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Prediction of Cancer Symptom Trajectory Using Longitudinal Electronic Health Record Data and Long Short-Term Memory Neural Network
Journal article   Peer reviewed

Prediction of Cancer Symptom Trajectory Using Longitudinal Electronic Health Record Data and Long Short-Term Memory Neural Network

Sena Chae, W Nick Street, Naveenkumar Ramaraju and Stephanie Gilbertson-White
JCO clinical cancer informatics, Vol.8, e2300039
03/2024
DOI: 10.1200/CCI.23.00039
PMCID: PMC10948138
PMID: 38471054
url
https://pmc.ncbi.nlm.nih.gov/articles/PMC10948138/pdf/cci-8-e2300039.pdfView
Open Access

Abstract

Ability to predict symptom severity and progression across treatment trajectories would allow clinicians to provide timely intervention and treatment planning. However, such predictions are difficult because of sparse and inconsistent assessment, and simplistic measures such as the last observed symptom severity are often used. The purpose of this study is to develop a model for predicting future cancer symptom experiences on the basis of past symptom experiences. We performed a retrospective, longitudinal analysis using records of patients with cancer (n = 208) hospitalized between 2008 and 2014. A long short-term memory (LSTM)-based recurrent neural network, a linear regression, and random forest models were trained on previous symptoms experienced and used to predict future symptom trajectories. We found that at least one of three tested models (LSTM, linear regression, and random forest) outperform predictions based solely on the previous clinical observation. LSTM models significantly outperformed linear regression and random forest models in predicting nausea ( < .1) and psychosocial status ( < .01). Linear regression outperformed all models when predicting oral health ( < .01), while random forest outperformed all models when predicting mobility ( < .01) and nutrition ( < .01). We can successfully predict patients' symptom trajectories with a prediction model, built with sparse assessment data, using routinely collected nursing documentation. The results of this project can be applied to better individualize symptom management to support cancer patients' quality of life.
 Electronic Health Records Quality of Life Humans Memory, Short-Term Neoplasms Neural Networks, Computer Retrospective Studies

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