Alle Papers – Machine Learning im Gesundheitswesen

Critical assessment of transformer-based AI models for German clinical notes

The Stanford Medicine data science ecosystem for clinical and translational research

Explainable CNN-attention Networks (C-Attention Network) for Automated Detection of Alzheimer’s Disease

The Clinical Data Intelligence Project

An Interpretable Machine Learning Framework for Accurate Severe vs Non-Severe COVID-19 Clinical Type Classification

From ‘loose fitting’ to high-performance, uncertainty-aware brain-age modelling

Medical Provider Embeddings for Healthcare Fraud Detection

Predicting Onset of Dementia Using Clinical Notes and Machine Learning: Case-Control Study

Identification of patients who will not achieve seizure remission within 5 years on AEDs

Predicting the risk of stroke among patients with type 2 diabetes: a systematic review and meta-analysis of C-statistics

Using machine learning to selectively highlight patient information

Neural Cross-Lingual Coreference Resolution And Its Application To Entity Linking

EANDC: An explainable attention network based deep adaptive clustering model for mental health treatment

The TRANSFoRm project: Experience and lessons learned regarding functional and interoperability requirements to support primary care

Machine Learning Based Sentiment Text Classification for Evaluating Treatment Quality of Discharge Summary

Estimating explainable Alzheimer’s disease likelihood map via clinically-guided prototype learning

Study on Artificial Intelligence in Healthcare

TEST CHARACTERISTICS AND DECISION RULES

Leveraging Multiple Types of Domain Knowledge for Safe and Effective Drug Recommendation

Ethical considerations in implementing AI for mortality prediction in the emergency department: Linking theory and practice

From Text to Tables: A Local Privacy Preserving Large Language Model for Structured Information Retrieval from Medical Documents

Fair and Diverse DPP-based Data Summarization

Fine-Tuning GPT-3 for Russian Text Summarization

A scalable approach for developing clinical risk prediction applications in different hospitals

Comparison of artificial intelligence and human-based prediction and stratification of the risk of long-term kidney allograft failure