Alle Papers – Machine Learning im Gesundheitswesen
104.164 Papers insgesamt · Seite 358 von 400
Sample Size Considerations for Fine-Tuning Large Language Models for Named Entity Recognition Tasks: Methodological Study
DeepSeek vs ChatGPT: a comparison study of their performance in answering prostate cancer radiotherapy questions in multiple languages
Research and Application of Artificial Intelligence Based on Electronic Health Records of Patients With Cancer: Systematic Review
The Importance of Real-World Data to Precision Medicine
Practical challenges for precision medicine
The bigger, the better? When multicenter clinical trials and meta-analyses do not work
An Explainable AI Framework for Artificial Intelligence of Medical Things
Introduction to Supervised Machine Learning
Identification of postoperative complications using electronic health record data and machine learning
An augmented estimation procedure for EHR-based association studies accounting for differential misclassification
Automatic detection of omissions in medication lists
Autonomous medical evaluation for guideline adherence of large language models
Automatic correction of performance drift under acquisition shift in medical image classification
Hierarchical Deep Generative Models for Multi-Rate Multivariate Time Series
Unraveling the Black Box: A Review of Explainable Deep Learning Healthcare Techniques
Generative large language models are all-purpose text analytics engines: text-to-text learning is all your need
Harnessing electronic medical records to advance research on multiple sclerosis
F<scp>ORMULA</scp>: <u>F</u>act<u>OR</u>ized <u>MU</u>lti-task <u>L</u>e<u>A</u>rning for task discovery in personalized medical models
Prediction of blood transfusion donation
Microvascular Complications in Type-2 Diabetes: A Review of Statistical Techniques and Machine Learning Models
Improving Clinical Predictions through Unsupervised Time Series Representation Learning
Critical Care Database Comprising Patients With Infection
Explainable Knowledge Distillation for On-Device Chest X-Ray Classification
What Clinicians Want: Contextualizing Explainable Machine Learning for Clinical End Use.
Exclusion cycles: Reinforcing disparities in medicine