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dc.contributor.authorSilvina, Agastya
dc.contributor.authorKuster Filipe Bowles, Juliana
dc.contributor.authorHall, Peter
dc.contributor.editorRiaño, David
dc.contributor.editorWilk, Szymon
dc.contributor.editorten Teije, Annette
dc.identifier.citationSilvina , A , Kuster Filipe Bowles , J & Hall , P 2019 , On predicting the outcomes of chemotherapy treatments in Breast cancer . in D Riaño , S Wilk & A ten Teije (eds) , Artificial Intelligence in Medicine : 17th Conference on Artificial Intelligence in Medicine, AIME 2019, Poznan, Poland, June 26–29, 2019, Proceedings . Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) , vol. 11526 LNAI , Springer , pp. 180-190 , AIME 2019 17th Conference on Artificial Intelligence in Medicine , Poznan , Poland , 26/06/19 .
dc.identifier.otherPURE: 258191887
dc.identifier.otherPURE UUID: 66f93970-0c43-4c29-9ef9-c51077e6a191
dc.identifier.otherScopus: 85068330300
dc.identifier.otherORCID: /0000-0002-5918-9114/work/58755454
dc.identifier.otherWOS: 000495606500024
dc.description.abstractChemotherapy 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.
dc.relation.ispartofArtificial Intelligence in Medicineen
dc.relation.ispartofseriesLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)en
dc.rights© 2019, Springer Nature Switzerland AG. This work has been made available online in accordance with the publisher’s policies. This is the author created accepted version manuscript following peer review and as such may differ slightly from the final published version. The final published version of this work is available at
dc.subjectBreast cancer dataen
dc.subjectToxicity predictionen
dc.subjectMachine learningen
dc.subjectQA75 Electronic computers. Computer scienceen
dc.subjectRC0254 Neoplasms. Tumors. Oncology (including Cancer)en
dc.subjectRM Therapeutics. Pharmacologyen
dc.subjectComputer Science(all)en
dc.subjectTheoretical Computer Scienceen
dc.subjectSDG 3 - Good Health and Well-beingen
dc.titleOn predicting the outcomes of chemotherapy treatments in Breast canceren
dc.typeConference itemen
dc.contributor.sponsorEuropean Commissionen
dc.contributor.institutionUniversity of St Andrews. School of Computer Scienceen

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