Predicting acute and late symptoms of head and neck cancer treatment using deep learning and longitudinal patient reported outcomes
Year: 2021
Type: dissertation
Abstract: In clinical treatments for cancer patients, Patient-Reported Outcome (PRO) surveys are mainly used to monitor patients' symptoms during and after treatment. In the course of their treatment, patients experience a number of symptoms with different severity and PRO surveys are used to reflect the patients' conditions during and after treatment. Acute symptoms, or symptoms experienced by the patients shortly after treatment, are sometimes a limiting factor that prevents completion of the initial treatment and requires changes in treatment's dose or duration. Late symptoms or symptoms experience some time after treatment, are used as a proxy of the patient's quality of life (QoL) after treatment. Researchers use PRO surveys to investigate the relationships among symptoms and predict symptoms to guide treatment selection and improve the long-term QoL of the patients. In the case of 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 sense, PRO data can be modeled as time-series data. In this thesis, we apply Long-Short Term Memory (LSTM) neural networks to PRO survey data in order to predict symptom ratings in both acute and late stages. An LSTM model is a type of recurrent neural network proven effective at predicting time-series data. In this work, the acute stage refers to week six during treatment and the late-stage up to 18 months after treatment. The data used in this project corresponds to MDASI questionnaires collected from head and neck cancer patients treated at the MD Anderson Cancer Center. We apply several methods for missing data imputation and evaluate the performance of the LSTM model for each method. We show that the LSTM model is effective in predicting symptom ratings under the RMSE and NRMSE metrics. In our experiments, the LSTM model also outperforms other machine learning models. We also show that the late-stage symptom rating has a smaller range than the acute-stage symptom rating.
DOI: 10.17077/etd.005874
Language: English
Topic: Cancer survivorship and care
Subfield: Oncology
Field: Medicine
Domain: Health Sciences
Open Access status: green
OpenAlex ID: W4255179360