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Predicting late symptoms of head and neck cancer treatment using LSTM and patient reported outcomes

Authors:

Guadalupe M Canahuate,

Lisanne V Van Dijk,

Abdallah S. R. Mohamed,

Clifton David Fuller,

Georgeta-Elisabeta MaraiAuthors Info & Claims

IDEAS '21: Proceedings of the 25th International Database Engineering & Applications Symposium

Pages 273 - 279

https://doi.org/10.1145/3472163.3472177

Published: 07 September 2021 Publication History

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Abstract

Patient-Reported Outcome (PRO) surveys are used to monitor patients’ symptoms during and after cancer treatment. Late symptoms refer to those experienced after treatment. While most patients experience severe symptoms during treatment, these usually subside in the late stage. However, for some patients, late toxicities persist negatively affecting the patient’s quality of life (QoL). In the case of head and neck cancer patients, PRO surveys are recorded every week during the patient’s visit to the clinic and at different follow-up times after the treatment has concluded. In this paper, we model the PRO data as a time-series and apply Long-Short Term Memory (LSTM) neural networks for predicting symptom severity in the late stage. The PRO data used in this project corresponds to MD Anderson Symptom Inventory (MDASI) questionnaires collected from head and neck cancer patients treated at the MD Anderson Cancer Center. We show that the LSTM model is effective in predicting symptom ratings under the RMSE and NRMSE metrics. Our experiments show that the LSTM model also outperforms other machine learning models and time-series prediction models for these data.

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Cited By

Anyimadu EWang YFloricel CKamel SFuller CZhang XMarai GCanahuate G(2025)PRO-Based Stratification Improves Model Prediction for Toxicity and Survival of Head and Neck Cancer PatientsIEEE Journal of Biomedical and Health Informatics10.1109/JBHI.2024.351509229:2(807-814)Online publication date: Feb-2025
https://doi.org/10.1109/JBHI.2024.3515092
Finkelstein JSmiley AEcheverria CMooney K(2024)AI-Driven Prediction of Symptom Trajectories in Cancer Care: A Deep Learning Approach for Chemotherapy ManagementBioengineering10.3390/bioengineering1111117211:11(1172)Online publication date: 20-Nov-2024
https://doi.org/10.3390/bioengineering11111172
Zeinali NYoun NAlbashayreh AFan WGilbertson White S(2024)Machine Learning Approaches to Predict Symptoms in People With Cancer: Systematic ReviewJMIR Cancer10.2196/5232210(e52322)Online publication date: 19-Mar-2024
https://doi.org/10.2196/52322
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Information & Contributors

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Published In

cover image ACM Other conferences

IDEAS '21: Proceedings of the 25th International Database Engineering & Applications Symposium

July 2021

308 pages

ISBN:9781450389914

DOI:10.1145/3472163

Editors:
Bipin C. Desai
Concordia University, Canada
,
Bipin C. Desai
Concordia University, Canada
,
Jeffrey Ullman
Stanford University, USA
,
Richard McClatchey
Univ. Of West England, U.K
,
Motomichi Toyoma
Keio University, Japan

Copyright © 2021 Owner/Author.

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike International 4.0 License.

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Association for Computing Machinery

New York, NY, United States

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Published: 07 September 2021

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IDEAS 2021

IDEAS 2021: 25th International Database Engineering & Applications Symposium

July 14 - 16, 2021

QC, Montreal, Canada

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Overall Acceptance Rate 74 of 210 submissions, 35%

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Cited By

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https://doi.org/10.1109/JBHI.2024.3515092
Finkelstein JSmiley AEcheverria CMooney K(2024)AI-Driven Prediction of Symptom Trajectories in Cancer Care: A Deep Learning Approach for Chemotherapy ManagementBioengineering10.3390/bioengineering1111117211:11(1172)Online publication date: 20-Nov-2024
https://doi.org/10.3390/bioengineering11111172
Zeinali NYoun NAlbashayreh AFan WGilbertson White S(2024)Machine Learning Approaches to Predict Symptoms in People With Cancer: Systematic ReviewJMIR Cancer10.2196/5232210(e52322)Online publication date: 19-Mar-2024
https://doi.org/10.2196/52322
van der Heijden Tde Ligt KHubel Nvan der Mierden SHolzner Bvan de Poll-Franse Lde Rooij B(2024)Exploring the role of health-related quality of life measures in predictive modelling for oncology: a systematic reviewQuality of Life Research10.1007/s11136-024-03820-y34:2(305-323)Online publication date: 9-Dec-2024
https://doi.org/10.1007/s11136-024-03820-y
Anyimadu EFuller CZhang XMarai GCanahuate G(2024)Collaborative Filtering for the Imputation of Patient Reported OutcomesDatabase and Expert Systems Applications10.1007/978-3-031-68309-1_20(231-248)Online publication date: 18-Aug-2024
https://doi.org/10.1007/978-3-031-68309-1_20
Wójcik ZDimitrova VWarrington LVelikova GAbsolom K(2024)Patient-Centric Approach for Utilising Machine Learning to Predict Health-Related Quality of Life Changes During ChemotherapyArtificial Intelligence in Medicine10.1007/978-3-031-66538-7_12(101-116)Online publication date: 25-Jul-2024
https://doi.org/10.1007/978-3-031-66538-7_12
Zhou FHu SWan XLu ZWu J(2023)Risevi: A Disease Risk Prediction Model Based on Vision Transformer Applied to Nursing HomesElectronics10.3390/electronics1215320612:15(3206)Online publication date: 25-Jul-2023
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Or Rashid MShahriyar SShamrat FMahbub TTasnim ZAhmed M(2023)A Convolutional Neural Network Based Classification Approach for Breast Cancer Detection2023 7th International Conference on Trends in Electronics and Informatics (ICOEI)10.1109/ICOEI56765.2023.10125783(761-768)Online publication date: 11-Apr-2023
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Wang YDijk LMohamed ANaser MFuller CZhang XMarai GCanahuate G(2023)Improving Prediction of Late Symptoms using LSTM and Patient-reported Outcomes for Head and Neck Cancer Patients2023 IEEE 11th International Conference on Healthcare Informatics (ICHI)10.1109/ICHI57859.2023.00047(292-300)Online publication date: 26-Jun-2023
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