用元学习提升急诊就诊预测精度，融合23年多源数据。
Effective Predictive Modeling for Emergency Department Visits and Evaluating Exogenous Variables Impact: Using Explainable Meta-learning Gradient Boosting
- 构建元学习梯度提升框架，集成四种基础模型协同预测。
- 准确率达85.7%，较XGBoost等主流模型提升超50%。
- 可解释性强，适合医疗资源调度与政策评估场景。
在长达23年的跨度中，管理者和临床人员一直致力于精准预测急诊科（ED）就诊人数，以优化资源配置。尽管已有多种人工智能模型被用于精准预判，该任务仍面临泛化能力差、过拟合/欠拟合、可扩展性不足及超参数调优复杂等挑战。本研究提出一种新型元学习梯度提升模型（Meta-ED），用于精确预测每日急诊就诊量，并利用来自澳大利亚首都领地坎培拉医院的综合性外生变量数据集，涵盖社会人口学特征、医疗服务使用情况、慢性病、诊断信息及气候参数。Meta-ED由四种基础学习器（Catboost、Random Forest、Extra Tree、lightGBM）与一个顶层多层感知机（MLP）构成，整合各子模型优势。通过与23种模型的对比分析，结果显示Meta-ED在10项评估指标上均表现优异，准确率达85.7%（95% CI: 85.4%, 86.0%）。相较于XGBoost、Random Forest、AdaBoost、LightGBM和Extra Tree，其准确率分别提升58.6%、106.3%、22.3%、7.0%和15.7%。此外，引入气象相关特征使预测准确率提升3.25%。结果表明，Meta-ED可作为急诊就诊预测的通用基础模型。
原文摘要 · Abstract (English)
Over an extensive duration, administrators and clinicians have endeavoured to predict Emergency Department (ED) visits with precision, aiming to optimise resource distribution. Despite the proliferation of diverse AI-driven models tailored for precise prognostication, this task persists as a formidable challenge, besieged by constraints such as restrained generalisability, susceptibility to overfitting and underfitting, scalability issues, and complex fine-tuning hyper-parameters. In this study, we introduce a novel Meta-learning Gradient Booster (Meta-ED) approach for precisely forecasting daily ED visits and leveraging a comprehensive dataset of exogenous variables, including socio-demographic characteristics, healthcare service use, chronic diseases, diagnosis, and climate parameters spanning 23 years from Canberra Hospital in ACT, Australia. The proposed Meta-ED consists of four foundational learners-Catboost, Random Forest, Extra Tree, and lightGBoost-alongside a dependable top-level learner, Multi-Layer Perceptron (MLP), by combining the unique capabilities of varied base models (sub-learners). Our study assesses the efficacy of the Meta-ED model through an extensive comparative analysis involving 23 models. The evaluation outcomes reveal a notable superiority of Meta-ED over the other models in accuracy at 85.7% (95% CI ;85.4%, 86.0%) and across a spectrum of 10 evaluation metrics. Notably, when compared with prominent techniques, XGBoost, Random Forest (RF), AdaBoost, LightGBoost, and Extra Tree (ExT), Meta-ED showcases substantial accuracy enhancements of 58.6%, 106.3%, 22.3%, 7.0%, and 15.7%, respectively. Furthermore, incorporating weather-related features demonstrates a 3.25% improvement in the prediction accuracy of visitors' numbers. The encouraging outcomes of our study underscore Meta-ED as a foundation model for the precise prediction of daily ED visitors.
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