Abnormal detection of hospital admissions based on meteorological factors

Guangjun YU; Yun XIONG; Sijia PENG; Lu RUAN

doi:10.11959/j.issn.2096-0271.2018030

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Abnormal detection of hospital admissions based on meteorological factors

TOPIC：BIOMEDICAL BIG DATA | 更新时间：2024-06-03

- Abnormal detection of hospital admissions based on meteorological factors
- Big Data Research Vol. 4, Issue 3, Pages: 2018030(2018)
- 作者机构：
  
  1. 上海市儿童医院，上海 200040
  2. 上海交通大学医学院，上海 200025
  3. 复旦大学计算机科学技术学院，上海 200433
  4. 上海市数据科学重点实验室，上海 200433
  5. 复旦大学化学系，上海 200433
- 作者简介：
- 基金信息：
- DOI：10.11959/j.issn.2096-0271.2018030
  CLC： TP391
- Online First：2018-05，
  
  Published：15 May 2018
- 稿件说明：
移动端阅览
Guangjun YU, Yun XIONG, Sijia PENG, et al. Abnormal detection of hospital admissions based on meteorological factors[J]. Big Data Research, 2018, 4(3): 2018030.
DOI：

Guangjun YU, Yun XIONG, Sijia PENG, et al. Abnormal detection of hospital admissions based on meteorological factors[J]. Big Data Research, 2018, 4(3): 2018030. DOI： 10.11959/j.issn.2096-0271.2018030.

摘要

通过分析某儿童医院传染科就医人数异常（突增、突减）情况，建立就医人数与气象特征间的分类模型，实现对传染科就医突变情况的高准确率预测，以便院方合理调配科室、安排医生出诊人数。建立的模型对就医人数突增情况的预测准确率达到92.8%，召回率达到83.5%；对就医人数突减情况的预测准确率达到87.4%，召回率达到92.4%，并与多种分类器进行比较，实验表明该方法在预警传染科就诊人数的突变方面综合表现更佳。

Abstract

The hospital admission data from medicine department and infectious disease department of a hospital was analyzed and a classify model between the number of patients and meteorological factors was built.High accuracy of prediction in abnormal number of patients by utilizing random forest classifier was achieved

and decision support to Public Health Department was provided so that the hospital can make a reasonable allocation of doctors.All experiments were conducted on real data from the hospital and the results show that the final trained model achieve relatively high accuracy and recall.

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references

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