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Nunez, John-Jose Andres

University of British Columbia

Cancer is associated not only with mortality, but also with impacts on physical, mental, and social health. When unmet, these resulting psychosocial needs are associated with worsened quality-of-life and survival. Cancer centres employ psychiatrists, counsellors, and other allied health clinicians to help address these needs. However, these needs often go unmet even when these resources exist. It can be difficult for treating oncologists to detect these needs and refer patients to these resources. In this work, we investigated the use of neural natural language processing (NLP) models to predict these psychosocial needs using initial oncologist consultation documents. We compared a non-neural model, bag-of-words (BOW), with three neural models: convolutional neural networks (CNN), long-short term memory (LSTM), and bidirectional encoder representation from transformers (BERT). We used these models to predict self-reported emotional and informational needs around the time these documents were generated. We also used these models to predict whether the patient will have clinician-addressed needs – specifically, seeing a psychiatrist or counsellor within the five years following document generation. We compared the prediction of these psychosocial needs to predicting a non-psychosocial outcome, survival. We found these models can predict whether patients will see a psychiatrist with balanced accuracy and receiver-operator-area-under-curve (AUC) above 0.70. This is a similar performance to comparable prior work predicting mental health outcomes. We also predicted seeing a counsellor with AUC above 0.70, but predicting self-reported psychosocial needs seemed to be a more difficult task, with these metrics usually below 0.70. We predicted the non-psychosocial outcome, survival, with higher performance. For this task, balanced accuracy was above 0.80 and AUC above 0.90. Predictions using subsets of our study population suggest that predicting these psychosocial outcomes is easier in females, and with cancer patients diagnosed with a Stage II illness. We found that CNN and LSTM models performed the best, and investigated how BERT’s document size limit may hinder its performance on these tasks. This work is the first of its kind using NLP for this application, and builds a foundation to improve how these techniques may one day help cancer patients.

Text

2022-04-19

Attribution-NonCommercial-NoDerivatives 4.0 International

http://hdl.handle.net/2429/81233

Computer Science

University of British Columbia

UBCV

http://creativecommons.org/licenses/by-nc-nd/4.0/