Evaluating the accuracy of a state-of-the-art large language model for prediction of admissions from the emergency room
Journal of the American Medical Informatics Association, ISSN: 1527-974X, Vol: 31, Issue: 9, Page: 1921-1928
2024
- 7Citations
- 49Captures
- 7Mentions
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
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Metrics Details
- Citations7
- Citation Indexes7
- CrossRef5
- Captures49
- Readers49
- 49
- Mentions7
- News Mentions7
- 7
Most Recent News
AI Spotlight: Leveraging Generative AI to Predict ER Admissions
Eyal Klang, MD, Associate Professor of Medicine, and Director of the Generative AI Research Program within the Division of Data-Driven and Digital Medicine (D3M), at
Article Description
Background: Artificial intelligence (AI) and large language models (LLMs) can play a critical role in emergency room operations by augmenting decision-making about patient admission. However, there are no studies for LLMs using real-world data and scenarios, in comparison to and being informed by traditional supervised machine learning (ML) models. We evaluated the performance of GPT-4 for predicting patient admissions from emergency department (ED) visits. We compared performance to traditional ML models both naively and when informed by few-shot examples and/or numerical probabilities. Methods: We conducted a retrospective study using electronic health records across 7 NYC hospitals. We trained Bio-Clinical-BERT and XGBoost (XGB) models on unstructured and structured data, respectively, and created an ensemble model reflecting ML performance. We then assessed GPT-4 capabilities in many scenarios: through Zero-shot, Few-shot with and without retrieval-augmented generation (RAG), and with and without ML numerical probabilities. Results: The Ensemble ML model achieved an area under the receiver operating characteristic curve (AUC) of 0.88, an area under the precision-recall curve (AUPRC) of 0.72 and an accuracy of 82.9%. The naïve GPT-4's performance (0.79 AUC, 0.48 AUPRC, and 77.5% accuracy) showed substantial improvement when given limited, relevant data to learn from (ie, RAG) and underlying ML probabilities (0.87 AUC, 0.71 AUPRC, and 83.1% accuracy). Interestingly, RAG alone boosted performance to near peak levels (0.82 AUC, 0.56 AUPRC, and 81.3% accuracy). Conclusions: The naïve LLM had limited performance but showed significant improvement in predicting ED admissions when supplemented with real-world examples to learn from, particularly through RAG, and/or numerical probabilities from traditional ML models. Its peak performance, although slightly lower than the pure ML model, is noteworthy given its potential for providing reasoning behind predictions. Further refinement of LLMs with real-world data is necessary for successful integration as decision-support tools in care settings.
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85201786452&origin=inward; http://dx.doi.org/10.1093/jamia/ocae103; http://www.ncbi.nlm.nih.gov/pubmed/38771093; https://academic.oup.com/jamia/article/31/9/1921/7676138; https://dx.doi.org/10.1093/jamia/ocae103; https://academic.oup.com/jamia/advance-article-abstract/doi/10.1093/jamia/ocae103/7676138?redirectedFrom=fulltext
Oxford University Press (OUP)
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