Alle Papers – Machine Learning im Gesundheitswesen

Predicting hypertension control using machine learning

Explainable Argumentation for Wellness Consultation

Artificial intelligence in medicine

Modular Multi-Modal Attention Network for Alzheimer’s Disease Detection Using Patient Audio and Language Data

Challenges and Opportunities in Secondary Analyses of Electronic Health Record Data

Different Data Mining Approaches Based Medical Text Data

Considerations for the Use of Machine Learning Extracted Real-World Data to Support Evidence Generation: A Research-Centric Evaluation Framework

Simple models for estimating dementia severity using machine learning.

Synergistic Feature Engineering and Ensemble Learning for Early Chronic Disease Prediction

Data Leakage in Health Outcomes Prediction With Machine Learning. Comment on “Prediction of Incident Hypertension Within the Next Year: Prospective Study Using Statewide Electronic Health Records and Machine Learning”

hal9001: Scalable highly adaptive lasso regression in R

Assessing the similarity of surface linguistic features related to epilepsy across pediatric hospitals

Benefit of Digital Tools Used for Integrated Personalized Diabetes Management: Results From the PDM-ProValue Study Program

Healthcare Trust Evolution with Explainable Artificial Intelligence: Bibliometric Analysis

Requirements analysis for an AI-based clinical decision support system for general practitioners: a user-centered design process

Artificial Intelligence Algorithms for Treatment of Diabetes

Artificial Intelligence in Medicine: Text Mining of Health Care Workers’ Opinions

A scoping review of publicly available language tasks in clinical natural language processing

Deep significance clustering: a novel approach for identifying risk-stratified and predictive patient subgroups

<title>Finite-sample effects and resampling plans: applications to linear classifiers in computer-aided diagnosis</title>

Prediction performance of individual and ensemble learners for chronic kidney disease

Deep Learning in Dynamic Modeling of Medical Imaging: A Review Study

Adapting State-of-the-Art Deep Language Models to Clinical Information Extraction Systems: Potentials, Challenges, and Solutions

Artificial intelligence: the future for cardiology

Transformer-based named entity recognition for parsing clinical trial eligibility criteria