You are required to read and agree to the below before accessing a full-text version of an article in the IDE article repository.
The full-text document you are about to access is subject to national and international copyright laws. In most cases (but not necessarily all) the consequence is that personal use is allowed given that the copyright owner is duly acknowledged and respected. All other use (typically) require an explicit permission (often in writing) by the copyright owner.
For the reports in this repository we specifically note that
- the use of articles under IEEE copyright is governed by the IEEE copyright policy (available at http://www.ieee.org/web/publications/rights/copyrightpolicy.html)
- the use of articles under ACM copyright is governed by the ACM copyright policy (available at http://www.acm.org/pubs/copyright_policy/)
- technical reports and other articles issued by M‰lardalen University is free for personal use. For other use, the explicit consent of the authors is required
- in other cases, please contact the copyright owner for detailed information
By accepting I agree to acknowledge and respect the rights of the copyright owner of the document I am about to access.
If you are in doubt, feel free to contact webmaster@ide.mdh.se
A Multimodal approach for clinical diagnosis and treatment
Publication Type:
Doctoral Thesis
Publisher:
Mälardalen University, Västerås, Sweden
Abstract
A computer-aided Clinical Decision Support System (CDSS) for diagnosis and treatment often plays a
vital role and brings essential benefits for clinicians. Such a CDSS could function as an expert for a less
experienced clinician or as a second option/opinion of an experienced clinician to their decision making
task. Nevertheless, it has been a real challenge to design and develop such a functional system where
accuracy of the system performance is an important issue.
This research work focuses on development of intelligent CDSS based on a multimodal approach
for diagnosis, classification and treatment in medical domains i.e. stress and post-operative pain
management domains. Several Artificial Intelligence (AI) techniques such as Case-Based Reasoning
(CBR), textual Information Retrieval (IR), Rule-Based Reasoning (RBR), Fuzzy Logic and clustering
approaches have been investigated in this thesis work.
Patientâs data i.e. their stress and pain related information are collected from complex data sources
for instance, finger temperature measurements through sensor signals, pain measurements using
a Numerical Visual Analogue Scale (NVAS), patientâs information from text and multiple choice
questionnaires. The proposed approach considers multimedia data management to be able to use them
in CDSSs for both the domains.
The functionalities and performance of the systems have been evaluated based on close collaboration
with experts and clinicians of the domains. In stress management, 68 measurements from 46 subjects and
1572 patientsâ cases out of â4000 in post-operative pain have been used to design, develop and validate
the systems. In the stress management domain, besides the 68 measurement cases, three trainees and one
senior clinician also have been involved in order to conduct the experimental work. The result from the
evaluation shows that the system reaches a level of performance close to the expert and better than the
senior and trainee clinicians. Thus, the proposed CDSS could be used as an expert for a less experienced
clinician (i.e. trainee) or as a second option/opinion for an experienced clinician (i.e. senior) to their
decision making process in stress management. In post-operative pain treatment, the CDSS retrieves
and presents most similar cases (e.g. both rare and regular) with their outcomes to assist physicians.
Moreover, an automatic approach is presented in order to identify rare cases and 18% of cases from the
whole cases library i.e. 276 out of 1572 are identified as rare cases by the approach. Again, among the rare
cases (i.e. 276), around 57.25% of the cases are classified as âunusually badâ i.e. the average pain outcome
value is greater or equal to 5 on the NVAS scale 0 to 10. Identification of rear cases is an important part
of the PAIN OUT project and can be used to improve the quality of individual pain treatment.
Bibtex
@phdthesis{Ahmed2300,
author = {Mobyen Uddin Ahmed},
title = {A Multimodal approach for clinical diagnosis and treatment},
number = {113},
month = {November},
year = {2011},
school = {M{\"a}lardalen University, V{\"a}ster{\aa}s, Sweden},
url = {http://www.es.mdu.se/publications/2300-}
}