ASYMS-SERAT: a side-effect risk assessment tool to predict chemotherapy related toxicity in patients with cancer receiving chemotherapy

Julie Cowie, Kevin Swingler, Clare Leadbetter, Roma Maguire, Kathryn McCall, Nora Kearney

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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 which 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 SERAT (Side-Effect Risk Assessment Tool) has 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.
Original languageEnglish
Title of host publicationProceedings of the First International Conference on Health Informatics (Volume 2)
EditorsLuis Azevedo, Ana Londrai
PublisherSciTePress
Pages225-230
Number of pages6
ISBN (Print)9789898111166
DOIs
Publication statusPublished - 30 Nov 2007

Keywords

  • Risk modelling
  • side-effect prediction
  • cancer
  • chemotherapy
  • Cancer Chemotherapy
  • Cancer Research
  • Health risk assessment

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    Cowie, J., Swingler, K., Leadbetter, C., Maguire, R., McCall, K., & Kearney, N. (2007). ASYMS-SERAT: a side-effect risk assessment tool to predict chemotherapy related toxicity in patients with cancer receiving chemotherapy. In L. Azevedo, & A. Londrai (Eds.), Proceedings of the First International Conference on Health Informatics (Volume 2) (pp. 225-230). SciTePress. https://doi.org/10.5220/0001044502250230