ArticleScientific reports2023
Novel method for predicting nonvisible symptoms using machine learning in cancer palliative care.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 2 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
8 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Artificial Intelligence and Decision-Making in Oncology: A Review of Ethical, Legal, and Informed Consent Challenges.Current oncology reports · 2025Pooled it
- Artificial Intelligence and Machine Learning in Cancer Pain: A Systematic Review.Journal of pain and symptom management · 2024Pooled it
- Artificial intelligence in cancer care: Opportunities and challenges from a nursing perspective.Asia-Pacific journal of oncology nursing · 2026Article
- Application of Digital Technology in Palliative Care in Japan.Journal of hospice and palliative care · 2026Review
- Review
- Ethics of artificial intelligence prognostication in palliative care: perspectives from a national survey of palliative care physicians.BMJ supportive & palliative care · 2026Article
- Impact of Artificial Intelligence on the Care of Terminally Ill Patients.Healthcare (Basel, Switzerland) · 2026Review
- Understanding, Recognizing, and Managing Cancer-Related Fatigue Associated with Breast Cancer.Current treatment options in oncology · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
End-of-life patients with cancer may find expressing their symptoms difficult if they can no longer communicate verbally because of deteriorating health. In this study, we assessed these symptoms using machine learning, which has excellent predictive capabilities and has recently been applied in healthcare. We performed a retrospective clinical survey involving 213 patients with cancer from August 2015 to August 2016. We divided the reported symptoms into two groups-visible and nonvisible symptoms. We used decision tree analysis, an analytical machine learning method that organizes and analyzes information in the form of a tree diagram to visually represent the information structure. Our machine learning model used patient background data and visible symptoms to predict nonvisible symptoms: pain, dyspnea, fatigue, drowsiness, anxiety, delirium, inadequate informed consent, and spiritual issues. The highest and/or lowest values for prediction accuracy, sensitivity, and specificity were 88.0%/55.5%, 84.9%/3.3%, and 96.7%/24.1%, respectively. This work will facilitate better assessment and management of symptoms in patients with cancer. This study was the first to predict nonvisible symptoms using decision tree analyses for patients with cancer receiving palliative care. Notably, applications based on our results may assess symptoms to the same extent as healthcare professionals.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.