Medical big data-based epidemiological research population screening method and storage medium

Abstract

The invention discloses an epidemiological research population screening method based on medical big data and a storage medium. The method comprises the following steps: acquiring health medical data; determining to-be-constructed target population types according to requirements of epidemiological research, wherein the to-be-constructed target population types comprise a cross section research population, a fixed queue research population and a dynamic queue research population; setting screening conditions including a research time range, follow-up visit time, a time window, an inclusion rule and an ending rule; and screening records meeting the requirements of the target crowd from the health medical data, and constructing the target population. According to the medical big data-based epidemiological research population screening method and the storage medium, the target population is directly screened from the existing massive medical data to be brought into epidemiological research, and the target population comprises a cross section research population, a fixed queue research population and a dynamic array research population; the efficiency and correctness of epidemiological research population screening based on medical big data are improved, and manpower is saved.

Description

technical field [0001] The invention belongs to the technical field of data processing, and in particular relates to a screening method and a storage medium for epidemiological research population based on medical big data. Background technique [0002] With the surge of electronic medical data, big data research has become a new hot spot in the field of scientific research. Although academic research on the theme of medical big data has been developed to varying degrees in different fields, the extraction and analysis of medical data is seriously insufficient. The clinical process model analysis function refers to the ability to analyze and improve the process data by using the big data analysis system. [0003] Population screening is an important step in clinical research, but the existing medical data has the characteristics of large volume, low standardization, low integration rate, and uneven quality. It is difficult for traditional medical data analysis systems to d...

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Problems solved by technology

[0003] Population screening is an important step in clinical research, but the existing medical data has the characteristics of large volume, low standardization, low integration rate, and uneven quality

It is difficult for traditional medical data analysis systems to directly screen out the target population from the data and analyze a large amount of data to discover the horizontal and vertical relationships between them.

In addition, manual screening of the population consumes a lot of energy, and various logical errors are prone to occur when the population is included, and the efficiency is still very low, which hinders the development of clinical research

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