---
title: "层次化时空Transformer用于连贯急诊科需求预测"
source: "arXiv AI/CL/LG（论文）"
source_tier: "t2"
url: "https://arxiv.org/abs/2607.27106v1"
permalink: "https://feed.astralor.com/items/itm_4d79891245020ee706f77267"
published: "2026-07-29T16:33:18.000Z"
score: 81
channel: "ai"
tags: ["HierSTT","Transformer","急诊科预测"]
---

# 层次化时空Transformer用于连贯急诊科需求预测

## AI 点评

> 这篇论文解决了医疗需求预测中一个长期被忽视的问题——跨层级预测的一致性。HierSTT通过精巧的层次化Transformer设计和连贯性损失，用实际数据证明了其效果。对于医疗AI研究者和医院运营管理人员来说，这是一个值得关注的实用框架。

## 速览

本文提出HierSTT，一种基于Transformer的层次化框架，用于联合预测医院、区域和国家三级的急诊科需求。该模型通过时序融合Transformer捕获国家动态，并利用时空Transformer编解码模块在更高级别预测条件下建模区域和医院需求。在包含81家医院、5个区域卫生管理局的葡萄牙全国数据集上，HierSTT相比最佳非层次化基线将平均加权绝对百分比误差降低32%，同时产生近乎连贯的跨层次预测。

## 关键点

- 提出HierSTT，一种端到端层次化Transformer框架，同时预测医院、区域和国家三级急诊需求。
- 引入连贯性感知损失函数，惩罚跨层级预测的不一致性。
- 在葡萄牙81家医院数据集上，相比现有方法平均WAPE降低32%，并保持预测连贯性。

## 全文

Emergency Departments (EDs) are critical access points in healthcare systems, yet they face persistent pressure from unpredictable patient demand, seasonal surges, and non-urgent visits. Effective ED planning requires forecasts at multiple decision-making levels: hospitals need local demand estimates for staffing and bed management, regions require forecasts to coordinate healthcare units, and national authorities need system-wide projections for capacity planning. However, most existing approaches forecast ED demand independently at a single level, ignoring the hierarchy linking hospitals, regions, and national systems. This can produce incoherent predictions, where hospital-level forecasts do not aggregate consistently to regional or national demand. We propose HierSTT, a hierarchical Transformer-based framework for coherent multi-level ED forecasting. HierSTT jointly predicts hospital, regional, and national level demand in a single end-to-end model. A Temporal Fusion Transformer captures national dynamics, while spatio-temporal Transformer encoder-decoder modules model regional and hospital demand conditioned on higher-level forecasts. A coherence-aware loss penalizes cross-level inconsistencies during training. We further introduce a nationwide Portuguese ED dataset covering 81 hospitals across 5 regional health administrations, with heterogeneous covariates at each level. Experiments show that HierSTT reduces average WAPE by 32\% relative to the best non-hierarchical deep learning baseline and outperforms all classical hierarchical reconciliation methods, while producing near-coherent predictions across levels. Additional resources associated with this work are available at https://github.com/FilipaLino/HierSTT.

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引用前请核对原文;本文由星际流动整理:https://feed.astralor.com/items/itm_4d79891245020ee706f77267