ArticleJCO clinical cancer informatics2022
Deep Learning for Cancer Symptoms Monitoring on the Basis of Electronic Health Record Unstructured Clinical Notes.
Article in JCO clinical cancer informatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 2 of them syntheses that pooled it.
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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.
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Who cites it
20 citing papers in PubMed, 2 syntheses or guidelines pooled it, 24 citations in OpenAlex.
- Cancer Risk Prediction Using Machine Learning for Supporting Early Cancer Diagnosis in Symptomatic Patients: A Systematic Review of Model Types.Cancer medicine · 2025Pooled 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
- Development of a benchmarking dataset for symptom detection using large language models.JAMIA open · 2026Article
- Review
- Leveraging Natural Language Processing for Symptom Identification in Acute Myeloid Leukemia Using Clinical Notes from Electronic Health Records.Cancer nursing · 2026Article
- Deep Learning Approaches to Forecast Physical and Mental Deterioration During Chemotherapy in Patients with Cancer.Diagnostics (Basel, Switzerland) · 2025Article
- Detection of differences in physical symptoms between depressed and undepressed patients with breast cancer: a study using K-medoids clustering.BMC cancer · 2025Article
- AI-Driven Prediction of Symptom Trajectories in Cancer Care: A Deep Learning Approach for Chemotherapy Management.Bioengineering (Basel, Switzerland) · 2024Article
- 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
- Symptom-BERT: Enhancing Cancer Symptom Detection in EHR Clinical Notes.Journal of pain and symptom management · 2024Article
- Assessing Real-World Data From Electronic Health Records for Health Technology Assessment: The SUITABILITY Checklist: A Good Practices Report of an ISPOR Task Force.Value in health : the journal of the International Society for Pharmacoeconomics and Outcomes Research · 2024Article
- Artificial intelligence to unlock real-world evidence in clinical oncology: A primer on recent advances.Cancer medicine · 2024Review
- Extraction of Unstructured Electronic Health Records to Evaluate Glioblastoma Treatment Patterns.JCO clinical cancer informatics · 2024Article
- Promise and Perils of Large Language Models for Cancer Survivorship and Supportive Care.Journal of clinical oncology : official journal of the American Society of Clinical Oncology · 2024Article
- Machine learning applied in patient-reported outcome research-exploring symptoms in adjuvant treatment of breast cancer.Breast cancer (Tokyo, Japan) · 2024Article
- Natural Language Processing for Radiation Oncology: Personalizing Treatment Pathways.Pharmacogenomics and personalized medicine · 2024Review
- Review: A Roadmap to Use Nonstructured Data to Discover Multitarget Cancer Therapies.JCO clinical cancer informatics · 2023Review
- Development of a keyword library for capturing PRO-CTCAE-focused "symptom talk" in oncology conversations.JAMIA open · 2023Article
- Developing a Data and Analytics Platform to Enable a Breast Cancer Learning Health System at a Regional Cancer Center.JCO clinical cancer informatics · 2023Article
Corrections and comments
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Authors and funding
10 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeSymptoms are vital outcomes for cancer clinical trials, observational research, and population-level surveillance. Patient-reported outcomes (PROs) are valuable for monitoring symptoms, yet there are many challenges to collecting PROs at scale. We sought to develop, test, and externally validate a deep learning model to extract symptoms from unstructured clinical notes in the electronic health record.
methodsWe randomly selected 1,225 outpatient progress notes from among patients treated at the Dana-Farber Cancer Institute between January 2016 and December 2019 and used 1,125 notes as our training/validation data set and 100 notes as our test data set. We evaluated the performance of 10 deep learning models for detecting 80 symptoms included in the National Cancer Institute's Patient-Reported Outcomes version of the Common Terminology Criteria for Adverse Events (PRO-CTCAE) framework. Model performance as compared with manual chart abstraction was assessed using standard metrics, and the highest performer was externally validated on a sample of 100 physician notes from a different clinical context.
resultsIn our training and test data sets, 75 of the 80 candidate symptoms were identified. The ELECTRA-small model had the highest performance for symptom identification at the token level (ie, at the individual symptom level), with an F1 of 0.87 and a processing time of 3.95 seconds per note. For the 10 most common symptoms in the test data set, the F1 score ranged from 0.98 for anxious to 0.86 for fatigue. For external validation of the same symptoms, the note-level performance ranged from F1 = 0.97 for diarrhea and dizziness to F1 = 0.73 for swelling.
conclusionTraining a deep learning model to identify a wide range of electronic health record-documented symptoms relevant to cancer care is feasible. This approach could be used at the health system scale to complement to electronic PROs.
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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.