Deep continual multitask severity assessment from changing clinical features

Pablo Ferri, Carlos Sáez, Antonio Félix-De Castro, Purificación Sánchez-Cuesta, Juan M García-Gómez
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Abstract

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.
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从不断变化的临床特征出发,深入持续地进行多任务严重性评估
在开发支持紧急医疗分诊的机器学习模型时,必须考虑数据分布随时间的变化会对模型性能产生怎样的负面影响。本研究的目的是评估各种持续学习管道在输入特征随时间变化(包括新特征的出现和现有特征的消失)时保持模型性能稳定的有效性。该模型旨在识别危及生命的情况,计算其可接受的响应延迟,并确定其机构管辖范围。我们分析了从 2009 年到 2019 年共 1 414 575 起事件。我们的研究结果表明,在采用深度持续方法时,性能有了显著提高,在生命威胁方面提高了 7.8%,在响应延迟方面提高了 14.8%(以 F1 分数计算)。我们注意到,结合微调和动态特征域更新策略为解决医疗急救数据中的分布漂移问题提供了一种实用而有效的解决方案。
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