A machine learning model for triage in lean pediatric emergency departments
High demand periods and under-staffing due to financial constraints cause Emergency Departments (EDs) to frequently exhibit over-crowding and slow response times to provide adequate patient care. In response, Lean Thinking has been applied to help alleviate some of these issues and improve patient h...
- Autores:
- Tipo de recurso:
- Fecha de publicación:
- 2016
- Institución:
- Universidad Tecnológica de Bolívar
- Repositorio:
- Repositorio Institucional UTB
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.utb.edu.co:20.500.12585/8997
- Acceso en línea:
- https://hdl.handle.net/20.500.12585/8997
- Palabra clave:
- Emergency department
Fast track
Lean
Logistic regression
Machine learning
Neural networks
PCA
SVM
Triage
Artificial intelligence
Complex networks
Emergency rooms
Hospitals
Neural networks
Patient monitoring
Patient treatment
Pediatrics
Emergency departments
Fast tracks
Lean
Logistic regressions
Triage
Learning systems
- Rights
- restrictedAccess
- License
- http://creativecommons.org/licenses/by-nc-nd/4.0/
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A machine learning model for triage in lean pediatric emergency departments |
| title |
A machine learning model for triage in lean pediatric emergency departments |
| spellingShingle |
A machine learning model for triage in lean pediatric emergency departments Emergency department Fast track Lean Logistic regression Machine learning Neural networks PCA SVM Triage Artificial intelligence Complex networks Emergency rooms Hospitals Neural networks Patient monitoring Patient treatment Pediatrics Emergency departments Fast tracks Lean Logistic regressions Triage Learning systems |
| title_short |
A machine learning model for triage in lean pediatric emergency departments |
| title_full |
A machine learning model for triage in lean pediatric emergency departments |
| title_fullStr |
A machine learning model for triage in lean pediatric emergency departments |
| title_full_unstemmed |
A machine learning model for triage in lean pediatric emergency departments |
| title_sort |
A machine learning model for triage in lean pediatric emergency departments |
| dc.contributor.editor.none.fl_str_mv |
Escalante H.J. Montes-y-Gomez M. Segura A. de Dios Murillo J. |
| dc.subject.keywords.none.fl_str_mv |
Emergency department Fast track Lean Logistic regression Machine learning Neural networks PCA SVM Triage Artificial intelligence Complex networks Emergency rooms Hospitals Neural networks Patient monitoring Patient treatment Pediatrics Emergency departments Fast tracks Lean Logistic regressions Triage Learning systems |
| topic |
Emergency department Fast track Lean Logistic regression Machine learning Neural networks PCA SVM Triage Artificial intelligence Complex networks Emergency rooms Hospitals Neural networks Patient monitoring Patient treatment Pediatrics Emergency departments Fast tracks Lean Logistic regressions Triage Learning systems |
| description |
High demand periods and under-staffing due to financial constraints cause Emergency Departments (EDs) to frequently exhibit over-crowding and slow response times to provide adequate patient care. In response, Lean Thinking has been applied to help alleviate some of these issues and improve patient handling, with success. Lean approaches in EDs include separate patient streams, with low-complexity patients treated in a so-called Fast Track, in order to reduce total waiting time and to free-up capacity to treat more complicated patients in a timely manner. In this work we propose the use of Machine Learning techniques in a Lean Pediatric ED to correctly predict which patients should be admitted to the Fast Track, given their signs and symptoms. Charts from 1205 patients of the emergency department of Hospital Napoleón Franco Pareja in Cartagena - Colombia, were used to construct a dataset and build several predictive models. Validation and test results are promising and support the validity of this approach and further research on the subject. © Springer International Publishing AG 2016. |
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2016 |
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2016 |
| dc.date.accessioned.none.fl_str_mv |
2020-03-26T16:32:44Z |
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2020-03-26T16:32:44Z |
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http://purl.org/coar/version/c_970fb48d4fbd8a85 |
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Conferencia |
