When developing Machine Learning models to support emergency medical triage, it is important to consider how changes over time in the data distribution can negatively affect the models' performance. The objective of this study was to assess the effectiveness of various Continual Learning pipelines in keeping model performance stable when input features are subject to change over time, including the emergence of new features and the disappearance of existing ones. The model is designed to identify life-threatening situations, calculate its admissible response delay, and determine its institution jurisdiction. We analyzed a total of 1 414 575 events spanning from 2009 to 2019. Our findings demonstrate important performance improvements, up to 7.8% in life-threatening and 14.8% in response delay, in terms of F1-score, when employing deep continual approaches. We noticed that combining fine-tuning and dynamic feature domain updating strategies offers a practical and effective solution for addressing these distributional drifts in medical emergency data.

The authors have declared no competing interest.

This work has received support from the Ministry of Science, Innovation, and Universities of Spain through the FPU18/06441 program and the KINEMAI project (PID2022-138636OA-I00).

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The details of the IRB/oversight body that provided approval or exemption for the research described are given below:

Data use was approved by the Institutional Review Board of the Health Services Department of the Valencian Region (Conselleria de Sanitat de la Generalitat Valenciana).

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The data produced in the present work regarding to the performance of the compared strategies is included within the manuscript (figures and tables).