基于环境气象因素影响的异常就诊量预测

doi:10.11959/j.issn.2096-0271.2018030

Abstract

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.

Key words: meteorological factor, random forest, abnormal detection

CLC Number:

TP391

Guangjun YU, Yun XIONG, Sijia PENG, Lu RUAN. Abnormal detection of hospital admissions based on meteorological factors[J]. Big Data Research, 2018, 4(3): 54-60.

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References 8

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就医人数描述	G(Y)	L(Y)
突增	G≥α	1
突减	G<β	-1
平稳	β≤G<α	0

名称	平均	标准误差	中位数	众数	标准差	方差	峰度	偏度	最小值	最大值
最高温度/℃	21.7	0.3	23.0	26.0	9.0	80.2	-0.8	-0.1	2.0	40.0
最低温度/℃	15.2	0.3	16.0	23.0	8.7	75.7	-1.1	-0.2	-2.0	31.0
平均温度/℃	18.5	0.3	19.5	24.0	8.7	76.4	-1.0	-0.2	0.0	35.5
就诊人数/人	145.8	2.9	130.0	70.0	76.5	5853.0	-0.1	0.8	11.0	412.0
PM2.5/	76.2	1.9	62.0	30.0	51.2	2619.4	6.9	2.1	12.0	465.0
SO₂/(μg·m^-3)	19.54	0.479	15.0	13.0	12.68	160.7	3.56	1.92	6.0	77.0
NO₂/(μg·m^-3)	56.57	0.889	52	47	23.49	551.83	-0.01	0.71	10	128
CO/(μg·m^-3)	20.39	0.3	18	16	7.94	63.1	6.42	2.04	10	76

N值		突增情况			突减情况			正常情况
N值	准确率		召回率	准确率		召回率	准确率		召回率
N=1	0.858		0.785	0.828		0.898	0.722		0.690
N=2	0.866		0.825	0.840		0.897	0.746		0.712
N=3	0.891		0.848	0.846		0.893	0.762		0.742
N=4	0.928		0.835	0.874		0.924	0.805		0.781
N=5	0.908		0.815	0.855		0.910	0.778		0.762
N=6	0.913		0.825	0.860		0.885	0.743		0.762
N=7	0.907		0.839	0.841		0.897	0.768		0.740

算法	突增预测准确率	突增预测召回率	突减预测准确率	突减预测召回率	正常情况预测准确率	正常情况预测召回率
随机森林	0.928	0.835	0.874	0.924	0.805	0.781
高斯朴素贝叶斯	0.723	0.894	0.835	0.896	0.756	0.546
SVM	0.941	0.873	0.856	0.909	0.810	0.773
K近邻	0.898	0.846	0.858	0.858	0.741	0.756
决策树	0.858	0.852	0.849	0.819	0.707	0.736
XGBoost	0.925	0.882	0.849	0.895	0.794	0.749
逻辑回归	0.828	0.901	0.857	0.914	0.813	0.677

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Abnormal detection of hospital admissions based on meteorological factors

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