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Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10022 LNAI, pp. 212-221 |
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9783319479545 |
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03029743 |
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https://hdl.handle.net/20.500.12585/8997 |
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10.1007/978-3-319-47955-2_18 |
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Universidad Tecnológica de Bolívar |
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Repositorio UTB |
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55782426500 57191839719 55782490400 |
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Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10022 LNAI, pp. 212-221 9783319479545 03029743 10.1007/978-3-319-47955-2_18 Universidad Tecnológica de Bolívar Repositorio UTB 55782426500 57191839719 55782490400 |
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https://hdl.handle.net/20.500.12585/8997 |
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eng |
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eng |
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23 November 2016 through 25 November 2016 |
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Atribución-NoComercial 4.0 Internacional |
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Springer Verlag |
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Springer Verlag |
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15th Ibero-American Conference on Advances in Artificial Intelligence, IBERAMIA 2016 |
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Escalante H.J.Montes-y-Gomez M.Segura A.de Dios Murillo J.Caicedo-Torres W.García G.Pinzón H.2020-03-26T16:32:44Z2020-03-26T16:32:44Z2016Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10022 LNAI, pp. 212-221978331947954503029743https://hdl.handle.net/20.500.12585/899710.1007/978-3-319-47955-2_18Universidad Tecnológica de BolívarRepositorio UTB557824265005719183971955782490400High demand periods and under-staffing due to financial constraints cause Emergency Departments (EDs) to frequently exhibit over-crowding and slow response times to provide adequate patient care. In response, Lean Thinking has been applied to help alleviate some of these issues and improve patient handling, with success. Lean approaches in EDs include separate patient streams, with low-complexity patients treated in a so-called Fast Track, in order to reduce total waiting time and to free-up capacity to treat more complicated patients in a timely manner. In this work we propose the use of Machine Learning techniques in a Lean Pediatric ED to correctly predict which patients should be admitted to the Fast Track, given their signs and symptoms. Charts from 1205 patients of the emergency department of Hospital Napoleón Franco Pareja in Cartagena - Colombia, were used to construct a dataset and build several predictive models. Validation and test results are promising and support the validity of this approach and further research on the subject. © Springer International Publishing AG 2016.Recurso electrónicoapplication/pdfengSpringer Verlaghttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/restrictedAccessAtribución-NoComercial 4.0 Internacionalhttp://purl.org/coar/access_right/c_16echttps://www.scopus.com/inward/record.uri?eid=2-s2.0-84994153904&doi=10.1007%2f978-3-319-47955-2_18&partnerID=40&md5=c13dcd2b943033797d17b5c57ff8344a15th Ibero-American Conference on Advances in Artificial Intelligence, IBERAMIA 2016A machine learning model for triage in lean pediatric emergency departmentsinfo:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/publishedVersionConferenciahttp://purl.org/coar/version/c_970fb48d4fbd8a85http://purl.org/coar/resource_type/c_c94fEmergency departmentFast trackLeanLogistic regressionMachine learningNeural networksPCASVMTriageArtificial intelligenceComplex networksEmergency roomsHospitalsNeural networksPatient monitoringPatient treatmentPediatricsEmergency departmentsFast tracksLeanLogistic regressionsTriageLearning systems23 November 2016 through 25 November 2016Aplin, S., Baines, D., De Lima, J., Use of the ASA physical status grading system in pediatric practice (2007) Paediatr. Anaesth., 17 (3), pp. 216-222Beyer, J.E., Turner, S.B., Jones, L., Young, L., Onikul, R., Bohaty, B., The alternate forms reliability of the Oucher pain scale (2005) Pain Manag. Nurs, 6 (1), pp. 10-17Bonadio, W.A., Hennes, H., Smith, D., Ruffing, R., Melzer-Lange, M., Lye, P., Isaacman, D., Reliability