Multimodal artificial intelligence-based decision support for stroke classification in medical emergency calls using call transcripts and patient health records: A retrospective diagnostic accuracy study

trIAje project: protocol for a retrospective cohort study to optimise AI-assisted telephone triage of time-sensitive conditions in emergency medical services

Abstract Introduction Emergency telephone triage must rapidly recognise cardiac arrest, severe respiratory distress, chest pain and stroke, yet current systems struggle to balance under- and over-triage. trIAje will evaluate present performance and develop an artificial intelligence (AI) model to improve triage in an e...

Machine learning-based early detection of abnormal heart rate in critically ill patients: a real-world clinical dataset study with automated alert system integration

Introduction Cardiovascular diseases (CVD) are a leading global health challenge, particularly in resource-constrained settings where delayed diagnosis worsens outcomes. Abnormal heart rate (tachycardia/bradycardia) is a modifiable, independent risk factor that benefits from early, automated detection. Methods We used...

Automated Derivation of Cerebral Performance Category at Hospital Discharge After Cardiac Arrest Using Natural Language Processing and Machine Learning.

Predicting Critical Outcomes in Suspected Cardiopulmonary Emergencies Using Dispatch Narratives: Temporal Validation Study

Abstract Background Early risk stratification in emergency medical services (EMS) is essential for patients presenting with acute cardiopulmonary symptoms, yet prehospital decision-making at the dispatch stage is often based on limited structured information. Free-text dispatch narratives may contain additional clinica...

Improved sepsis surveillance using a fully automated electronic health record-based algorithm compared to diagnostic coding.

BACKGROUND Accurate diagnostic coding of sepsis is essential for surveillance, resource allocation, and health policy planning. Studies assessing the usability of claims-based data (ICD-10 codes) compared to clinical criteria for sepsis surveillance are needed. OBJECTIVES To assess the concordance between ICD-10 diag...

Machine learning versus conventional methods for prehospital detection of stroke due to large vessel occlusion or intracranial haemorrhage.

INTRODUCTION Adequate prehospital triage of anterior-circulation LVO (aLVO) or ICH enables direct allocation to appropriate stroke centres. Traditional triage based on clinical scales or logistic regression may miss complex predictor interactions, whereas machine learning approaches may improve diagnostic accuracy. We...