AIMC Topic: Risk Assessment

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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...

Early Postoperative Prediction of Complications and Readmission After Colorectal Cancer Surgery Using an Artificial Neural Network.

Diseases of the colon and rectum
BACKGROUND: Early predictors of postoperative complications can risk-stratify patients undergoing colorectal cancer surgery. However, conventional regression models have limited power to identify complex nonlinear relationships among a large set of v...

Prostate cancer risk assessment and avoidance of prostate biopsies using fully automatic deep learning in prostate MRI: comparison to PI-RADS and integration with clinical data in nomograms.

European radiology
OBJECTIVES: Risk calculators (RCs) improve patient selection for prostate biopsy with clinical/demographic information, recently with prostate MRI using the prostate imaging reporting and data system (PI-RADS). Fully-automated deep learning (DL) anal...

An integrated ergonomic risk assessment framework based on fuzzy logic and IVSF-AHP for optimising ergonomic risks in a mixed-model assembly line.

Ergonomics
This study proposes a systematic approach to address ergonomic factors, including physical, environmental and psychosocial aspects, in solving assembly line balancing problems. A three-stage framework is developed, starting with determining weights f...

Quantifying the pollution changes and meteorological dependence of airborne trace elements coupling source apportionment and machine learning.

The Science of the total environment
Airborne trace elements (TEs) present in atmospheric fine particulate matter (PM) exert notable threats to human health and ecosystems. To explore the impact of meteorological conditions on shaping the pollution characteristics of TEs and the associa...

Development and Validation of a Machine Learning Algorithm to Predict the Risk of Blood Transfusion after Total Hip Replacement in Patients with Femoral Neck Fractures: A Multicenter Retrospective Cohort Study.

Orthopaedic surgery
OBJECTIVE: Total hip arthroplasty (THA) remains the primary treatment option for femoral neck fractures in elderly patients. This study aims to explore the risk factors associated with allogeneic blood transfusion after surgery and to develop a dynam...

Deep learning analysis for differential diagnosis and risk classification of gastrointestinal tumors.

Scandinavian journal of gastroenterology
OBJECTIVES: Recently, artificial intelligence (AI) has been applied to clinical diagnosis. Although AI has already been developed for gastrointestinal (GI) tract endoscopy, few studies have applied AI to endoscopic ultrasound (EUS) images. In this st...

Prediction of in-hospital Mortality of Intensive Care Unit Patients with Acute Pancreatitis Based on an Explainable Machine Learning Algorithm.

Journal of clinical gastroenterology
BACKGROUND AND AIM: Acute pancreatitis (AP) is potentially fatal. Therefore, early identification of patients at a high mortality risk and timely intervention are essential. This study aimed to establish an explainable machine-learning model for pred...