Authors:
Alexis Mitelpunkt
;
Tal Galili
;
Netta Shachar
;
Mira Marcus-Kalish
and
Yoav Benjamini
Affiliation:
Tel Aviv University, Israel
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)