ArticleCommunications medicine2024
Predicting which patients with cancer will see a psychiatrist or counsellor from their initial oncology consultation document using natural language processing.
Article in Communications medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Analysing Emotional Well-Being in Cancer Patients: A Natural Language Processing Approach to Correlating Text with Hospital Anxiety and Depression Scale Scores.Current oncology (Toronto, Ont.) · 2026Article
- Investigating fine-tuning versus zero-shot learning for general large language models when predicting cancer survival from initial oncology consultation documents.ESMO real world data and digital oncology · 2026Article
- 5 Years of bipolar disorder conversations on Reddit: Methods, key topics and future directions.PloS one · 2026Article
- Understanding resilience in medical interns through ecological momentary assessments, predictive modelling, and topic analysis.Communications medicine · 2025Article
- Opportunities for Artificial Intelligence in Oncology: From the Lens of Clinicians and Patients.JCO oncology practice · 2025Review
- Article
- Review
- Exploring supportive care needs of lung cancer patients in China and predicting with machine learning models.Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer · 2025Article
- Virtual Waiting Room: The New Narrative of Waiting in Oncology Care.Journal of cancer education : the official journal of the American Association for Cancer Education · 2025Article
- Patients' attitudes toward artificial intelligence (AI) in cancer care: A scoping review protocol.PloS one · 2025Article
- Supervised machine learning applied in nursing notes for identifying the need of childhood cancer patients for psychosocial support.Frontiers in digital health · 2025Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundPatients with cancer often have unmet psychosocial needs. Early detection of who requires referral to a counsellor or psychiatrist may improve their care. This work used natural language processing to predict which patients will see a counsellor or psychiatrist from a patient's initial oncology consultation document. We believe this is the first use of artificial intelligence to predict psychiatric outcomes from non-psychiatric medical documents.
methodsThis retrospective prognostic study used data from 47,625 patients at BC Cancer. We analyzed initial oncology consultation documents using traditional and neural language models to predict whether patients would see a counsellor or psychiatrist in the 12 months following their initial oncology consultation.
resultsHere, we show our best models achieved a balanced accuracy (receiver-operating-characteristic area-under-curve) of 73.1% (0.824) for predicting seeing a psychiatrist, and 71.0% (0.784) for seeing a counsellor. Different words and phrases are important for predicting each outcome.
conclusionThese results suggest natural language processing can be used to predict psychosocial needs of patients with cancer from their initial oncology consultation document. Future research could extend this work to predict the psychosocial needs of medical patients in other settings.
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