ArticleAMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science2022
Using Natural Language Processing to Classify Serious Illness Communication with Oncology Patients.
Article in AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed, 11 citations in OpenAlex.
- Ethical issues in pain and palliation.Current opinion in anaesthesiology · 2024Review
- Detecting goals of care conversations in clinical notes with active learning.Journal of biomedical informatics · 2024Article
- Natural Language Processing for Radiation Oncology: Personalizing Treatment Pathways.Pharmacogenomics and personalized medicine · 2024Review
- A voice-based digital assistant for intelligent prompting of evidence-based practices during ICU rounds.Journal of biomedical informatics · 2023Article
- The Artificially Intelligent Teacher: Applying Natural Language Processing to Critical Care Education.ATS scholar · 2022Article
Corrections and comments
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Authors and funding
7 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
One core measure of healthcare quality set forth by the Institute of Medicine is whether care decisions match patient goals. High-quality "serious illness communication" about patient goals and prognosis is required to support patient-centered decision-making, however current methods are not sensitive enough to measure the quality of this communication or determine whether care delivered matches patient priorities. Natural language processing (NLP) offers an efficient method for identification and evaluation of documented serious illness communication, which could serve as the basis for future quality metrics in oncology and other forms of serious illness. In this study, we trained NLP algorithms to identify and characterize serious illness communication with oncology patients.
Identifiers
35854756PMC9285137W3193851305What 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.