An-Kwok Ian Wong

Feng Xie, Jun Zhou, Jin Wee Lee et al.

The demand for emergency department (ED) services is increasing across the globe, particularly during the current COVID-19 pandemic. Clinical triage and risk assessment have become increasingly challenging due to the shortage of medical resources and the strain on hospital infrastructure caused by the pandemic. As a result of the widespread use of electronic health records (EHRs), we now have access to a vast amount of clinical data, which allows us to develop predictive models and decision support systems to address these challenges. To date, however, there are no widely accepted benchmark ED triage prediction models based on large-scale public EHR data. An open-source benchmarking platform would streamline research workflows by eliminating cumbersome data preprocessing, and facilitate comparisons among different studies and methodologies. In this paper, based on the Medical Information Mart for Intensive Care IV Emergency Department (MIMIC-IV-ED) database, we developed a publicly available benchmark suite for ED triage predictive models and created a benchmark dataset that contains over 400,000 ED visits from 2011 to 2019. We introduced three ED-based outcomes (hospitalization, critical outcomes, and 72-hour ED reattendance) and implemented a variety of popular methodologies, ranging from machine learning methods to clinical scoring systems. We evaluated and compared the performance of these methods against benchmark tasks. Our codes are open-source, allowing anyone with MIMIC-IV-ED data access to perform the same steps in data processing, benchmark model building, and experiments. This study provides future researchers with insights, suggestions, and protocols for managing raw data and developing risk triaging tools for emergency care.

Mahmoud Alwakeel, Aditya Nagori, An-Kwok Ian Wong et al.

Large Language Models have been tested on medical student-level questions, but their performance in specialized fields like Critical Care Medicine (CCM) is less explored. This study evaluated Meta-Llama 3.1 models (8B and 70B parameters) on 871 CCM questions. Llama3.1:70B outperformed 8B by 30%, with 60% average accuracy. Performance varied across domains, highest in Research (68.4%) and lowest in Renal (47.9%), highlighting the need for broader future work to improve models across various subspecialty domains.

Mahmoud Alwakeel, Michael E. Yarrington, Rebekah H. Wrenn et al.

Antibiotic resistance poses a significant threat in in-patient settings with high mortality. Using MIMIC-III data, we generated Sentence-BERT embeddings from clinical notes and applied Neural Networks and XGBoost to predict antibiotic susceptibility. XGBoost achieved an average F1 score of 0.86, while Neural Networks scored 0.84. This study is among the first to use document embeddings for predicting antibiotic resistance, offering a novel pathway for improving antimicrobial stewardship.

Mahmoud Alwakeel, Emory Buck, Jonathan G. Martin et al.

Pulmonary embolism (PE) registries accelerate practice-improving research but depend on resource-intensive manual abstraction of radiology reports. We evaluated whether openly available large-language models (LLMs) can automate concept extraction from computed-tomography PE (CTPE) reports without sacrificing data quality. Four Llama-3 (L3) variants (3.0 8 B, 3.1 8 B, 3.1 70 B, 3.3 70 B) and two reviewer models Phi-4 (P4) 14 B and Gemma-3 27 B (G3) were tested on 250 dual-annotated CTPE reports each from MIMIC-IV and Duke University. Outcomes were accuracy, positive predictive value (PPV), and negative predictive value (NPV) versus a human gold standard across model sizes, temperature settings, and shot counts. Mean accuracy across all concepts increased with scale: 0.83 (L3-0 8 B), 0.91 (L3-1 8 B), and 0.96 for both 70 B variants; P4 14 B achieved 0.98; G3 matched. Accuracy differed by < 0.03 between datasets, underscoring external robustness. In dual-model concordance analysis (L3 70 B + P4 14 B), PE-presence PPV was >= 0.95 and NPV >= 0.98, while location, thrombus burden, right-heart strain, and image-quality artifacts each maintained PPV >= 0.90 and NPV >= 0.95. Fewer than 4% of individual concept annotations were discordant, and complete agreement was observed in more than 75% of reports. G3 performed comparably. LLMs therefore offer a scalable, accurate solution for PE registry abstraction, and a dual-model review workflow can further safeguard data quality with minimal human oversight.