Name
Abstract | ||
|---|---|---|
Clinical decision making frequently involves making decisions under uncertainty because of missing key patient data (e.g, demographics, episodic and clinical diagnosis details) - this information is essential for modern clinical decision support systems to perform learning, inference and prediction operations. Machine learning and clinical informatics experts aim to reduce this clinical uncertainty by learning from the missing clinical attributes with a view to improve the overall decision making. These high-dimensional clinical datasets are often complex and carry multifaceted patterns of key missing clinical attributes. In this paper we highlight the problem of learning from incomplete real patient data acquired from Raigmore Hospital in Scotland, UK) from a statistical perspective - the likelihood-based approach to deal with this challenging issue. There are multiple benefits of our approach: to complement existing SVM (Support Vector Machine) techniques to deal with missing data within a statistical framework, and to illustrate a set of challenging statistical machine learning algorithms, derived from the likelihood-based framework that handles clustering, classification, and function approximation from missing/incomplete data in an intelligent and resourceful manner. Our work concentrates on the implementation of mixture modelling algorithms as well as utilising Expectation-Maximization techniques for the estimation of mixture components and for dealing with the missing clinical data of chest pain patients. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/CICARE.2013.6583064 | Computational Intelligence in Healthcare and e-health |
Keywords | Field | DocType |
data handling,decision making,decision support systems,expectation-maximisation algorithm,health care,learning (artificial intelligence),medical information systems,patient diagnosis,support vector machines,Raigmore Hospital,SVM technique,Scotland,United Kingdom,chest pain patient,classification approximation,clinical decision making,clinical decision support system,clinical diagnosis detail,clinical informatics expert,clinical uncertainty reduction,clustering approximation,demographic data,episodic data,expectation-maximization technique,function approximation,high-dimensional clinical dataset,likelihood-based framework,missing clinical data,mixture modelling algorithm,patient data acquisition,statistical machine learning algorithm,support vector machine | Data science,Data modeling,Computer science,Support vector machine,Decision support system,Artificial intelligence,Missing data,Clinical decision support system,Health informatics,Cluster analysis,Group method of data handling,Machine learning | Conference |
Volume | Issue | ISSN |
null | null | null |
ISBN | Citations | PageRank |
978-1-4673-5882-8 | 2 | 0.41 |
References | Authors | |
2 | 7 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kamran Farooq | 1 | 12 | 2.63 |
| Peipei Yang | 2 | 27 | 3.09 |
| Amir Hussain | 3 | 672 | 67.84 |
| Kaizhu Huang | 4 | 1010 | 83.94 |
| Calum A. MacRae | 5 | 2 | 0.41 |
| Chris Eckl | 6 | 182 | 11.27 |
| Warner V. Slack | 7 | 46 | 10.38 |