ArticleJournal of pain and symptom management2022
Identification of Uncontrolled Symptoms in Cancer Patients Using Natural Language Processing.
Article in Journal of pain and symptom management, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 3 of them syntheses that pooled it.
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Who cites it
17 citing papers in PubMed, 3 syntheses or guidelines pooled it, 31 citations in OpenAlex.
- A systematic review of the assessment model for palliative care needs in cancer patients.Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer · 2026Pooled it
- Artificial Intelligence and Machine Learning in Cancer Pain: A Systematic Review.Journal of pain and symptom management · 2024Pooled it
- Using natural language processing to analyze unstructured patient-reported outcomes data derived from electronic health records for cancer populations: a systematic review.Expert review of pharmacoeconomics & outcomes research · 2024Pooled it
- Healthcare professionals' perceptions and needs for a Home-based Artificial Intelligence Pain Management System for cancer pain: A qualitative study.International journal of nursing sciences · 2026Article
- Review
- Development of a benchmarking dataset for symptom detection using large language models.JAMIA open · 2026Article
- Review
- Impact of Artificial Intelligence on the Care of Terminally Ill Patients.Healthcare (Basel, Switzerland) · 2026Review
- Review
- NRG Oncology Assessment of Artificial Intelligence for Automatic Treatment Planning in Radiation Therapy Clinical Trials: Present and Future.International journal of radiation oncology, biology, physics · 2025Article
- The ethical dimensions of utilizing Artificial Intelligence in palliative care.Nursing ethics · 2025Article
- CACER: Clinical concept Annotations for Cancer Events and Relations.Journal of the American Medical Informatics Association : JAMIA · 2024Article
- Natural Language Processing Accurately Differentiates Cancer Symptom Information in Electronic Health Record Narratives.JCO clinical cancer informatics · 2024Article
- Ethical issues in pain and palliation.Current opinion in anaesthesiology · 2024Review
- Using Clinician-Patient WeChat Group Communication Data to Identify Symptom Burdens in Patients With Uterine Fibroids Under Focused Ultrasound Ablation Surgery Treatment: Qualitative Study.JMIR formative research · 2023Article
- Different Machine Learning Approaches for Implementing Telehealth-Based Cancer Pain Management Strategies.Journal of clinical medicine · 2022Article
- Using a natural language processing toolkit to classify electronic health records by psychiatric diagnosis.Health informatics journalArticle
Corrections and comments
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Authors and funding
5 authors at 2 institutions in 1 country.
Funding
Abstract
contextFor patients with cancer, uncontrolled pain and other symptoms are the leading cause of unplanned hospitalizations. Early access to specialty palliative care (PC) is effective to reduce symptom burden, but more efficient approaches are needed for rapid identification and referral. Information on symptom burden largely exists in free-text notes, limiting its utility as a trigger for best practice alerts or automated referrals.
objectivesTo evaluate whether natural language processing (NLP) can be used to identify uncontrolled symptoms (pain, dyspnea, or nausea/vomiting) in the electronic health record (EHR) among hospitalized cancer patients with advanced disease.
methodsThe dataset included 1,644 hospitalization encounters for cancer patients admitted from 1/2017 -6/2019. We randomly sampled 296 encounters, which included 15,580 clinical notes. We manually reviewed the notes and recorded symptom severity. The primary endpoint was an indicator for whether a symptom was labeled as "controlled" (none, mild, not reported) or as "uncontrolled" (moderate or severe). We randomly split the data into training and test sets and used the Random Forest algorithm to evaluate final model performance.
resultsOur models predicted presence of an uncontrolled symptom with the following performance: pain with 61% accuracy, 69% sensitivity, and 46% specificity (F1: 69.5); nausea/vomiting with 68% accuracy, 21% sensitivity, and 90% specificity (F1: 29.4); and dyspnea with 80% accuracy, 22% sensitivity, and 88% specificity (F1: 21.1).
conclusionThis study demonstrated initial feasibility of using NLP to identify hospitalized cancer patients with uncontrolled symptoms. Further model development is needed before these algorithms could be implemented to trigger early access to PC.
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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.