Mina Attin (Nursing), Bryer Shareef (Computer Science), Xan Goodman (University Libraries), and Kavita Batra (Medicine), along with their student researchers, have published “” in Artificial Intelligence in Emergency Medicine. The study highlights the growing promise of machine learning in identifying patients at risk for in-hospital cardiac arrest using real-world EHR data, vital signs, laboratory biomarkers, and ECG monitoring. While several models demonstrated strong predictive performance, the authors emphasize the need for greater methodological standardization, external validation, and alignment with clinical workflows. This work exemplifies the power of interdisciplinary collaboration in advancing patient care through the use of advanced technologies in artificial intelligence.
