Alle Papers – Machine Learning im Gesundheitswesen

Applying interpretable deep learning models to identify chronic cough patients using EHR data

PregGAN: A prognosis prediction model for breast cancer based on conditional generative adversarial networks

Patient-specific COVID-19 resource utilization prediction using fusion AI model

Semantic Interoperability of Electronic Health Records: Systematic Review of Alternative Approaches for Enhancing Patient Information Availability

Computer probability estimates of angiographic coronary artery disease: Transportability and comparison with cardiologists' estimates

Radiological classification of dementia from anatomical MRI assisted by machine learning-derived maps

Memory for remote events in alcoholics.

Clinical applications of artificial intelligence and machine learning in the modern cardiac intensive care unit

Use of Machine Learning and Artificial Intelligence Methods in Geriatric Mental Health Research Involving Electronic Health Record or Administrative Claims Data: A Systematic Review

Large Language Models are Few-Shot Health Learners

HealthPrompt: A Zero-shot Learning Paradigm for Clinical Natural Language Processing

Interpretability of a deep learning model in the application of cardiac MRI segmentation with an ACDC challenge dataset

Applying representational state transfer (REST) architecture to archetype-based electronic health record systems

Estimation of Operating Characteristics for Dependent Diagnostic Tests Based on Latent Markov Models

Artificial intelligence and computer simulation models in critical illness

Language Model Adaptation

Breast cancer classification by a new approach to assessing deep neural network-based uncertainty quantification methods

An Intelligent Learning System for Unbiased Prediction of Dementia Based on Autoencoder and Adaboost Ensemble Learning

Computational Cognitive Assessment: Investigating the Use of an Intelligent Virtual Agent for the Detection of Early Signs of Dementia

Incorporating Explainable Artificial Intelligence (XAI) to aid Understanding of Machine Learning in the Healthcare Domain

Big Data and diabetes: the applications of Big Data for diabetes care now and in the future

Prediction Model of Type 2 Diabetes Mellitus for Oman Prediabetes Patients Using Artificial Neural Network and Six Machine Learning Classifiers

JMbayes2: Extended Joint Models for Longitudinal and Time-to-Event Data

Two-Stage Machine Learning model for guideline development

Septic Shock Diagnosis by Neural Networks and Rule Based Systems