Sparse statistical health monitoring: A novel variable selection approach to diagnosis and follow-up of individual patients
| Autor: | Lutgarde M. C. Buydens, Udo F. H. Engelke, Lionel Blanchet, Ron A. Wevers, Jasper Engel |
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| Přispěvatelé: | RS: NUTRIM - R4 - Gene-environment interaction, RS: NUTRIM - R3 - Respiratory & Age-related Health |
| Rok vydání: | 2017 |
| Předmět: |
0301 basic medicine
Computer science Feature selection Machine learning computer.software_genre Fault detection and isolation DISEASE Analytical Chemistry 03 medical and health sciences ROAD FAULT ISOLATION Health care Metabolomics CLASS-MODELING TECHNIQUES Spectroscopy Human disease diagnosis Mahalanobis distance business.industry Process Chemistry and Technology Dimensionality reduction Precision medicine Disorders of movement Donders Center for Medical Neuroscience [Radboudumc 3] DISCRIMINANT Variable smearing Computer Science Applications Identification (information) Multivariate statistical process monitoring 030104 developmental biology Sparse Mahalanobis distance Principal component analysis Metric (unit) Artificial intelligence Data mining business computer Software |
| Zdroj: | Chemometrics and Intelligent Laboratory Systems, 164, pp. 83-93 Chemometrics and Intelligent Laboratory Systems, 164, 83-93. Elsevier Science Chemometrics and Intelligent Laboratory Systems, 164, 83-93 |
| ISSN: | 0169-7439 |
| Popis: | The -omics technologies are becoming increasingly important in health care and are expected to contribute to personalized health care. In a typical experiment, cases and controls are compared as a two-class classification problem. This approach is often unsuitable, for example, because the classes are not well defined due to associated populations being biologically too heterogeneous. Recently, statistical health monitoring (SHM) was introduced as a complementary approach to allow for predictions at the individual level. This approach could be of use in all sorts of applications such as diagnosis of rare diseases, analysis of individual patterns in disease manifestation, disease monitoring, or personalized therapy.SHM uses the framework of statistical process monitoring (SPM) in a clinical setting. The method essentially combines estimation of Mahalanobis distances (MD) with principal component analysis (PCA) to evaluate the difference in the -omics data of an individual subject to a normal reference range (normal operating conditions). It is well known from SPM, however, that reliable identification of the variables primarily responsible for this difference is hampered by the smearing effect, which is a result of the PCA step. To avoid this problem, we propose to combine estimation of the MD with variable selection via an 11-norm penalty instead of using dimension reduction. This way a sparse MD metric is obtained.The effectiveness of this method is illustrated by several simulation studies and its application to urine H-1-NMR metabolomics data for diagnosis of multiple inborn errors of metabolism. |
| Databáze: | OpenAIRE |
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