Patient crowd selection method, device and equipment and computer readable storage medium

Abstract

The invention relates to the field of big data and digital medical treatment, and provides a patient crowd selection method, device and equipment and a computer readable storage medium. The method comprises the following steps: acquiring medical record data of a plurality of patients, and importing the plurality of medical record data into a data table of a preset database according to a preset format; processing the plurality of arrays in the data table through a preset high-order function to obtain feature vector data corresponding to each piece of medical record data; generating multiple pieces of first bitmap data according to the feature vector data corresponding to each piece of medical record data; calling a preset bitmap algorithm to perform set operation on the plurality of first bitmap data to obtain target bitmap data; acquiring a plurality of target bits from the target bitmap data, and selecting a patient group from the plurality of patients according to the plurality of target bits. The invention further relates to the block chain, and aims to improve the selection efficiency of the patient crowd.

Description

technical field [0001] The present application relates to the technical field of big data, and in particular to a method, device, equipment, and computer-readable storage medium for group selection of patients. Background technique [0002] With the development of big data technology, the storage and computing capabilities of big data platforms, such as data computing engines such as mapreduce, hive, or impala, can be used to realize the circle selection of crowd packets. The existing big data technology creates a calculation task to realize the circle selection of crowd packages by using tags as the conditions for screening crowds, so as to screen out the desired crowds and deliver real-time advertisement information to the screened crowds. Since different advertisement information needs to be delivered to different groups of people, the conditions used to select different groups of people are varied. [0003] However, in the medical field, the patient group has a large am...

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Problems solved by technology

[0003] However, in the medical field, the patient group has a large amount of medical record data. When the medical record data of the patient group increases, for example, when the data volume of the medical record data reaches the level of 100 million, the above-mentioned data calculation engine cannot meet the quasi-real-time performance requirements of the business. , the circle selection efficiency of the patient population is low, which cannot meet the needs of rapid response

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