关键词: 云框架, 症状自查服务, 海量医疗数据, Hadoop, Lucene

Abstract:  With the increase of the sub-health people in current society, symptom self-inspection services have become more and more important. However, the establishment of the regional health cloud platform based on the EHR (electronic health record) provides the data support of the symptom self-inspection services. For example, people can find similar EHRs through this platform. As proposing the symptom self-inspection services based on the cloud framework, people face the challenge that a large amount of the EHRs should be acquired, stored, searched and analyzed. To solving those problems, this paper proposes the symptom self-inspection services based on the cloud framework. Firstly, this paper builds a Hadoop cluster to store and retrieve the massive medical data so that the response time of searching the EHRs can be improved. Secondly, this paper designs the distributed search node cluster based on the Lucene project to retrieve, analyze and filter the massive EHRs in real time. Moreover, this paper discusses the implementation of the symptom self-inspection services which includes the selection of searching nodes, the indexing of EHRs, the method of calculating the similarity of EHRs and the sorting algorithm. In the end, this paper conducts an experiment which proves the scalability and effectiveness of the proposed cloud framework.

Key words: cloud framework, symptom self-inspection services, massive medical data, Hadoop, Lucene