A Deep Learning Approach for Predictive Healthcare Process Monitoring

• Published in: Information (Basel) 2023-09, Vol.14 (9), p.508
• Main Authors: Ramirez-Alcocer, Ulises Manuel, Tello-Leal, Edgar, Romero, Gerardo, Macías-Hernández, Bárbara A.
• Format: Article
• Language: English
• Subjects: Accuracy; Behavior; Deep learning; Electronic health records; Health care; Health services; healthcare process; Hospitals; Information systems; LSTM; Methods; Neural networks; next event; Organizational aspects; Patients; Prediction models; predictive model; Quality of service; Taiwan; Time series
• ISSN: 2078-2489; 2078-2489
• DOI: 10.3390/info14090508

In 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.