Alle Papers – Machine Learning im Gesundheitswesen

Analysis and research on mental health of college students based on cognitive computing

Learning the Dynamic Treatment Regimes from Medical Registry Data through Deep Q-network

Pediatrics in Artificial Intelligence Era: A Systematic Review on Challenges, Opportunities, and Explainability

Evaluation of Machine Learning Algorithms for Predicting Readmission After Acute Myocardial Infarction Using Routinely Collected Clinical Data

Electronic health records and quality of care for heart failure

A deep inference learning framework for healthcare

Cautions on the use of medical databases

Bayesian network analysis of Covid-19 data reveals higher infection prevalence rates and lower fatality rates than widely reported

The Price of Artificial Intelligence

Prediction of specific pathogens in patients with sepsis: evaluation of TREAT, a computerized decision support system

Distilled Wasserstein Learning for Word Embedding and Topic Modeling

Iteratively Pruned Deep Learning Ensembles for COVID-19 Detection in Chest X-Rays

Review of Machine Learning and Deep Learning Based Recommender Systems for Health Informatics

RETRACTED: An effective convolutional neural network-based stacked long short-term memory approach for automated Alzheimer’s disease prediction

Analyzing and predicting the risk of death in stroke patients using machine learning

Temporal-based Swin Transformer network for workflow recognition of surgical video

A stacked ensemble machine learning approach for the prediction of diabetes

Advances in artificial intelligence for diabetes prediction: insights from a systematic literature review

Stratified Sampling Design Based on Data Mining

Extracting Physician Group Intelligence from Electronic Health Records to Support Evidence Based Medicine

A novel neural-inspired learning algorithm with application to clinical risk prediction

Deep Learning-Based Knowledge Graph Generation for COVID-19

Towards Source-Aligned Variational Models for Cross-Domain Recommendation

Explainable SHAP-XGBoost models for in-hospital mortality after myocardial infarction

Leveraging text skeleton for de-identification of electronic medical records