北京生物医学工程

基于多特征集成决策树算法的门诊需求预测

Outpatient demand forecasting based on multi-feature ensemble decision tree algorithm

作者：彭俊张肖建徐超谢勇项薇何达

单位：宁波大学机械工程与力学学院（浙江宁波315211）宁波大学先进储能技术与装备研究院（浙江宁波315211）宁波市鄞州区妇幼保健院（浙江宁波315211）

关键词：门诊需求预测; 多特征; GBDT; 随机森林; ARIMA

分类号：R318.04

出版年·卷·期（页码）：2021·40·1（68-73）

摘要：

目的为了准确预测医疗门诊需求量，以便医院管理者科学分配关键医疗资源，提高服务效率，本文提出一种基于多特征集成决策树的医疗需求预测模型。方法首先引入了机器学习算法中的梯度提升决策树（gradient boosting decision tree, GBDT）和随机森林（random forest, RF）。考虑外部因素对门诊人数的影响，根据宁波市某妇幼保健院的日产前检查人数的历史数据，引入前一天产前检查人数、时间、节假日、天气等特征，建立多特征日检查人数预测模型。预测结果与经典自回归移动平均模型(autoregressive integrated moving average model, ARIMA)模型进行对比。结果 GBDT、RF和ARIMA模型预测结果的平均绝对百分比误差（mean absolute percentage error, MAPE）分别是14.95%、17.16%、18.53%。结论集成决策树模型在医疗需求预测中的有效性和可行性，并且预测精度较传统的ARIMA模型高。

Objective To predict accurately the demand for medical outpatients, a medical demand forecasting model based on multi-feature ensemble decision tree is proposed. Thus, hospital administrators can scientifically allocate key medical resources and improve service efficiency. Methods Gradient boosting decision tree and random forest (GBDT and RF) in machine learning algorithms were introduced. We considered the influence of external factors on the number of outpatients,according to the historical data of the number of daily prenatal check-ups in a maternal and child health care hospital in Ningbo, the characteristics of the number of prenatal check-ups, time, holidays, weather and other characteristics of the previous day were introduced to establish a prediction model for the number of daily check-ups with multiple characteristics .The prediction results were compared with the classical ARIMA model. Results The average absolute percentage errors of the GBDT , RF and ARIMA model predictions were 14.95%, 17.16%, and 18.53%, respectively. Conclusions The effectiveness and feasibility of the ensemble decision tree model in medical demand forecasting is proven, and the prediction accuracy is higher than the traditional ARIMA model.

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