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A Machine Learning Approach to Understand Complex Interstitial Lung Diseases

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Title A Machine Learning Approach to Understand Complex Interstitial Lung Diseases
Period 01 / 2007 - unknown
Status Current
Research number OND1322209
Data Supplier Website MICC

Abstract

The main subject is the application of machine learning techniques to improve understanding in complex interstitial lung diseases, in particular sarcoidosis. We hypothesize that sarcoidosis is not a single disease but a collection of genetically complex diseases with a wide degree of clinical heterogeneity. Knowledge of the individual groups (diseases) may be crucial in providing patients with effective treatments, for the prognosis of the disease as well as for future scientific research. Therefore, our primary goal is to find a categorization of sarcoidosis by means of unsupervised learning techniques. In addition to identifying the diagnostic categories, this research studies the problem of predicting the correct diagnostic class based on genetic information, especially SNPs. Another purpose of the study is to search for the optimal diagnostic policy for sarcoidosis patients using machine learning algorithms. An attempt will be made to generalize the results to other interstitial lung diseases.

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Related people

Supervisor Prof.dr. H.J. van den Herik
Supervisor Prof.dr. E.O. Postma
Doctoral/PhD student Ir. V. Karthaus

Classification

D21100 Bioinformatics, biomathematics, biomechanics
D23220 Internal medicine

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