Computational Health Informatics in the Big Data Age: A Survey
Abstract
The explosive growth and widespread accessibility of digital health data have led to a surge of research activity in the healthcare and data sciences fields. The conventional approaches for health data management have achieved limited success as they are incapable of handling the huge amount of complex data with high volume, high velocity, and high variety. This article presents a comprehensive overview of the existing challenges, techniques, and future directions for computational health informatics in the big data age, with a structured analysis of the historical and state-of-the-art methods. We have summarized the challenges into four Vs (i.e., volume, velocity, variety, and veracity) and proposed a systematic data-processing pipeline for generic big data in health informatics, covering data capturing, storing, sharing, analyzing, searching, and decision support. Specifically, numerous techniques and algorithms in machine learning are categorized and compared. On the basis of this material, we identify and discuss the essential prospects lying ahead for computational health informatics in this big data age.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Fang et al. did a detailed survey on inference of computational methods in big data-based health informatics.
“A detailed survey on the inference of computational methods in the big data-based health informatics has been done by Fang et al. in [30].”
From Applications of Artificial Intelligence and Big Data Analytics in m-Health: A Healthcare System Perspective (2020) · cites this paper as [30]
Fang et al. focused on a novel framework called "Health informatics processing pipeline" which incorporates steps to obtain significant patterns from healthcare-based big data.
“They focused on a novel framework called “Health informatics processing pipeline” which incorporates various steps to obtain significant patterns from healthcare-based big data.”
From Applications of Artificial Intelligence and Big Data Analytics in m-Health: A Healthcare System Perspective (2020) · cites this paper as [30]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155 · see citing works
Big data analytics in healthcare: promise and potential
Raghupathi, W., & Raghupathi, V. (2014) · Health Information Science and Systems · doi:10.1186/2047-2501-2-3 · Cited by 3,034 · see citing works
Chen, M., Mao, S., & Liu, Y. (2014) · Mobile Networks and Applications · doi:10.1007/s11036-013-0489-0 · Cited by 2,913 · see citing works
Near-Optimal Hashing Algorithms for Approximate Nearest Neighbor in High Dimensions
Andoni, A., & Indyk, P. (2006) · 2006 47th Annual IEEE Symposium on Foundations of Computer Science (FOCS'06) · doi:10.1109/focs.2006.49 · Cited by 1,067 · see citing works
Banaee, H., Ahmed, M., & Loutfi, A. (2013) · Sensors · doi:10.3390/s131217472 · Cited by 452 · see citing works
Managing and Mining Sensor Data
(2013) · doi:10.1007/978-1-4614-6309-2 · Cited by 294 · see citing works
Using data mining techniques to predict hospitalization of hemodialysis patients
Yeh, J.-Y., Wu, T.-H., & Tsao, C.-W. (2011) · Decision Support Systems · doi:10.1016/j.dss.2010.11.001 · Cited by 110 · see citing works
High-throughput histopathological image analysis via robust cell segmentation and hashing
Zhang, X., Xing, F., Su, H., Yang, L., & Zhang, S. (2015) · Medical Image Analysis · doi:10.1016/j.media.2015.10.005 · Cited by 87 · see citing works
Robust medical ad hoc sensor networks (MASN) with wavelet-based ECG data mining
Hu, F., Jiang, M., Celentano, L., & Xiao, Y. (2008) · Ad Hoc Networks · doi:10.1016/j.adhoc.2007.09.002 · Cited by 74 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
khan, Z. F., & Alotaibi, S. R. (2020) · Journal of Healthcare Engineering · doi:10.1155/2020/8894694 · Cited by 235 · see citing works
Health Big Data Analytics: A Technology Survey
Harerimana, G., Jang, B., Kim, J. W., & Park, H. K. (2018) · IEEE Access · doi:10.1109/access.2018.2878254 · Cited by 119 · see citing works
A Survey of Sensors in Healthcare Workflow Monitoring
Antunes, R. S., Seewald, L. A., Rodrigues, V. F., Costa, C. A. D., Jr., L. G., Righi, R. R., Maier, A., Eskofier, B., Ollenschläger, M., Naderi, F., et al. (2019) · ACM Computing Surveys · doi:10.1145/3177852 · Cited by 48 · see citing works
Emergency drug procurement planning based on big-data driven morbidity prediction
Song, Q., Zheng, Y.-J., Huang, Y.-J., Xu, Z.-G., Sheng, W.-G., & Yang, J. (2019) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2018.2870879 · Cited by 24 · see citing works
- Authors
- Fang, R., Pouyanfar, S., Yang, Y., Chen, S.-C., & Iyengar, S. S.
- Affiliations
- Florida International University (US)
- Venue
- ACM Computing Surveys
- Published
- 2016-06-14
- Volume
- 49
- Issue
- 1
- Pages
- 1-36
- ISSN
- 0360-0300 (ISSN-L)
- 1557-7341
- OpenAlex
- W2437797956
- Cited by
- 210
- References
- 9
- Citations by year
- 2026: 5 · 2025: 7 · 2024: 10 · 2023: 12 · 2022: 19 · 2021: 35 · 2020: 29 · 2019: 31 · 2018: 34 · 2017: 25 · 2016: 3
- FWCI
- 52.86
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- ACM Computing Surveys (publishedVersion)
- Topics
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Social Sciences › Decision Sciences › Information Systems and Management › Big Data Technologies and Applications
- Keywords
- big data, digital health data, health data management, data pipelines, machine learning methods, clinical decision support, health data sharing
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
