Alle Papers – Machine Learning im Gesundheitswesen

Towards Personalized Federated Learning

Artificial intelligence with multi-functional machine learning platform development for better healthcare and precision medicine

Federated learning of predictive models from federated Electronic Health Records

Administrative Behavior

Do no harm: a roadmap for responsible machine learning for health care

Using recurrent neural network models for early detection of heart failure onset

A Survey on the Explainability of Supervised Machine Learning

Screening for dementia by memory testing

Recurrent Neural Networks with Top-k Gains for Session-based Recommendations

Machine Learning Approaches for Clinical Psychology and Psychiatry

Predictive Inference: An Introduction

Opportunities and challenges in developing risk prediction models with electronic health records data: a systematic review

<scp>eD</scp>octor: machine learning and the future of medicine

Clinical information extraction applications: A literature review

Artificial Intelligence for Mental Health and Mental Illnesses: an Overview

The “All of Us” Research Program

The ethics of AI in health care: A mapping review

ROCKET: exceptionally fast and accurate time series classification using random convolutional kernels

Explanation of machine learning models using shapley additive explanation and application for real data in hospital

Evaluating Virtual Screening Methods:  Good and Bad Metrics for the “Early Recognition” Problem

Transfer Learning in Biomedical Natural Language Processing: An Evaluation of BERT and ELMo on Ten Benchmarking Datasets

Designing Theory-Driven User-Centric Explainable AI

Automated machine learning: Review of the state-of-the-art and opportunities for healthcare

Predictive data mining in clinical medicine: Current issues and guidelines

Survival Analysis: A Self-Learning Text