Using Natural Language Processing to Predict which Cancer Patients will see a Psychiatrist or Counsellor

Background/Significance

Patients with cancer often have unmet psychosocial needs(1). Early
detection of who might require referral to a counsellor or psychiatrist may help
improve their care. In this work, we investigated whether natural language
processing (NLP) can predict which patients will see a counsellor or psychiatrist
from a patient’s initial oncologist consultation document. NLP is the branch of
artificial intelligence that uses language models to accomplish tasks using written
text. A different application of NLP has recently been popularized through the
ChatGPT online question-answering system(2). In this work, we used traditional and
neural language models to predict whether patients will see a psychiatrist or
counsellor based on their initial oncologist consultation document.

Methods

This retrospective prognostic study used data from 47,625 of 59,800
patients who received initial cancer care at any of the 6 BC Cancer sites located in
the province of British Columbia between April 1, 2011 and December 31, 2016. We
excluded patients with multiple cancer diagnoses, or those that did not see a
medical or radiation oncologist within 180 days of their cancer diagnosis. We
trained neural language models to predict which patients would see a psychiatrist
or counsellor in the twelve months following their initial oncologist consultation. We
used the non-neural language model bag-of-words, as well as three neural models:
convolutional neural networks (CNN), long-short term memory, and bidirectional
encoder representations from transformers.

Results

The models performed similar to or better than previous applications of
artificial intelligence in predicting psychosocial needs. Our best-performing models,
utilizing CNNs, achieved a balanced accuracy above 72% and a receiver-operator
area-under-curve (AUC) above 0.80 when predicting which patients would see a
psychiatrist. Performance was lower for predicting which patients would see a
counsellor, with balanced accuracy and AUC exceeding 70% and 0.75,
respectively. We examined what words or phrases our models used to make the
predictions, finding symptom burden, certain cancers, and family history of cancer
were positive predictors of future referral to psychosocial services.

Conclusions
Our results suggest NLP can be used to predict which cancer
patients require referral to a psychiatrist or counsellor from their initial oncologist
consultation. Future research may be able to extend our work to predict other
needs of cancer patients such as palliative care, or other cancer outcomes more
generally.

References
1. Lu D, Andersson TML, Fall K, et al. Clinical Diagnosis of Mental Disorders Immediately Before and After Cancer Diagnosis: A Nationwide Matched Cohort Study in Sweden. JAMA Oncol. 2016;2(9):1188-1196. doi:10.1001/jamaoncol.2016.0483
2. ChatGPT: Optimizing Language Models for Dialogue. OpenAI. Published November 30, 2022. Accessed January 27, 2023. https://openai.com/blog/chatgpt/