Deep continual multitask severity assessment from changing clinical features
Ferri, P.; Saez, C.; Felix-De Castro, A.; Sanchez-Cuesta, P.; Garcia-Gomez, J. M.
Show abstract
When developing Machine Learning models to support emergency medical triage, it is important to consider how changes over time in the data can negatively affect the models performance. The objective of this study was to assess the effectiveness of novel Deep Continual Learning pipelines in maximizing model performance 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, predict its admissible response delay, and determine its institutional jurisdiction. We analyzed a total of 1 414 575 events spanning from 2009 to 2019. Our findings demonstrate important performance improvements, up to 4.9% in life-threatening, 18.5% in response delay and 1.7% in jurisdiction, in absolute F1-score, compared to the current triage protocol, and improvements up to 4.4% in life-threatening and 11% in response delay, in absolute F1-score, respect to non-continual approaches.
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