Alle Papers – Machine Learning im Gesundheitswesen

Lifting Hospital Electronic Health Record Data Treasures: Challenges and Opportunities

De-identification of health records using Anonym: Effectiveness and robustness across datasets

Machine learning applied to diabetes dataset using Quantum versus Classical computation

A comparative data analytic approach to construct a risk trade-off for cardiac patients’ re-admissions

Reversing the Abnormal: Pseudo-Healthy Generative Networks for Anomaly Detection

Extracting symptoms from free-text responses using ChatGPT among COVID-19 cases in Hong Kong

Multicentre clinical trials’ data management: a hybrid solution to exploit the strengths of electronic data capture and electronic health records systems

Machine Learning for Pharmacokinetic/Pharmacodynamic Modeling

AI chatbots not yet ready for clinical use

Using Deep Neural Networks for Predicting Age and Sex in Healthy Adult Chest Radiographs

Reconsidering hospital EHR adoption at the dawn of HITECH: implications of the reported 9% adoption of a “basic” EHR

Multi-Channel brain atrophy pattern analysis in neuroimaging retrieval

Mind + Machine: ChatGPT as a Basic Clinical Decisions Support Tool

Generative AI and Large Language Models - Benefits, Drawbacks, Future and Recommendations

Imbalanced target prediction with pattern discovery on clinical data repositories

Development of NLP-Integrated Intelligent Web System for E-Mental Health

Conv-RGNN: An efficient Convolutional Residual Graph Neural Network for ECG classification

Using machine learning for predicting intensive care unit resource use during the COVID-19 pandemic in Denmark

Fully bayesian longitudinal unsupervised learning for the assessment and visualization of AD heterogeneity and progression

Utilizing time series data embedded in electronic health records to develop continuous mortality risk prediction models using hidden Markov models: A sepsis case study

The value of federated learning during and post-COVID-19

Design and Development of Modified Ensemble Learning with Weighted RBM Features for Enhanced Multi-disease Prediction Model

Instance importance-Aware graph convolutional network for 3D medical diagnosis

Responsible AI for cardiovascular disease detection: Towards a privacy-preserving and interpretable model

Predicting conversion to Alzheimer’s disease in individuals with Mild Cognitive Impairment using clinically transferable features