Summary of Dellirium: a Large Language Model For Delirium Prediction in the Icu Using Structured Ehr, by Miguel Contreras et al.
DeLLiriuM: A large language model for delirium prediction in the ICU using structured EHR
by Miguel Contreras, Sumit Kapoor, Jiaqing Zhang, Andrea Davidson, Yuanfang Ren, Ziyuan Guan, Tezcan Ozrazgat-Baslanti, Subhash Nerella, Azra Bihorac, Parisa Rashidi
First submitted to arxiv on: 22 Oct 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This study proposes a novel artificial intelligence (AI) model, DeLLiriuM, for predicting delirium in intensive care unit (ICU) patients. Delirium is an acute confusional state affecting up to 31% of ICU patients, and early detection could lead to more timely interventions and improved health outcomes. The authors use large language models (LLMs), with hundreds of millions to billions of parameters, combined with structured electronic health records (EHR) data to predict the probability of delirium development during the rest of a patient’s ICU admission. The model is developed and validated on three large databases, including the eICU Collaborative Research Database, MIMIC-IV, and University of Florida Health’s Integrated Data Repository, with an area under the receiver operating characteristic curve (AUROC) performance outperforming all baselines in two external validation sets. This study demonstrates the potential of LLM-based delirium prediction tools for ICU patients, providing helpful information to clinicians for timely interventions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Delirium is a serious condition that affects many people in hospitals. The goal is to detect it early so doctors can help them sooner. Right now, AI models are being used to predict delirium using hospital records. But most of these models have some limitations. This new study proposes a better way to use AI for predicting delirium. They combine big language models with medical records from many hospitals and test it on over 77,000 patients across 194 hospitals. The results show that this new method is really good at predicting delirium and can help doctors make better decisions. |
Keywords
» Artificial intelligence » Probability