of observation variables in distinguishing infectious outcome of febrile young infants (1993) Pediatr. Infect. Dis. J, 12 (2), pp. 111-114Carrol, E., Riordan, F., Thomson, A., Sills, J., Hart, C., The role of the Glasgow meningococcal septicaemia prognostic score in the emergency management of meningococcal disease (1999) Arch. Dis. Child, 81 (3), p. 278. , http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1718049/Cortes, C., Vapnik, V., Support-vector networks (1995) Mach. Learn, 20 (3), pp. 273-297. , http://dx.doi.org/10.1007/BF00994018Demšar, J., Curk, T., Erjavec, A., Gorup, Č., Hočevar, T., Milutinovič, M., Možina, M., Zupan, B., Orange: Data mining toolbox in python (2013) J. Mach. Learn. Res, 14, pp. 2349-2353. , http://jmlr.org/papers/v14/demsar13a.htmlFerres, J., Comparison of two nebulized treatments in wheezing infants (1988) Eur. Respir. J, 1, p. 306Fitzgerald, G., Triage revisited (1998) Emerg. Med, 10 (4), pp. 291-293. , http://dx.doi.org/10.1111/j.1442-2026.1998.tb00694.xHerndon, R., (2006) Handbook of Neurologic Rating Scales, , http://books.google.com.co/books?id=w1yPmehSZ2cC, 2nd edn. Demos Medical Publishing, New YorkHolden, R.J., Lean thinking in emergency departments: A critical review (2010) Ann. Emerg. Med, 57 (3), pp. 265-278. , http://dx.doi.org/10.1016/j.annemergmed.2010.08.001Huppler, A.R., Eickhoff, J.C., Wald, E.R., Performance of low-risk criteria in the evaluation of young infants with fever: Review of the literature (2010) Pediatrics, 125 (2), pp. 228-233. , http://pediatrics.aappublications.org/content/125/2/228Ieraci, S., Digiusto, E., Sonntag, P., Dann, L., Fox, D., Streaming by case complexity: Evaluation of a model for emergency department fast track (2008) Emerg. Med. Australas, 20 (3), pp. 241-249. , http://dx.doi.org/10.1111/j.1742-6723.2008.01087.xJolliffe, I., (2002) Principal Component Analysis. Springer Series in Statistics, , http://books.google.com.co/books?id=TtVF-ao4fI8C, Springer, BerlinKelly, A.M., Bryant, M., Cox, L., Jolley, D., Improving emergency department efficiency by patient streaming to outcomes-based teams (2007) Aust. Health Rev, 31 (1), pp. 16-21. , http://www.publish.csiro.au/paper/AH070016McCarthy, P.L., Sharpe, M.R., Spiesel, S.Z., Dolan, T.F., Forsyth, B.W., Dewitt, T.G., Fink, H.D., Cicchetti, D.V., Observation scales to identify serious illness in febrile children (1982) Pediatrics, 70 (5), pp. 802-809. , http://pediatrics.aappublications.org/content/70/5/802McCullagh, P., Nelder, J., (1989) Generalized Linear Models, , http://books.google.co.uk/books?id=h9kFH2FfBkC, 2nd edn. Chapman & Hall/CRC Monographs on Statistics & Applied Probability. Taylor & Francis, Abingdon-on-ThamesMintegui, R.S., Sanchez, E.J., Benito, F.J., Angulo, B.P., Gastiasoro, C.L., Ortiz, A.A., Usefulness of oxygen saturation in the assessment of children with moderated laryngitis (1996) An. Esp. Pediatr, 45 (3), pp. 261-263Ng, D., Vail, G., Thomas, S., Schmidt, N., Applying the lean principles of the Toyota production system to reduce wait times in the emergency department (2010) CJEM, 12 (1), pp. 50-57Paganini, H., de Santolaya, P., Álvarez, M., Araña Rosaínz, M.D.J., Arteaga Bonilla, R., Bonilla, A., Caniza, M., Scopinaro, M., Diagn (2011) Revista Chilena De Infectolog, 28, pp. 10-38. , http://www.scielo.cl/scielo.php?script=sciarttext&pid=S0716-10182011000400003&nrm=isoRumelhart, D.E., Hinton, G.E., Williams, R.J., Parallel distributed processing: Explorations in the microstructure of cognition (1986) Learning Internal Representations by Error Propagation, 1, pp. 318-362. , http://dl.acm.org/citation.cfm?id=104279.104293, MIT Press, CambridgeScarfone, R.J., Fuchs, S.M., Nager, A.L., Shane, S.A., Controlled trial of oral prednisone in the emergency department treatment of children with acute asthma (1993) Pediatrics, 92 (4), pp. 513-518Velasco-Pérez, G., Escalera analg (2014) Acta pedi´atrica De M, 35, pp. 249-255. , http://www.scielo.org.mx/scielo.php?script=sciarttext&pid=S0186-23912014000300011&nrm=isoWomack, J.P., Jones, D.T., Roos, D., (1991) The Machine that Changed The World: The Story of Lean Production, , https://books.google.de/books?id=Jz4zog27W7gC, The MIT International Motor Vehicle Program. Harper- Collins, New Yorkhttp://purl.org/coar/resource_type/c_c94fTHUMBNAILMiniProdInv.pngMiniProdInv.pngimage/png23941https://repositorio.utb.edu.co/bitstream/20.500.12585/8997/1/MiniProdInv.png0cb0f101a8d16897fb46fc914d3d7043MD5120.500.12585/8997oai:repositorio.utb.edu.co:20.500.12585/89972021-02-02 14:15:10.598Repositorio Institucional UTBrepositorioutb@utb.edu.co |