NeuMORE
(NeuroMotor Recovery Ontology)

Latest News


11/03/2010 - Paper Accepted
Our paper, "Ontology-Based Knowledge Acquisition for Neuromotor Functional Recovery in Stroke," has been accepted by the KEDDH workshop at IEEE International Conference on Bioinformatics & Biomedicine (BIBM 2010), Hong Kong, China, December, 2010.

10/25/2010 - Paper Accepted
Our paper, "NeuMORE: Ontology in Stroke Recovery," has been accepted by the IEEE International Conference on Bioinformatics & Biomedicine (BIBM 2010), Hong Kong, China, December, 2010.

08/18/2010 - Ontology Submitted to NCBO
The NeuMORE ontology has been submitted to the National Center for Biomedical Ontology (NCBO) BioPortal (NeuMORE in NCBO).

08/03/2010 - NeuMORE Framework
 
framework

Welcome to the NeuMORE Project


Hemiparesis is the most common impairment after stroke, and the initial severity of hemiparesis has been the strongest predictor of neural motor functional recovery level. However, the intervention response of stroke survivors does not always correlate with their initial level of impairment, which implies the existence of some other factors that may significantly affect stroke survivors' recovery process. It is critical to consider these factors in a principled, comprehensive way, so that physical rehabilitation (PR) researchers may predict which stroke survivors will respond best to therapy and, as a result, to determine if a particular type of therapy is a more optimal match. Currently, such prediction is mainly a manual process and remains a challenging task to PR researchers.


 Based upon a domain-specific ontology, NeuMORE (NeuroMotor Recovery Ontology), we propose a computing framework that aims to facilitate knowledge acquisition from existing sources via ontological techniques. It will assist PR researchers in better predicting stroke survivors' neural motor functional recovery level, and will help physical therapists customize most effective intervention therapy plans for individual stroke survivors.


Challenges to be handled include, but not limited to:

  • How to develop a formal knowledge representation model in the domain of stroke recovery?
  • How to combine the advantages from both top-down (knowledge-driven) and bottom-up (data-driven) approaches during the ontology development?
  • How will the ontological techniques help in mapping the electromyography (EMG) and temporal-distance measures to motion of whole body center of mass (COM)?
  • How to balance the pros and cons between a "shallow" semantic annotation and a "deep" annotation?
  • Is a centralized RDF data warehouse a better choice than a traditional relational data warehouse for annotated sources?
  • How to design a user-friendly interface that provides PR researchers a unified query-answering mechanism?

Our investigation in the aforementioned challenges is supposed to generate insights and broader impacts in the fields of (1) biomedical informatics, (2) biomechanics, (3) stroke recovery, and (4) ontological techniques including semantic annotation & integration.