Alle Papers – Machine Learning im Gesundheitswesen

A survey on AI and decision support systems in psychiatry – Uncovering a dilemma

Construction of a Digestive System Tumor Knowledge Graph Based on Chinese Electronic Medical Records: Development and Usability Study

Deep representation learning for individualized treatment effect estimation using electronic health records

The future of artificial intelligence in intensive care: moving from predictive to actionable AI

Automated disease diagnosis and precaution recommender system using supervised machine learning

Longitudinal cohorts for harnessing the electronic health record for disease prediction in a US population

SHAP values for Explaining CNN-based Text Classification Models

Development of a Bayesian Network for the Prognosis of Head Injuries using Graphical Model Selection Techniques

Automated ICD coding using extreme multi-label long text transformer-based models

Capturing Semantic Relationships in Electronic Health Records Using Knowledge Graphs: An Implementation Using MIMIC III Dataset and GraphDB

Modeling the Temporal Complexities of Symptoms

Rare disease knowledge enrichment through a data-driven approach

Bariatric Evaluation Through AI: a Survey of Expert Opinions Versus ChatGPT-4 (BETA-SEOV)

Health Care Prediction for Various Diseases using Computational Intelligence Approaches: A Review

Brain age prediction across the human lifespan using multimodal MRI data

Using the machine learning approach to predict patient survival from high-dimensional survival data

Calibration of medical diagnostic classifier scores to the probability of disease

ChatGPT and large language models in gastroenterology

The future of laboratory medicine in the era of precision medicine

Retrospective analysis and prospective validation of an AI-based software for intracranial haemorrhage detection at a high-volume trauma centre

A Decision Support System for Diabetes Chronic Care Models Based on General Practitioner Engagement and EHR Data Sharing

SOM-VAE: Interpretable Discrete Representation Learning on Time Series

Towards Domain-Specific Explainable AI: Model Interpretation of a Skin Image Classifier using a Human Approach

Assessing the Collective Population Representativeness of Related Type 2 Diabetes Trials by Combining Public Data from ClinicalTrials.gov and NHANES

Recurrent neural networks with missing information imputation for medical examination data prediction