On predicting the outcomes of chemotherapy treatments in Breast cancer

Agastya Silvina; Juliana Kuster Filipe Bowles; Peter Hall

doi:10.1007/978-3-030-21642-9_24

On predicting the outcomes of chemotherapy treatments in Breast cancer

Agastya Silvina, Juliana Kuster Filipe Bowles^*, Peter Hall

^*Corresponding author for this work

School of Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

3 Citations (Scopus)

6 Downloads (Pure)

Abstract

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 language	English
Title of host publication	Artificial Intelligence in Medicine
Subtitle of host publication	17th Conference on Artificial Intelligence in Medicine, AIME 2019, Poznan, Poland, June 26–29, 2019, Proceedings
Editors	David Riaño, Szymon Wilk, Annette ten Teije
Publisher	Springer
Pages	180-190
Number of pages	11
ISBN (Electronic)	9783030216429
ISBN (Print)	9783030216412
DOIs	https://doi.org/10.1007/978-3-030-21642-9_24
Publication status	Published - 2019
Event	AIME 2019 17th Conference on Artificial Intelligence in Medicine - Poznan, Poland Duration: 26 Jun 2019 → 29 Jun 2019 Conference number: 17 http://aime19.aimedicine.info/

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	11526 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	AIME 2019 17th Conference on Artificial Intelligence in Medicine
Abbreviated title	AIME
Country/Territory	Poland
City	Poznan
Period	26/06/19 → 29/06/19
Internet address	http://aime19.aimedicine.info/

Keywords

Breast cancer data
Toxicity prediction
Modelling
Machine learning

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1007/978-3-030-21642-9_24

paper-ASJBPH
© 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 https://doi.org/10.1007/978-3-030-21642-9_24
Accepted author manuscript, 334 KB

SERUMS: SERUMS
Kuster Filipe Bowles, J. (PI)
European Commission
1/01/19 → 30/06/22
Project: Standard

Cite this

Silvina, 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 (pp. 180-190). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11526 LNAI). Springer. https://doi.org/10.1007/978-3-030-21642-9_24

Silvina, Agastya ; Kuster Filipe Bowles, Juliana ; Hall, Peter. / On predicting the outcomes of chemotherapy treatments in Breast cancer. Artificial Intelligence in Medicine: 17th Conference on Artificial Intelligence in Medicine, AIME 2019, Poznan, Poland, June 26–29, 2019, Proceedings. editor / David Riaño ; Szymon Wilk ; Annette ten Teije. Springer, 2019. pp. 180-190 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "On predicting the outcomes of chemotherapy treatments in Breast cancer",

abstract = "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.",

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booktitle = "Artificial Intelligence in Medicine",

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Silvina, 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. https://doi.org/10.1007/978-3-030-21642-9_24

On predicting the outcomes of chemotherapy treatments in Breast cancer. / Silvina, Agastya; Kuster Filipe Bowles, Juliana; Hall, Peter.
Artificial Intelligence in Medicine: 17th Conference on Artificial Intelligence in Medicine, AIME 2019, Poznan, Poland, June 26–29, 2019, Proceedings. ed. / David Riaño; Szymon Wilk; Annette ten Teije. Springer, 2019. p. 180-190 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11526 LNAI).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - On predicting the outcomes of chemotherapy treatments in Breast cancer

AU - Silvina, Agastya

AU - Kuster Filipe Bowles, Juliana

AU - Hall, Peter

N1 - Conference code: 17

PY - 2019

Y1 - 2019

N2 - 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.

AB - 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.

KW - Breast cancer data

KW - Toxicity prediction

KW - Modelling

KW - Machine learning

U2 - 10.1007/978-3-030-21642-9_24

DO - 10.1007/978-3-030-21642-9_24

M3 - Conference contribution

AN - SCOPUS:85068330300

SN - 9783030216412

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 180

EP - 190

BT - Artificial Intelligence in Medicine

A2 - Riaño, David

A2 - Wilk, Szymon

A2 - ten Teije, Annette

PB - Springer

T2 - AIME 2019 17th Conference on Artificial Intelligence in Medicine<br/>

Y2 - 26 June 2019 through 29 June 2019

ER -

Silvina A, Kuster Filipe Bowles J, Hall P. On predicting the outcomes of chemotherapy treatments in Breast cancer. In Riaño D, Wilk S, ten Teije A, editors, Artificial Intelligence in Medicine: 17th Conference on Artificial Intelligence in Medicine, AIME 2019, Poznan, Poland, June 26–29, 2019, Proceedings. Springer. 2019. p. 180-190. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). Epub 2019 May 30. doi: 10.1007/978-3-030-21642-9_24

On predicting the outcomes of chemotherapy treatments in Breast cancer

Abstract

Publication series

Conference

Keywords

UN SDGs

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Projects

SERUMS: SERUMS

Cite this