Tag Archives: artificial intelligence

Artificial Intelligence: The Next Blockbuster Drug in Critical Care?

Razvan Azamfirei

Department of Anesthesiology and Critical Care Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA

DOI: 10.2478/jccm-2023-0017

The realm of critical care medicine is always waiting for the game-changing innovation – that elusive breakthrough poised to dramatically transform practice and yield remarkable results. Various contenders have come and gone: surfactant therapy, synthetic colloids, drotrecogin alfa [1-3]. While some have been consigned to the annals of history, a select few, such as low-tidal-volume ventilation, have endured [4]. Others re-emerge time and again, hoping that they’ll have an evidence-based role somewhere. The level of excitement is not always correlated with the staying power of the innovation, and sometimes the hype can overshadow the reality. [More]

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Using Machine Learning Techniques to Predict Hospital Admission at the Emergency Department

Georgios Feretzakis1,3, George Karlis2, Evangelos Loupelis1, Dimitris Kalles3, Rea Chatzikyriakou1, Nikolaos Trakas1, Eugenia Karakou1, Aikaterini Sakagianni1, Lazaros Tzelves1, Stavroula Petropoulou1, Aikaterini Tika1, Ilias Dalainas1, Vasileios Kaldis1

1 Sismanogleio General Hospital, Athens, Greece
2 Sotiria General Hospital of Chest Diseases of Athens, Athens, Greece
3 Hellenic Open University, Patras, Greece

DOI: 10.2478/jccm-2022-0003

Introduction: One of the most important tasks in the Emergency Department (ED) is to promptly identify the patients who will benefit from hospital admission. Machine Learning (ML) techniques show promise as diagnostic aids in healthcare.
Aim of the study: Our objective was to find an algorithm using ML techniques to assist clinical decision-making in the emergency setting.
Material and methods: We assessed the following features seeking to investigate their performance in predicting hospital admission: serum levels of Urea, Creatinine, Lactate Dehydrogenase, Creatine Kinase, C-Reactive Protein, Complete Blood Count with differential, Activated Partial Thromboplastin Time, DDimer, International Normalized Ratio, age, gender, triage disposition to ED unit and ambulance utilization. A total of 3,204 ED visits were analyzed.
Results: The proposed algorithms generated models which demonstrated acceptable performance in predicting hospital admission of ED patients. The range of F-measure and ROC Area values of all eight evaluated algorithms were [0.679-0.708] and [0.734-0.774], respectively. The main advantages of this tool include easy access, availability, yes/no result, and low cost. The clinical implications of our approach might facilitate a shift from traditional clinical decision-making to a more sophisticated model.
Conclusions: Developing robust prognostic models with the utilization of common biomarkers is a project that might shape the future of emergency medicine. Our findings warrant confirmation with implementation in pragmatic ED trials.

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