TY - GEN
T1 - Predicting late symptoms of head and neck cancer treatment using LSTM and patient reported outcomes
AU - Wang, Yaohua
AU - Canahuate, Guadalupe M.
AU - Van Dijk, Lisanne V.
AU - Mohamed, Abdallah S.R.
AU - Fuller, Clifton David
AU - Zhang, Xinhua
AU - Marai, Georgeta Elisabeta
N1 - Publisher Copyright:
© 2021 Owner/Author.
PY - 2021/7/14
Y1 - 2021/7/14
N2 - 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.
AB - 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.
KW - Late Toxicity
KW - Long Short-Term Memory (LSTM)
KW - Patient Reported Outcomes (PRO)
KW - Symptom Severity Prediction
UR - http://www.scopus.com/inward/record.url?scp=85115105185&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85115105185&partnerID=8YFLogxK
U2 - 10.1145/3472163.3472177
DO - 10.1145/3472163.3472177
M3 - Conference contribution
C2 - 35392138
AN - SCOPUS:85115105185
T3 - ACM International Conference Proceeding Series
SP - 273
EP - 279
BT - IDEAS 2021 - 25th International Database Applications and Engineering Symposium
A2 - Desai, Bipin C.
PB - Association for Computing Machinery
T2 - 25th International Database Applications and Engineering Symposium, IDEAS 2021
Y2 - 14 July 2021 through 16 July 2021
ER -