Alle Papers – Machine Learning im Gesundheitswesen

Trends in repeat prostatectomy after surgery for benign prostate disease: application of record linkage to healthcare outcomes

Multiple Instance Learning for Classification of Dementia in Brain MRI

COVID-19 Mortality Prediction Using Machine Learning-Integrated Random Forest Algorithm under Varying Patient Frailty

Diabetes Mellitus Disease Prediction and Type Classification Involving Predictive Modeling Using Machine Learning Techniques and Classifiers

Using electronic health record data to establish a chronic kidney disease surveillance system in China: protocol for the China Kidney Disease Network (CK-NET)-Yinzhou Study

We work with them? Healthcare workers interpretation of organizational relations mined from electronic health records

Using machine learning to identify major shifts in human gut microbiome protein family abundance in disease

Coherence models in schizophrenia

Semantic Structuring of and Information Extraction from Medical Documents Using the UMLS

LifeCode: A Deployed Application for Automated Medical Coding

EC-GAN: Inferring Brain Effective Connectivity via Generative Adversarial Networks

Smooth Bayesian network model for the prediction of future high-cost patients with COPD

Dynamic Treatment Regimen Estimation via Regression-Based Techniques: Introducing <i>R</i> Package <b>DTRreg</b>

Creation of Realistic Appearing Simulated Patient Cases Using the INTERNIST-1/QMR Knowledge Base and Interrelationship Properties of Manifestations

Predicting Diabetes Mellitus With Machine Learning Techniques Using Multi-Criteria Decision Making

Multi-Task Medical Concept Normalization Using Multi-View Convolutional Neural Network

Replication of a mortality prediction model in Dutch patients with COVID-19

Self-Reported SARS-CoV-2 Vaccination Is Consistent with Electronic Health Record Data among the COVID-19 Community Research Partnership

A Multiscale Interactive Recurrent Network for Time-Series Forecasting

Harnessing Electronic Health Records and Artificial Intelligence for Enhanced Cardiovascular Risk Prediction: A Comprehensive Review

"Your Model Is Predictive-- but Is It Useful?" Theoretical and Empirical Considerations of a New Paradigm for Adaptive Tutoring Evaluation.

Introduction to Explainable AI

Improved performance and robustness of multi-task representation learning with consistency loss between pretexts for intracranial hemorrhage identification in head CT

An evolutionary machine learning algorithm for cardiovascular disease risk prediction

Automatic Diagnosis of Alzheimer’s Disease and Mild Cognitive Impairment Based on CNN + SVM Networks with End‐to‐End Training