Alle Papers – Machine Learning im Gesundheitswesen

eXplainable Artificial Intelligence (XAI) in aging clock models

Artificial neural network predictions of lengths of stay on a post-coronary care unit

Breaking away from labels: The promise of self-supervised machine learning in intelligent health

Bagging and Deep Learning in Optimal Individualized Treatment Rules

Clinical prediction models in the precision medicine era: old and new algorithms

Current state and prospects of artificial intelligence in allergy

Classification models for likelihood prediction of diabetes at early stage using feature selection

The cost of quality: Implementing generalization and suppression for anonymizing biomedical data with minimal information loss

Explainable quantum clustering method to model medical data

Improving Cardiovascular Disease Prediction Through Comparative Analysis of Machine Learning Models: A Case Study on Myocardial Infarction

Risk stratification in acute heart failure: Rationale and design of the STRATIFY and DECIDE studies

Predicting frequent emergency department visits among children with asthma using EHR data

Integrating prior knowledge to build transformer models

Automatic Deep Learning Semantic Segmentation of Ultrasound Thyroid Cineclips Using Recurrent Fully Convolutional Networks

Using Structured EHR Data and SVM to Support ICD-9-CM Coding

Ambient virtual scribes: Mutuo Health’s AutoScribe as a case study of artificial intelligence-based technology

Mining Health Examination Records—A Graph-Based Approach

A retrospective analysis of the utility of an artificial neural network to predict ED volume

Is Artificial Intelligence Better Than Human Clinicians in Predicting Patient Outcomes?

Explainable artificial intelligence to increase transparency for revolutionizing healthcare ecosystem and the road ahead

Mortality prediction system for heart failure with orthogonal relief and dynamic radius means

Artificial intelligence–enabled tools in cardiovascular medicine: A survey of current use, perceptions, and challenges

End-to-End Models to Imitate Traditional Chinese Medicine Syndrome Differentiation in Lung Cancer Diagnosis: Model Development and Validation

Comparison of Different Machine Learning Approaches to Predict Small for Gestational Age Infants

Large-Scale Discovery of Disease-Disease and Disease-Gene Associations