Machine Learning-Based Models for Prediction of Critical Illness at Community, Paramedic, and Hospital Stages

Abstract

Overcrowding of emergency department (ED) has put a strain on national healthcare systems and adversely affected the clinical outcomes of critically ill patients. Early identification of critically ill patients prior to ED visits can help induce optimal patient flow and allocate medical resources effectively. This study aims to develop ML-based models for predicting critical illness in the community, paramedic, and hospital stages using Korean National Emergency Department Information System (NEDIS) data. Random forest and light gradient boosting machine (LightGBM) were applied to develop predictive models. The predictive model performance based on AUROC in community stage, paramedic stage, and hospital stage was estimated to be 0.870 (95% CI: 0.869–0.871), 0.897 (95% CI: 0.896–0.898), and 0.950 (95% CI: 0.949–0.950) in random forest and 0.877 (95% CI: 0.876–0.878), 0.899 (95% CI: 0.898–0.900), and 0.950 (95% CI: 0.950–0.951) in LightGBM, respectively. The ML models showed high performance in predicting critical illness using variables available at each stage, which can be helpful in guiding patients to appropriate hospitals according to their severity of illness. Furthermore, a simulation model can be developed for proper allocation of limited medical resources.

Reimagining Core Entrustable Professional Activities for Undergraduate Medical Education in the Era of Artificial Intelligence

“…AI could significantly enhance these areas of medical education. For example, AI-driven predictive analytics could assist students in identifying patients needing urgent care [ 24 , 25 ], while AI-powered simulated reality simulations could offer opportunities to practice obtaining informed consent or performing procedures [ 26 ].…”

“…Current EPAs do not explicitly address the need for transdisciplinary team skills, yet the evolving nature of health care teams makes this a critical competency. Medical graduates need the ability to communicate effectively with a broad spectrum of professionals; understand their roles, expertise, and contributions; and collaborate with them to deliver optimal care for patients [ 25 , 42 ]. This extends beyond simply understanding the language and perspectives of other disciplines; it involves appreciating their contributions and integrating their expertise into patient care.…”

Predicting short-term outcomes in atrial-fibrillation-related stroke using machine learning

“…To overcome the limitations of conventional statistical and machine learning models, more accurate high-level machine learning techniques must be developed and applied (11). The machine-learning-based prediction of outcomes using information obtained during the early period after hospital arrival-including clinical, laboratory, and imaging findings-is a feasible method of formulating therapeutic plans and prognoses (11,12). In this study, machine learning models were constructed and validated for the prediction of short-term outcomes in AF-related stroke patients based on various features acquired during early hospitalization using two independent multicenter prospective hospital-based registries.…”