Big Data Application in Biomedical Research and Health Care: A Literature Review

By Jake Luo, Min Wu, Deepika Gopukumar, Yiqing Zhao. 2016

456 citations recorded by the source corpus.

Big data technologies are increasingly used for biomedical and health-care informatics research. Large amounts of biological and clinical data have been generated and collected at an unprecedented speed and scale. For example, the new generation of sequencing technologies enables the processing of billions of DNA sequence data per day, and the application of electronic health records (EHRs) is documenting large amounts of patient data. The cost of acquiring and analyzing biomedical data is expected to decrease dramatically with the help of technology upgrades, such as the emergence of new sequencing machines, the development of novel hardware and software for parallel computing, and the extensive expansion of EHRs. Big data applications present new opportunities to discover new knowledge and create novel methods to improve the quality of health care. The application of big data in health care is a fast-growing field, with many new discoveries and methodologies published in the last five years. In this paper, we review and discuss big data application in four major biomedical subdisciplines: (1) bioinformatics, (2) clinical informatics, (3) imaging informatics, and (4) public health informatics. Specifically, in bioinformatics, high-throughput experiments facilitate the research of new genome-wide association studies of diseases, and with clinical informatics, the clinical field benefits from the vast amount of collected patient data for making intelligent decisions. Imaging informatics is now more rapidly integrated with cloud platforms to share medical image data and workflows, and public health informatics leverages big data techniques for predicting and monitoring infectious disease outbreaks, such as Ebola. In this paper, we review the recent progress and breakthroughs of big data applications in these health-care domains and summarize the challenges, gaps, and opportunities to improve and advance big data applications in health care.

Subfields: Artificial Intelligence, Health Information Management, Information Systems and Management

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

Citations in the corpus

Reinforcement Learning in Healthcare: A Survey (2019, methodology): “methods and machine learning approaches in a variety of healthcare applications [5], [6], [7], [8], [9].”
Data Analysis Strategies in Medical Imaging (2018, background): “Buoyed by advancements in artificial intelligence (AI), statistical methodology, and image processing capabilities, the number of publications related to big data analysis of radiographic datasets is growing at an exponential pace (1, 2).”
Evaluation of a decided sample size in machine learning applications (2023, background): “Questions about generalization of this guideline and the best classifiers Biomedicine comprises divergent data types, for instance, imaging informatics, bioinformatics, clinical informatics, and public health informatics [41].”
Big Data technologies: A survey (2017, unknown): “Luo et al. (2016b) reviews and discuss big data application in four major biomedical subdisciplines: (1) bioinformatics, (2) clinical informatics, (3) imaging informatics, and (4) public health informatics.”
Deep Learning on 1-D Biosignals: a Taxonomy-based Survey (2018, background): “Due to the intrinsic properties of the biological systems, physiological biosignals are highly irregular, high-dimensional, composed from multi-components, non-stationary, and heterogeneous [13, 20].”
Data mining in clinical big data: the frequently used databases, steps, and methodological models (2021, background): “However, in medical research, the wide variety of clinical data and differences between several medical concepts in different classification standards results in a high degree of dimensionality heterogeneity, timeliness, scarcity, and irregularity to existing clinical data [6, 7].”