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Authors: Alexis Mitelpunkt ; Tal Galili ; Netta Shachar ; Mira Marcus-Kalish and Yoav Benjamini

Affiliation: Tel Aviv University, Israel

ISBN: 978-989-758-068-0

Keyword(s): Medical Informatics, Bioinformatics, Disease Profiling, Disease Signature, Categorization, Clustering, Classification.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Biomedical Engineering ; Business Analytics ; Cardiovascular Technologies ; Computing and Telecommunications in Cardiology ; Data Engineering ; Data Mining ; Databases and Information Systems Integration ; Datamining ; Design and Development Methodologies for Healthcare IT ; Enterprise Information Systems ; Health Engineering and Technology Applications ; Health Information Systems ; Medical and Nursing Informatics ; Sensor Networks ; Signal Processing ; Soft Computing

Abstract: Health informatics is facing many challenges these days, in analysing current medical data and especially hospital data towards understanding disease mechanisms, predicting the course of a disease or assist in targeting potential therapeutic options. Alongside the promises, many challenges emerge. Among the major ones we identify: current diagnosis criteria that are too vague to capture disease manifestation; the irrelevance of personalized medicine when only heterogeneous classes of patients are available, and how to properly process big data to avoid false claims. We offer a 3C strategy that starts from the medical knowledge, categorizing the available set of features into three types: the patients' assigned disease diagnosis, clinical measurements and potential biological markers, proceeds to an unsupervised learning process targeted to create new disease diagnosis classes, and finally, classifying the newly proposed diagnosis classes utilizing the potential biological markers. In order to allow the evaluation and comparison of different algorithmic components of the 3C strategy a simulation model was built and put to use. Our strategy, developed as part of the medical informatics work package at the EU Human Brain flagship Project strives to connect between potential biomarkers, and more homogeneous classes of disease manifestation that are expressed by meaningful features. We demonstrate this strategy using data from the Alzheimer's Disease Neuroimaging Initiative cohort (ADNI). (More)

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Paper citation in several formats:
Mitelpunkt, A.; Galili, T.; Shachar, N.; Marcus-Kalish, M. and Benjamini, Y. (2015). Categorize, Cluster & Classify - The 3C Strategy Applied to Alzheimer's Disease as a Case Study.In Proceedings of the International Conference on Health Informatics - Volume 1: HEALTHINF, (BIOSTEC 2015) ISBN 978-989-758-068-0, pages 566-573. DOI: 10.5220/0005275705660573

@conference{healthinf15,
author={Alexis Mitelpunkt. and Tal Galili. and Netta Shachar. and Mira Marcus{-}Kalish. and Yoav Benjamini.},
title={Categorize, Cluster & Classify - The 3C Strategy Applied to Alzheimer's Disease as a Case Study},
booktitle={Proceedings of the International Conference on Health Informatics - Volume 1: HEALTHINF, (BIOSTEC 2015)},
year={2015},
pages={566-573},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005275705660573},
isbn={978-989-758-068-0},
}

TY - CONF

JO - Proceedings of the International Conference on Health Informatics - Volume 1: HEALTHINF, (BIOSTEC 2015)
TI - Categorize, Cluster & Classify - The 3C Strategy Applied to Alzheimer's Disease as a Case Study
SN - 978-989-758-068-0
AU - Mitelpunkt, A.
AU - Galili, T.
AU - Shachar, N.
AU - Marcus-Kalish, M.
AU - Benjamini, Y.
PY - 2015
SP - 566
EP - 573
DO - 10.5220/0005275705660573

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