On predicting the outcomes of chemotherapy treatments in Breast cancer

Agastya Silvina, Juliana Kuster Filipe Bowles*, Peter Hall

*Corresponding author for this work

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

2 Citations (Scopus)
2 Downloads (Pure)


Chemotherapy is the main treatment commonly used for treating cancer patients. However, chemotherapy usually causes side effects some of which can be severe. The effects depend on a variety of factors including the type of drugs used, dosage, length of treatment and patient characteristics. In this paper, we use a data extraction from an oncology department in Scotland with information on treatment cycles, recorded toxicity level, and various observations concerning breast cancer patients for three years. The objective of our paper is to compare several different techniques applied to the same data set to predict the toxicity outcome of the treatment. We use a Markov model, Hidden Markov model, Random Forest and Recurrent Neural Network in our comparison. Through analysis and evaluation of the performance of these techniques, we can determine which method is more suitable in different situations to assist the medical oncologist in real-time clinical practice. We discuss the context of our work more generally and further work.
Original languageEnglish
Title of host publicationArtificial Intelligence in Medicine
Subtitle of host publication17th Conference on Artificial Intelligence in Medicine, AIME 2019, Poznan, Poland, June 26–29, 2019, Proceedings
EditorsDavid Riaño, Szymon Wilk, Annette ten Teije
Number of pages11
ISBN (Electronic)9783030216429
ISBN (Print)9783030216412
Publication statusPublished - 2019
EventAIME 2019 17th Conference on Artificial Intelligence in Medicine
- Poznan, Poland
Duration: 26 Jun 201929 Jun 2019
Conference number: 17

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11526 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


ConferenceAIME 2019 17th Conference on Artificial Intelligence in Medicine
Abbreviated titleAIME
Internet address


  • Breast cancer data
  • Toxicity prediction
  • Modelling
  • Machine learning


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