Alle Papers – Machine Learning im Gesundheitswesen

A Deep Learning Architecture for De-identification of Patient Notes: Implementation and Evaluation

Learning temporal rules to forecast instability in continuously monitored patients

Robust diabetic prediction using ensemble machine learning models with synthetic minority over-sampling technique

Exploring the potential of ChatGPT in medical dialogue summarization: a study on consistency with human preferences

Machine learning approaches for predicting and diagnosing chronic kidney disease: current trends, challenges, solutions, and future directions

A Statistical Approach for Analyzing Clonogenic Survival Data

Assessment of depression and anxiety in young and old with a question-based computational language approach

Cue-CoT: Chain-of-thought Prompting for Responding to In-depth Dialogue Questions with LLMs

Prediction and pattern analysis of medication refill adherence through electronic health records and dispensation data

Personalized Federated Learning with Clustering: Non-IID Heart Rate Variability Data Application

Evaluation of Diagnostic Tests

Generating Electronic Health Records with Multiple Data Types and\n Constraints

Early experience with Watson for Oncology: a clinical decision-support system for prostate cancer treatment recommendations

Early Illness Recognition Using In-home Monitoring Sensors and Multiple Instance Learning

Data-driven decision-making for precision diagnosis of digestive diseases

The role of artificial intelligence for the application of integrating electronic health records and patient-generated data in clinical decision support.

Multi-Trajectory Models of Chronic Kidney Disease Progression.

On How Well Generative Adversarial Networks Learn Densities: Nonparametric and Parametric Results.

A comparative analysis of predictive models of morbidity in intensive care unit after cardiac surgery – Part I: model planning

Machine Learning-Powered Smart Healthcare Systems in the Era of Big Data: Applications, Diagnostic Insights, Challenges, and Ethical Implications

TR-GAN: Multi-Session Future MRI Prediction With Temporal Recurrent Generative Adversarial Network

Applications of interpretability in deep learning models for ophthalmology

Developing an AI-assisted clinical decision support system to enhance in-patient holistic health care

Anomaly Analysis of Alzheimer’s Disease in PET Images Using an Unsupervised Adversarial Deep Learning Model

Two models for outcome prediction - a comparison of logistic regression and neural networks.