AIMC Topic: Risk Factors

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Machine Learning Based Prediction of Post-operative Infrarenal Endograft Apposition for Abdominal Aortic Aneurysms.

European journal of vascular and endovascular surgery : the official journal of the European Society for Vascular Surgery
OBJECTIVE: Challenging infrarenal aortic neck characteristics have been associated with an increased risk of type Ia endoleak after endovascular aneurysm repair (EVAR). Short apposition (< 10 mm circumferential shortest apposition length [SAL]) on th...

Random survival forest for predicting the combined effects of multiple physiological risk factors on all-cause mortality.

Scientific reports
Understanding the combined effects of risk factors on all-cause mortality is crucial for implementing effective risk stratification and designing targeted interventions, but such combined effects are understudied. We aim to use survival-tree based ma...

Validation of an Electronic Health Record-Based Machine Learning Model Compared With Clinical Risk Scores for Gastrointestinal Bleeding.

Gastroenterology
BACKGROUND & AIMS: Guidelines recommend use of risk stratification scores for patients presenting with gastrointestinal bleeding (GIB) to identify very-low-risk patients eligible for discharge from emergency departments. Machine learning models may o...

Deep learning prediction of survival in patients with heart failure using chest radiographs.

The international journal of cardiovascular imaging
Heart failure (HF) is associated with high rates of morbidity and mortality. The value of deep learning survival prediction models using chest radiographs in patients with heart failure is currently unclear. The aim of our study is to develop and val...

External validation of a 2-year all-cause mortality prediction tool developed using machine learning in patients with stage 4-5 chronic kidney disease.

Journal of nephrology
BACKGROUND: Chronic kidney disease (CKD) is associated with increased mortality. Individual mortality prediction could be of interest to improve individual clinical outcomes. Using an independent regional dataset, the aim of the present study was to ...

Exploring spatial heterogeneity in factors associated with injury severity in speeding-related crashes: An integrated machine learning and spatial modeling approach.

Accident; analysis and prevention
Speeding, a risky act of driving a vehicle at a speed exceeding the posted limit, has consistently emerged as a leading contributor to traffic fatalities. Identifying the risk factors associated with injury severity in speeding-related crashes is ess...

Artificial Intelligence in Urology: Application of a Machine Learning Model to Predict the Risk of Urolithiasis in a General Population.

Journal of endourology
This research presents our application of artificial intelligence (AI) in predicting urolithiasis risk. Previous applications, including AI for stone disease, have focused on stone composition and aiding diagnostic imaging. AI applications centered a...

Risk assessment and prediction of nosocomial infections based on surveillance data using machine learning methods.

BMC public health
BACKGROUND: Nosocomial infections with heavy disease burden are becoming a major threat to the health care system around the world. Through long-term, systematic, continuous data collection and analysis, Nosocomial infection surveillance (NIS) system...