ArticleAMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science2023
Natural Language Processing Methods to Identify Oncology Patients at High Risk for Acute Care with Clinical Notes.
Article in AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Review
- Cost-Benefit Analysis of Preventing Acute Care Use in Oncology Patients Following Systemic Therapy Using Medicare Claims Data: Retrospective Cohort Study.JMIR medical informatics · 2025Article
- Opportunities for Artificial Intelligence in Oncology: From the Lens of Clinicians and Patients.JCO oncology practice · 2025Review
- Ensemble learning to enhance accurate identification of patients with glaucoma using electronic health records.JAMIA open · 2025Article
- Data-Driven Defragmentation: Achieving Value-Based Sarcoma and Rare Cancer Care Through Integrated Care Pathway Mapping.Journal of personalized medicine · 2025Review
- Large language models in cancer: potentials, risks, and safeguards.BJR artificial intelligence · 2025Review
- Applying natural language processing to patient messages to identify depression concerns in cancer patients.Journal of the American Medical Informatics Association : JAMIA · 2024Article
- Automated stratification of trauma injury severity across multiple body regions using multi-modal, multi-class machine learning models.Journal of the American Medical Informatics Association : JAMIA · 2024Article
- The Role of Large Language Models in Transforming Emergency Medicine: Scoping Review.JMIR medical informatics · 2024Article
- Predicting Depression Risk in Patients With Cancer Using Multimodal Data: Algorithm Development Study.JMIR medical informatics · 2024Article
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Authors and funding
3 authors.
Funding
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Abstract
Clinical notes are an essential component of a health record. This paper evaluates how natural language processing (NLP) can be used to identify the risk of acute care use (ACU) in oncology patients, once chemotherapy starts. Risk prediction using structured health data (SHD) is now standard, but predictions using free-text formats are complex. This paper explores the use of free-text notes for the prediction of ACU in leu of SHD. Deep Learning models were compared to manually engineered language features. Results show that SHD models minimally outperform NLP models; an ℓ
Identifiers
37350895PMC10283145What OpenQuestion holds
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.