Outputs (6)

Comparison of algorithms that detect drug side effects using electronic healthcare databases (2013)
Journal Article
Reps, J. M., Garibaldi, J. M., Aickelin, U., Soria, D., Gibson, J. E., & Hubbard, R. B. (2013). Comparison of algorithms that detect drug side effects using electronic healthcare databases. Soft Computing, 17(12), https://doi.org/10.1007/s00500-013-1097-4

The electronic healthcare databases are starting to become more readily available and are thought to have excellent potential for generating adverse drug reaction signals. The Health Improvement Network (THIN) database is an electronic healthcare dat... Read More about Comparison of algorithms that detect drug side effects using electronic healthcare databases.

A Novel Semisupervised Algorithm for Rare Prescription Side Effect Discovery (2013)
Journal Article
Reps, J. M., Garibaldi, J. M., Aickelin, U., Soria, D., Gibson, J. E., & Hubbard, R. B. (2014). A Novel Semisupervised Algorithm for Rare Prescription Side Effect Discovery. IEEE Journal of Biomedical and Health Informatics, 18(2), 537-547. https://doi.org/10.1109/JBHI.2013.2281505

Drugs are frequently prescribed to patients with the aim of improving each patient's medical state, but an unfortunate consequence of most prescription drugs is the occurrence of undesirable side effects. Side effects that occur in more than one in a... Read More about A Novel Semisupervised Algorithm for Rare Prescription Side Effect Discovery.

A methodology for automatic classification of breast cancer immunohistochemical data using semi-supervised Fuzzy c-means (2013)
Journal Article
Lai, D. T. C., Garibaldi, J. M., Soria, D., & Roadknight, C. M. (2014). A methodology for automatic classification of breast cancer immunohistochemical data using semi-supervised Fuzzy c-means. Central European Journal of Operations Research, 22(3), 475-499. https://doi.org/10.1007/s10100-013-0318-3

Previously, a semi-manual method was used to identify six novel and clinically useful classes in the Nottingham Tenovus Breast Cancer dataset. 663 out of 1,076 patients were classified. The objectives of our work is three folds. Firstly, our primary... Read More about A methodology for automatic classification of breast cancer immunohistochemical data using semi-supervised Fuzzy c-means.

A quantifier-based fuzzy classification system for breast cancer patients (2013)
Journal Article
Soria, D., Garibaldi, J. M., Green, A. R., Powe, D. G., Nolan, C. C., Lemetre, C., …Ellis, I. O. (2013). A quantifier-based fuzzy classification system for breast cancer patients. Artificial Intelligence in Medicine, 58(3), https://doi.org/10.1016/j.artmed.2013.04.006

Objectives:Recent studies of breast cancer data have identified seven distinct clinical phenotypes (groups) using immunohistochemical analysis and a range of different clustering techniques. Consensus between unsupervised classification algorithms ha... Read More about A quantifier-based fuzzy classification system for breast cancer patients.

Towards a more systematic approach to secure systems design and analysis (2013)
Journal Article
Miller, S., Appleby, S., Garibaldi, J. M., & Aickelin, U. (2013). Towards a more systematic approach to secure systems design and analysis. International Journal of Secure Software Engineering, 4(1), https://doi.org/10.4018/jsse.2013010102

The task of designing secure software systems is fraught with uncertainty, as data on uncommon attacks is limited, costs are difficult to estimate, and technology and tools are continually changing. Consequently, experts may interpret the security ri... Read More about Towards a more systematic approach to secure systems design and analysis.

Attributes for causal inference in electronic healthcare databases (2013)
Presentation / Conference Contribution
Reps, J., Garibaldi, J. M., Aickelin, U., Soria, D., Gibson, J. E., & Hubbard, R. B. (2013). Attributes for causal inference in electronic healthcare databases.

Side effects of prescription drugs present a serious issue.
Existing algorithms that detect side effects generally
require further analysis to confirm causality. In this paper
we investigate attributes based on the Bradford-Hill causality criteria... Read More about Attributes for causal inference in electronic healthcare databases.