
Costa, C. A, Yamin, A. Geyer, C. (2008). “Towards a General Software Infrastructure for Ubiquitous Computing”. IEEE Pervasive Computing. V. 7, p. 64-73.
Elgazzar K., Aboelfotoh M., Martin P. (2012). “Ubiquitous Health Monitoring Using Mobile Web Service”. Procedia Computer Science. V. 10, n. 0, p. 332 – 339.
Gelogo, Y. E., and Kim, H. K. (2013). “Unified Ubiquitous Healthcare System Architecture with Collaborative Model”. International Journal of Multimedia and Ubiquitous Engineering, 8(3).
Gonçalves P. et al. (2012). “Reduced frequency of cardiopulmonary arrests by rapid response teams”. Einstein. SciELO.
Hendrich, A. L. (2007). “Predicting Patient Falls”. American Journal of Nursing. 107(11): 50-58.
Kirkland, L. et al. (2013). “A Clinical Deterioration Prediction Tool for Internal Medicine Patients”. American Journal of Medical Quality. 28(2) 135–142.
Kitchenham, B. (2004). “Procedures for Performing Systematic Reviews”. Joint Technical Report. Keele University.
Kyriacos, U. et al. (2014). “Monitoring Vital Signs: Development of a Modified Early Warning Scoring (Mews) System for General Wards in a Developing Country”. PLoS ONE. 9(1): e87073. doi:10.1371/journal.pone.0087073
Konig I., Voigtmann C., Klein B., David K. (2011). “Enhancing alignment based context prediction by using multiple context sources: experiment and analysis”. In Modeling and Using Context. Springer-Verlag, p. 159–172.
McCannon C., Hackbarth A., Griffin A. (2007). “Miles to Go: An Introduction to the 5 Million Lives Campaign”. The Joint Commission Journal on Quality and Patient Safety, Volume 33, Number 8, pp. 477-484(8).
Morgan R. J. M, Williams F., Wright M. M. (1997). “An early warning scoring system for detecting developing critical illness”. Clinical Intensive Care. 8: 100.
Offner P., Heit J., Roberts R. (2007). “Implementation of a Rapid Response Team Decreases Cardiac Arrest Outside of the Intensive Care Unit”. Journal of Trauma-Injury Infection & Critical Care. OVID.
Patterson D., Liao L., Fox D., Kautz H. (2003). “Inferring High-Level Behavior from Low-Level Sensors”. In Ubiquitous Computing, p. 73–89.
Rocha, C., Costa, C. A., Righi, R. R. (2015). Um modelo para monitoramento de sinais vitais do coração baseado em ciência da situação e computação ubíqua. SBCUP 2015.
Rosa J. H., Barbosa J. L.V., Ribeiro G. D. (2015). “ORACON: An Adaptive Model for Contexts Prediction”. Expert Systems With Applications, Elsevier.
Satyanarayanan M. (2001). “Pervasive computing: vision and challenges”. IEEE Personal Communications. IEEE, 2001.
Sigg S. (2008). “Development of a Novel Context Prediction Algorithm and Analysis of Context Prediction Schemes”. Kassel University Press.
Watcher R., Pronovost P. (2006). “The 100,000 Lives Campaign: A Scientific and Policy Review”. Journal on Quality and Patient Safety.
Weiser M. (1991). “The computer for the twenty-first century”. ACM SIGMOBILE Mobile Computing and Communications Review - Special issue dedicated to Mark Weiser. ACM Digital Library.
Weiss G., Hirsh H. (1998). “Learning to Predict Rare Events in Categorical Time-Series Data”. In Proceedings of the Fourth International Conference on Knowledge Discovery and Data Mining, AAAI Press, Menlo Park, CA.
Publicado
04/07/2016
Como Citar
DIAS, Fabio de Oliveira; DA COSTA, Cristiano André; RIGHI, Rodrigo da Rosa.
Um modelo proativo de antecipação de ações de times de resposta rápida baseado em análise preditiva. In: SIMPÓSIO BRASILEIRO DE COMPUTAÇÃO UBÍQUA E PERVASIVA (SBCUP), 8. , 2016, Porto Alegre.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2016
.
p. 1166-1175.
ISSN 2595-6183.
DOI: https://doi.org/10.5753/sbcup.2016.9465.
