Alle Papers – Machine Learning im Gesundheitswesen

A Machine Learning Model for Early Prediction and Detection of Sepsis in Intensive Care Unit Patients

Deep learning-based age estimation from chest X-rays indicates cardiovascular prognosis

Application of Machine Learning Methods on Patient Reported Outcome Measurements for Predicting Outcomes: A Literature Review

Attend and Diagnose: Clinical Time Series Analysis using Attention Models

Automatic generation of case‐detection algorithms to identify children with asthma from large electronic health record databases

Intelligent diagnosis with Chinese electronic medical records based on convolutional neural networks

Large Language Models and Artificial Intelligence: A Primer for Plastic Surgeons on the Demonstrated and Potential Applications, Promises, and Limitations of ChatGPT

An Enhanced Machine Learning Framework for Type 2 Diabetes Classification Using Imbalanced Data with Missing Values

Machine learning based prediction of depression among type 2 diabetic patients

From explainable to interpretable deep learning for natural language processing in healthcare: How far from reality?

FedClassAvg: Local Representation Learning for Personalized Federated Learning on Heterogeneous Neural Networks

Chat GPT-4 significantly surpasses GPT-3.5 in drug information queries

Generative Adversarial Networks Assist Missing Data Imputation: A Comprehensive Survey and Evaluation

Classification Comparison of Machine Learning Algorithms Using Two Independent CAD Datasets

Using machine learning for predicting cervical cancer from Swedish electronic health records by mining hierarchical representations

Mitigating Cognitive Biases in Clinical Decision-Making Through Multi-Agent Conversations Using Large Language Models: Simulation Study

Validation of an indirect linkage algorithm to combine registry data with Medicare claims

A richly interactive exploratory data analysis and visualization tool using electronic medical records

Predicting the microbial cause of community-acquired pneumonia: can physicians or a data-driven method differentiate viral from bacterial pneumonia at patient presentation?

Explainable AI for Interpretable Credit Scoring

Road map for clinicians to develop and evaluate AI predictive models to inform clinical decision-making

Disease Prediction using Machine Learning

Stacked Deep Dense Neural Network Model to Predict Alzheimer’s Dementia Using Audio Transcript Data

Development and validation of a practical machine learning model to predict sepsis after liver transplantation

Modeling 4D fMRI Data via Spatio-Temporal Convolutional Neural Networks (ST-CNN)