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Predicting the Recurrence of Common Bile Duct Stones After ERCP Treatment with Automated Machine Learning Algorithms
BackgroundRecurrence of common bile duct stones (CBDs) commonly happens after endoscopic retrograde cholangiopancreatography (ERCP). The clinical...
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MRI-based automated machine learning model for preoperative identification of variant histology in muscle-invasive bladder carcinoma
ObjectivesIt is essential yet highly challenging to preoperatively diagnose variant histologies such as urothelial carcinoma with squamous...
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A novel blood-based epigenetic biosignature in first-episode schizophrenia patients through automated machine learning
Schizophrenia (SCZ) is a chronic, severe, and complex psychiatric disorder that affects all aspects of personal functioning. While SCZ has a very...
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Automated machine learning for early prediction of acute kidney injury in acute pancreatitis
BackgroundAcute kidney injury (AKI) represents a frequent and grave complication associated with acute pancreatitis (AP), substantially elevating...
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Automated machine learning for the identification of asymptomatic COVID-19 carriers based on chest CT images
BackgroundAsymptomatic COVID-19 carriers with normal chest computed tomography (CT) scans have perpetuated the ongoing pandemic of this disease. This...
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Enhancing paranasal sinus disease detection with AutoML: efficient AI development and evaluation via magnetic resonance imaging
PurposeArtificial intelligence (AI) in the form of automated machine learning (AutoML) offers a new potential breakthrough to overcome the barrier of...
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Development of an individualized risk calculator of treatment resistance in patients with first-episode psychosis (TRipCal) using automated machine learning: a 12-year follow-up study with clozapine prescription as a proxy indicator
About 15–40% of patients with schizophrenia are treatment resistance (TR) and require clozapine. Identifying individuals who have higher risk of...
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Automated Multimodal Machine Learning for Esophageal Variceal Bleeding Prediction Based on Endoscopy and Structured Data
Esophageal variceal (EV) bleeding is a severe medical emergency related to cirrhosis. Early identification of cirrhotic patients with at a high risk...
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Evaluating an automated machine learning model that predicts visual acuity outcomes in patients with neovascular age-related macular degeneration
PurposeNeovascular age-related macular degeneration (nAMD) is a major global cause of blindness. Whilst anti-vascular endothelial growth factor...
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Natural Language Processing for Imaging Protocol Assignment: Machine Learning for Multiclass Classification of Abdominal CT Protocols Using Indication Text Data
A correct protocol assignment is critical to high-quality imaging examinations, and its automation can be amenable to natural language processing...
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Unmasking Risky Habits: Identifying and Predicting Problem Gamblers Through Machine Learning Techniques
The use of machine learning techniques to identify problem gamblers has been widely established. However, existing methods often rely on...
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Just Add Data: automated predictive modeling for knowledge discovery and feature selection
Fully automated machine learning (AutoML) for predictive modeling is becoming a reality, giving rise to a whole new field. We present the basic ideas...
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The role of machine learning algorithms in detection of gestational diabetes; a narrative review of current evidence
Gestational Diabetes Mellitus (GDM) poses significant health risks to mothers and infants. Early prediction and effective management are crucial to...
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DREAMER: a computational framework to evaluate readiness of datasets for machine learning
BackgroundMachine learning (ML) has emerged as the predominant computational paradigm for analyzing large-scale datasets across diverse domains. The...
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Classification of chest radiographs using general purpose cloud-based automated machine learning: pilot study
BackgroundWidespread implementation of machine learning models in diagnostic imaging is restricted by dearth of expertise and resources. General...
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White matter diffusion estimates in obsessive-compulsive disorder across 1653 individuals: machine learning findings from the ENIGMA OCD Working Group
White matter pathways, typically studied with diffusion tensor imaging (DTI), have been implicated in the neurobiology of obsessive-compulsive...
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Development of a machine learning-based acuity score prediction model for virtual care settings
ObjectiveHealthcare is increasingly digitized, yet remote and automated machine learning (ML) triage prediction systems for virtual urgent care use...
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Artificial intelligence for the diagnosis of clinically significant prostate cancer based on multimodal data: a multicenter study
BackgroundThe introduction of multiparameter MRI and novel biomarkers has greatly improved the prediction of clinically significant prostate cancer...
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Comparing code-free and bespoke deep learning approaches in ophthalmology
AimCode-free deep learning (CFDL) allows clinicians without coding expertise to build high-quality artificial intelligence (AI) models without...
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Development and Validation of Multimodal Models to Predict the 30-Day Mortality of ICU Patients Based on Clinical Parameters and Chest X-Rays
We aimed to develop and validate multimodal ICU patient prognosis models that combine clinical parameters data and chest X-ray (CXR) images. A total...
