Preliminary study on exploring the trajectory of patients with COVID-19 by Data mining algorithms
10.3760/cma.j.cn113565-20200212-00013
- VernacularTitle: 采用数据挖掘算法研究新型冠状病毒患者活动轨迹初探
- Author:
Shuliang CHEN
1
;
Ce ZHANG
2
;
Ping REN
3
Author Information
1. Neurology of the Second Hospital of Dalian Medical University, Dalian 116027, Liaoning Province, China
2. Quality Management Department of the Second Hospital of Dalian Medical University, Dalian 116027, Liaoning Province, China
3. The Second Hospital of Dalian Medical University, Dalian 116027, Liaoning Province, China
- Publication Type:Journal Article
- Keywords:
COVID-19;
Epidemic prevention and control;
Data mining;
Python
- From:
Chinese Journal of Medical Science Research Management
2020;33(0):E005-E005
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To explore data mining methods and tools for the activity paths of confirmed patients, and provide data analysis tools for epidemic control.
Methods:The data used came from the trajectory data of confirmed cases collected by Tencent. The jieba word segmentation and word cloud map function of Python 3.6 were used to calculate the high-frequency vocabulary in the trajectory of confirmed patients. The epidemic prevention and control strategy was developed based on the high-frequency vocabulary.
Results:Taking Guangdong Province, the second most confirmed patients in the country, as an example, the key areas of epidemic control obtained through data mining involve Wuhan (epidemiological history), Zhuhai and Guangzhou. The key control activities include family visiting, traveling and shopping. Means of transportation include self-driving, trains and airplanes; the key patients studied were Li and Ding; the symptoms of this patient group were mainly fever and cough.
Conclusions:The data mining algorithm in this paper can provide an advantageous tool for epidemic prevention and control, also assist frontline personnel to adjust the deployment of epidemic prevention and control according to their priorities.
