Authors:
Julie Cowie
;
Kevin Swingler
;
Clare Leadbetter
;
Roma Maguire
;
Kathryn McCall
and
Nora Kearney
Affiliation:
University of Stirling, United Kingdom
Keyword(s):
Risk modelling, side-effect prediction, cancer chemotherapy.
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
;
Enterprise Information Systems
;
Health Engineering and Technology Applications
;
Health Information Systems
;
Medical and Nursing Informatics
;
Sensor Networks
;
Signal Processing
;
Soft Computing
Abstract:
Patients undergoing chemotherapy want specific information on potential toxicities of their treatment. Such information includes what side-effects they are likely to experience, how severe these side-effects will be, how long they will experience them for, and the best ways of managing them. As well as improving the experiences of patients, information about potential side-effects may also be of significant benefit clinically, as patients who are ‘at risk’ of developing certain toxicities may be identified, facilitating more targeted, cost-effective interventions. This paper describes research that uses risk-modelling techniques for identifying patterns in patient side-effect data to aid in predicting side-effects patients are likely to experience. Through analysis of patient data, a patient can receive information specific to the symptoms they are likely to experience. A user-friendly software tool ASyMS©-SERAT (Advanced Symptom Management System-Side-Effect Risk Assessment Tool) ha
s been developed, which presents side-effect information to the patients both at the start of treatment and reviews and monitors predictions with each new cycle of chemotherapy received.
(More)