Improving Prediction of Late Symptoms using LSTM and Patient-reported Outcomes for Head and Neck Cancer Patients

Authors

Yaohua Wang
Lisanne Van Dijk
Abdallah S R Mohamed
Mohamed Naser
Clifton David Fuller
Xinhua Zhang
G Elisabeta Marai
Guadalupe Canahuate

Publication Date

6-1-2023

Journal

IEEE International Conference on Healthcare Informatics (ICHI)

Abstract

Patient-Reported Outcomes (PRO) are collected directly from the patients using symptom questionnaires. In the case of head and neck cancer patients, PRO surveys are recorded every week during treatment with each patient's visit to the clinic and at different follow-up times after the treatment has concluded. PRO surveys can be very informative regarding the patient's status and the effect of treatment on the patient's quality of life (QoL). Processing PRO data is challenging for several reasons. First, missing data is frequent as patients might skip a question or a questionnaire altogether. Second, PROs are patient-dependent, a rating of 5 for one patient might be a rating of 10 for another patient. Finally, most patients experience severe symptoms during treatment which usually subside over time. However, for some patients, late toxicities persist negatively affecting the patient's QoL. These long-term severe symptoms are hard to predict and are the focus of this study. In this work, we model PRO data collected from head and neck cancer patients treated at the MD Anderson Cancer Center using the MD Anderson Symptom Inventory (MDASI) questionnaire as time series. We impute missing values with a combination of K nearest neighbor (KNN) and Long Short-Term Memory (LSTM) neural networks, and finally, apply LSTM to predict late symptom severity 12 months after treatment. We compare performance against clinical and ARIMA models. We show that the LSTM model combined with KNN imputation is effective in predicting late-stage symptom ratings for occurrence and severity under the AUC and F1 score metrics.

Keywords

Long Short-Term Memory (LSTM), Patient Reported Outcomes (PRO), Late Toxicity, Symptom Severity Prediction, KNN Baseline Imputation

DOI

10.1109/ichi57859.2023.00047

PMID

38343586

PMCID

PMC10853990

PubMedCentral® Posted Date

12-11-2024

PubMedCentral® Full Text Version

Author MSS

Published Open-Access

yes