Alle Papers – Machine Learning im Gesundheitswesen

Explainable machine learning predictions to help anesthesiologists prevent hypoxemia during surgery

Leveraging the Electronic Health Record to Address the COVID-19 Pandemic

Machine learning‐based patient classification system for adult patients in intensive care units: A cross‐sectional study

Frequency Analysis of Medical Concepts in Clinical Trials and their Coverage in MeSH and SNOMED-CT

Medical Data Mining for Early Deterioration Warning in General Hospital Wards

Concept Embedding Models: Beyond the Accuracy-Explainability Trade-Off

Neural Networks for Clustered and Longitudinal Data Using Mixed Effects Models

Polypharmacy side-effect prediction with enhanced interpretability based on graph feature attention network

Causal Hidden Markov Model for Time Series Disease Forecasting

End-to-end learning for semiquantitative rating of COVID-19 severity on Chest X-rays

Deep Feature Learning for Disease Risk Assessment Based on Convolutional Neural Network With Intra-Layer Recurrent Connection by Using Hospital Big Data

Stabilized sparse ordinal regression for medical risk stratification

Machine Learning Algorithms to Predict Breast Cancer Recurrence Using Structured and Unstructured Sources from Electronic Health Records

Development and Validation of a Deep Learning Model for Earlier Detection of Cognitive Decline From Clinical Notes in Electronic Health Records

Cardiology record multi-label classification using latent Dirichlet allocation

IEmS: A collaborative environment for patient empowerment

Checkpoint Ensembles: Ensemble Methods from a Single Training Process

Designing accessible, explainable AI (XAI) experiences

Data consistency in the English Hospital Episodes Statistics database

Early detection, prevention, and mitigation of critical illness outside intensive care settings

Deep Learning for Epidemiologists: An Introduction to Neural Networks

Dynamic Illness Severity Prediction via Multi-task RNNs for Intensive Care Unit

A Multimodal Transformer: Fusing Clinical Notes with Structured EHR Data for Interpretable In-Hospital Mortality Prediction

Predicting adverse drug reactions of combined medication from heterogeneous pharmacologic databases

Machine Learning based Early Prediction of Type 2 Diabetes: A New Hybrid Feature Selection Approach using Correlation Matrix with Heatmap and SFS