The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.
A review of data mining using big data in health informatics
Open accessAbstract
The amount of data produced within Health Informatics has grown to be quite vast, and analysis of this Big Data grants potentially limitless possibilities for knowledge to be gained. In addition, this information can improve the quality of healthcare offered to patients. However, there are a number of issues that arise when dealing with these vast quantities of data, especially how to analyze this data in a reliable manner. The basic goal of Health Informatics is to take in real world medical data from all levels of human existence to help advance our understanding of medicine and medical practice. This paper will present recent research using Big Data tools and approaches for the analysis of Health Informatics data gathered at multiple levels, including the molecular, tissue, patient, and population levels. In addition to gathering data at multiple levels, multiple levels of questions are addressed: human-scale biology, clinical-scale, and epidemic-scale. We will also analyze and examine possible future work for each of these areas, as well as how combining data from each level may provide the most promising approach to gain the most knowledge in Health Informatics.
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Cites
A new look at the statistical model identification
Akaike, H. (1974) · IEEE Transactions on Automatic Control · doi:10.1109/tac.1974.1100705 · Cited by 51,879
The WEKA data mining software: an update
Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., & Witten, I. H. (2009) · ACM SIGKDD Explorations Newsletter · doi:10.1145/1656274.1656278 · Cited by 17,750
The use of the area under the ROC curve in the evaluation of machine learning algorithms
Bradley, A. P. (1997) · Pattern Recognition · doi:10.1016/s0031-3203(96)00142-2 · Cited by 7,217
Zhang, J.-H., Chung, T. D. Y., & Oldenburg, K. R. (1999) · SLAS Discovery · doi:10.1177/108705719900400206 · Cited by 6,847
Cancer Screening in Elderly Patients: A Framework for Individualized Decision Making
Walter, L. C., & Covinsky, K. E. (2001) · JAMA · doi:10.1001/jama.285.21.2750 · Cited by 1,003
Data Mining and Electronic Health Records: Selecting Optimal Clinical Treatments in Practice
Bennett, C., & Doub, T. (2011) · arXiv · doi:10.48550/arxiv.1112.1668 · Cited by 35 · cited 1×
“[25] there is about a 15±2 year gap between clinical research and the actual clinical care used in practice.”
Background
Cited by
Hybrid genetic algorithm and a fuzzy logic classifier for heart disease diagnosis
Reddy, G. T., Reddy, M. P. K., Lakshmanna, K., Rajput, D. S., Kaluri, R., & Srivastava, G. (2020) · Evolutionary Intelligence · doi:10.1007/s12065-019-00327-1 · Cited by 404
RetractedDeep Learning applications for COVID-19
Shorten, C., Khoshgoftaar, T. M., & Furht, B. (2021) · Journal of Big Data · doi:10.1186/s40537-020-00392-9 · Cited by 350
Self-citationSmart health: Big data enabled health paradigm within smart cities
Pramanik, M. I., Lau, R. Y. K., Demirkan, H., & Azad, M. A. K. (2017) · Expert Systems with Applications · doi:10.1016/j.eswa.2017.06.027 · Cited by 341
Big Data and Machine Learning Based Secure Healthcare Framework
Kaur, P., Sharma, M., & Mittal, M. (2018) · Procedia Computer Science · doi:10.1016/j.procs.2018.05.020 · Cited by 218
Computational Health Informatics in the Big Data Age: A Survey
Fang, R., Pouyanfar, S., Yang, Y., Chen, S.-C., & Iyengar, S. S. (2017) · ACM Computing Surveys · doi:10.1145/2932707 · Cited by 210
Metadata
- Authors
- Herland, M., Khoshgoftaar, T. M., & Wald, R.
- Venue
- Journal Of Big Data
- Published
- 2014-06-24
- Volume
- 1
- Issue
- 1
- ISSN
- 2196-1115 (ISSN-L)
- OpenAlex
- W2121784101
- Cited by
- 319
- References
- 6
- FWCI
- 48.60
- Type
- journal article
- Language
- en
- Open access
- gold
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
