A Deep Learning Approach for Predictive Healthcare Process Monitoring

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AudienciaPúblico en generales_ES
CoberturaMéxicoes_ES
Fecha de ingreso2026-09-15T00:29:42Z
Fecha de publicación2023-01-01
ResumenIn this paper, we propose a deep learning-based approach to predict the next event in hospital organizational process models following the guidance of predictive process mining. This method provides value for the planning and allocating of resources since each trace linked to a case shows the consecutive execution of events in a healthcare process. The predictive model is based on a long short-term memory (LSTM) neural network that achieves high accuracy in the training and testing stages. In addition, a framework to implement the LSTM neural network is proposed, comprising stages from the preprocessing of the raw data to selecting the best LSTM model. The effectiveness of the prediction method is evaluated through four real-life event logs that contain historical information on the execution of the processes of patient transfer orders between hospitals, sepsis care cases, billing of medical services, and patient care management. In the test stage, the LSTM model reached values of 0.98, 0.91, 0.85, and 0.81 in the accuracy metric, and in the evaluation of the prediction of the next event using the 10-fold cross-validation technique, values of 0.94, 0.88, 0.84, and 0.81 were obtained for the four previously mentioned event logs. In addition, the performance of the LSTM prediction model was evaluated with the precision, recall, F1-score, and area under the receiver operating characteristic (ROC) curve (AUC) metrics, obtaining high scores very close to 1. The experimental results suggest that the proposed method achieves acceptable measures in predicting the next event regardless of whether an input event or a set of input events is used.es_ES
Doihttps://doi.org/10.3390/info14090508es_ES
URIhttps://riuat.uat.edu.mx/handle/123456789/1687
Idiomaeses_ES
EditorialMDPI AGes_ES
RelaciónInformationes_ES
URL relacionadohttps://doi.org/10.3390/info14090508es_ES
DerechosAcceso abierto (Metadatos de producción científica)es_ES
Licenciahttp://purl.org/coar/access_right/c_abf2es_ES
FuenteInformation
TítuloA Deep Learning Approach for Predictive Healthcare Process Monitoringes_ES
TipoArtículoes_ES
ArbitradoHa sido Arbitradoes_ES
AutorRamirez-Alcocer, Ulises Manuel
AutorTello-Leal, Edgar
AutorRomero, Gerardo
AutorMacías-Hernández, Bárbara A.
AutorRamirez-Alcocer, Ulises Manueles_ES
AutorTello-Leal, Edgares_ES
AutorRomero, Gerardoes_ES
AutorMacías-Hernández, Bárbara A.es_ES
InstituciónUniversidad Autónoma de Tamaulipas
InstituciónUniversidad Autónoma de Tamaulipases_ES
Número9es_ES
Rango de páginas508es_ES
URL relacionadahttps://doi.org/10.3390/info14090508
Tipo de artículoIndexado
Tipo de artículoIndexadoes_ES
Volumen14es_ES

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