The objective of this work was to develop a predictive model to aid non-clinical dispatchers to classify emergency medical call incidents by their life-threatening level (yes/no), admissible response delay (undelayable, minutes, hours, days) and emergency system jurisdiction (emergency system/primary care) in real time. We used a total of 1 244 624 independent incidents from the Valencian emergency medical dispatch service in Spain, compiled in retrospective from 2009 to 2012, including clinical features, demographics, circumstantial factors and free text dispatcher observations. Based on them, we designed and developed DeepEMC², a deep ensemble multitask model integrating four subnetworks: three specialized to context, clinical and text data, respectively, and another to ensemble the former. The four subnetworks are composed in turn by multi-layer perceptron modules, bidirectional long short-term memory units and a bidirectional encoding representations from transformers module. DeepEMC² showed a macro F1-score of 0.759 in life-threatening classification, 0.576 in admissible response delay and 0.757 in emergency system jurisdiction. These results show a substantial performance increase of 12.5 %, 17.5 % and 5.1 %, respectively, with respect to the current in-house triage protocol of the Valencian emergency medical dispatch service. Besides, DeepEMC² significantly outperformed a set of baseline machine learning models, including naive bayes, logistic regression, random forest and gradient boosting (α = 0.05). Hence, DeepEMC² is able to: 1) capture information present in emergency medical calls not considered by the existing triage protocol, and 2) model complex data dependencies not feasible by the tested baseline models. Likewise, our results suggest that most of this unconsidered information is present in the free text dispatcher observations. To our knowledge, this study describes the first deep learning model undertaking emergency medical call incidents classification. Its adoption in medical dispatch centers would potentially improve emergency dispatch processes, resulting in a positive impact in patient wellbeing and health services sustainability.

这项工作的目的是开发一个预测模型，以帮助非临床调度员根据危及生命的级别（是/否）、可接受的响应延迟（不可延迟、分钟、小时、天）和紧急系统对紧急医疗呼叫事件进行分类实时管辖权（紧急系统/初级保健）。我们使用了 1 244 624 起来自西班牙巴伦西亚紧急医疗调度服务的独立事件，这些事件是从 2009 年到 2012 年的回顾性汇编，包括临床特征、人口统计学、环境因素和自由文本调度员观察。基于它们，我们设计并开发了 DeepEMC ²，一个集成了四个子网络的深度集成多任务模型：三个分别专门用于上下文、临床和文本数据，另一个用于集成前者。四个子网络依次由多层感知器模块、双向长短期记忆单元和来自转换器模块的双向编码表示组成。DeepEMC ²的宏观 F1 分数在危及生命的分类中为 0.759，在可允许的响应延迟中为 0.576，在紧急系统管辖中为 0.757。这些结果显示，相对于瓦伦西亚紧急医疗调度服务当前的内部分类协议，性能分别显着提高了 12.5%、17.5% 和 5.1%。此外，DeepEMC ²显着优于一组基线机器学习模型，包括朴素贝叶斯、逻辑回归、随机森林和梯度提升（α = 0.05）。因此，DeepEMC ²能够：1) 捕获现有分类协议未考虑的紧急医疗呼叫中存在的信息，以及 2) 对经测试的基线模型不可行的复杂数据依赖性进行建模。同样，我们的结果表明，大部分未经考虑的信息都存在于自由文本调度员的观察中。据我们所知，这项研究描述了第一个进行紧急医疗呼叫事件分类的深度学习模型。它在医疗调度中心的采用可能会改善紧急调度流程，从而对患者福祉和健康服务的可持续性产生积极影响。