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. 2022 Oct 25:16:1031732.
doi: 10.3389/fnins.2022.1031732. eCollection 2022.

A survey of data element perspective: Application of artificial intelligence in health big data

Honglin Xiong  1 , Hongmin Chen  1 , Li Xu  1 , Hong Liu  2 , Lumin Fan  2   3 , Qifeng Tang  4   5   6 , Hsunfang Cho  5   6
Affiliations

A survey of data element perspective: Application of artificial intelligence in health big data

Honglin Xiong et al. Front Neurosci. .

Abstract

Artificial intelligence (AI) based on the perspective of data elements is widely used in the healthcare informatics domain. Large amounts of clinical data from electronic medical records (EMRs), electronic health records (EHRs), and electroencephalography records (EEGs) have been generated and collected at an unprecedented speed and scale. For instance, the new generation of wearable technologies enables easy-collecting peoples' daily health data such as blood pressure, blood glucose, and physiological data, as well as the application of EHRs documenting large amounts of patient data. The cost of acquiring and processing health big data is expected to reduce dramatically with the help of AI technologies and open-source big data platforms such as Hadoop and Spark. The application of AI technologies in health big data presents new opportunities to discover the relationship among living habits, sports, inheritances, diseases, symptoms, and drugs. Meanwhile, with the development of fast-growing AI technologies, many promising methodologies are proposed in the healthcare field recently. In this paper, we review and discuss the application of machine learning (ML) methods in health big data in two major aspects: (1) Special features of health big data including multimodal, incompletion, time validation, redundancy, and privacy. (2) ML methodologies in the healthcare field including classification, regression, clustering, and association. Furthermore, we review the recent progress and breakthroughs of automatic diagnosis in health big data and summarize the challenges, gaps, and opportunities to improve and advance automatic diagnosis in the health big data field.

Keywords: artificial intelligence; automatic diagnosis; data elements; healthcare big data; healthcare informatics; machine learning.

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Conflict of interest statement

Authors QT and HFC were employed by Shanghai Data Exchange Corporation. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

FIGURE 1
FIGURE 1
Five special features of health big data.
FIGURE 2
FIGURE 2
Google file system architecture.
FIGURE 3
FIGURE 3
MapReduce work procedure.
FIGURE 4
FIGURE 4
Classification flowchart.
See this image and copyright information in PMC

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