AIMC Topic: Acute Disease

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Artificial Intelligence Analysis of Chest Radiographs for Predicting Major Adverse Events in Patients Visiting the Emergency Department With Acute Cardiopulmonary Symptoms.

Korean journal of radiology
OBJECTIVE: In this study, we investigated whether artificial intelligence (AI) analysis of chest radiographs (CXRs) can predict major adverse clinical events in patients visiting the emergency department (ED) with acute cardiopulmonary symptoms.

Simultaneous T and ADC Mapping of Acute-to-Chronic Ischemic Stroke With Multiple Overlapping-Echo Detachment Imaging.

NMR in biomedicine
Multiparametric quantitative MRI based on multiple overlapping-echo detachment imaging (MQMOLED) can simultaneously quantify T and ADC with whole brain coverage within 40 s. T and ADC play an important role in the assessment and management of ischemi...

Predicting acute diarrhoea in rectal cancer chemoradiotherapy: Secondary analysis of the phase III ARISTOTLE trial.

Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology
BACKGROUND: Neoadjuvant chemoradiotherapy is a standard treatment for locally advanced rectal cancer, but acute diarrhoea remains a significant side effect, affecting the completion of chemoradiotherapy treatment.

Pseudotargeted metabolomics profiles potential damage-associated molecular patterns as machine learning predictors for acute pancreatitis.

Journal of pharmaceutical and biomedical analysis
Acute pancreatitis (AP) is a common gastrointestinal disease characterized by pancreatic cell damage and inflammation. Given the early clinical diagnosis and management challenges, exploring novel analytical frameworks from new orientations for inter...

Clinical phenotypes and risk of early hemodynamic deterioration in intermediate-high-risk patients with acute pulmonary embolism.

Thrombosis research
INTRODUCTION: Intermediate-high-risk pulmonary embolism (PE) patients face elevated risks of sudden clinical deterioration in early hours after symptoms onset. We performed a hierarchical cluster analysis among intermediate-high risk PE patients to i...

A machine learning approach for assessing acute infection by erythrocyte sedimentation rate (ESR) kinetics.

Clinica chimica acta; international journal of clinical chemistry
BACKGROUND: The erythrocyte sedimentation rate (ESR) is a traditional marker of inflammation, valued for its simplicity and low cost but limited by unsatisfactory specificity and sensitivity. This study evaluated the equivalence of ESR measurements o...

A Deep Learning Model for Identifying the Risk of Mesenteric Malperfusion in Acute Aortic Dissection Using Initial Diagnostic Data: Algorithm Development and Validation.

Journal of medical Internet research
BACKGROUND: Mesenteric malperfusion (MMP) is an uncommon but devastating complication of acute aortic dissection (AAD) that combines 2 life-threatening conditions-aortic dissection and acute mesenteric ischemia. The complex pathophysiology of MMP pos...

Machine learning models for predicting in-hospital mortality from acute pancreatitis in intensive care unit.

BMC medical informatics and decision making
BACKGROUND: Acute pancreatitis (AP) represents a critical medical condition where timely and precise prediction of in-hospital mortality is crucial for guiding optimal clinical management. This study focuses on the development of advanced machine lea...

A predictive model for hospital death in cancer patients with acute pulmonary embolism using XGBoost machine learning and SHAP interpretation.

Scientific reports
The prediction of in-hospital mortality in cancer patients with acute pulmonary embolism (APE) remains a significant clinical challenge. This study aimed to develop and validate a machine learning model using XGBoost to predict in-hospital mortality ...

Deep learning-based automatic differentiation of acute angle closure with or without zonulopathy using ultrasound biomicroscopy: a comparison of diagnostic performance with ophthalmologists.

BMJ open ophthalmology
OBJECTIVE: This study aims to develop ultrasound biomicroscopy (UBM)-based artificial intelligence (AI) models for preoperative differentiation of acute angle closure (AAC) with or without zonulopathy and to compare their comprehensive diagnostic per...