Alle Papers – Machine Learning im Gesundheitswesen

A Novel Deep Neural Network for Intracranial Haemorrhage Detection and Classification

Healthcare Intelligence: Turning Data into Knowledge

Early Prediction of Mortality for Septic Patients Visiting Emergency Room Based on Explainable Machine Learning: A Real-World Multicenter Study

Patient-tailored prioritization for a pediatric care decision support system through machine learning

Modeling electronic health records in ensembles of semantic spaces for adverse drug event detection

Artificial intelligence for neurodegenerative experimental models

Grad-CAM: Understanding AI Models

Identifying Risk Factors For Heart Disease in Electronic Medical Records: A Deep Learning Approach

Transforming Anesthesia Data Into the Observational Medical Outcomes Partnership Common Data Model: Development and Usability Study

Development and Evaluation of a Natural Language Processing Annotation Tool to Facilitate Phenotyping of Cognitive Status in Electronic Health Records: Diagnostic Study

Creating hospital-specific customized clinical pathways by applying semantic reasoning to clinical data

The Future of Clinical Trials

Advances in Machine Learning and Explainable Artificial Intelligence for Depression Prediction

End-to-End Dementia Status Prediction from Brain MRI Using Multi-task Weakly-Supervised Attention Network

Development and Validation of a Class Imbalance-Resilient Cardiac Arrest Prediction Framework Incorporating Multiscale Aggregation, ICA and Explainability

Doctor Recommendation Model Based on Ontology Characteristics and Disease Text Mining Perspective

Generation of Heterogeneous Synthetic Electronic Health Records using GANs

Redesigning Primary Care: The Emergence of Artificial-Intelligence-Driven Symptom Diagnostic Tools

Time-aware Context-Gated Graph Attention Network for Clinical Risk Prediction

Data-Driven Visual Characterization of Patient Health-Status Using Electronic Health Records and Self-Organizing Maps

Modeling electronic health record data using an end-to-end knowledge-graph-informed topic model

Machine learning algorithms in sepsis

Knowledge graph–based thought: a knowledge graph–enhanced LLM framework for pan-cancer question answering

Early predicting 30-day mortality in sepsis in MIMIC-III by an artificial neural networks model

Artificial intelligence in the GPs office: a retrospective study on diagnostic accuracy