Alle Papers – Machine Learning im Gesundheitswesen

COVID-19-The Role of Artificial Intelligence, Machine Learning, and Deep Learning: A Newfangled

An interpretable machine learning model for predicting 28-day mortality in patients with sepsis-associated liver injury

Structuring clinical text with AI: Old versus new natural language processing techniques evaluated on eight common cardiovascular diseases

Prediction Model for Coronavirus Pandemic Using Deep Learning

A Comparative Study of Machine Learning Algorithms as Expert Systems in Medical Diagnosis (Asthma)

Artificial intelligence in medicine

Neuro-Symbolic Interpretable Collaborative Filtering for Attribute-based Recommendation

Machine learning for prediction of schizophrenia using genetic and demographic factors in the UK biobank

Prediction of 30-Day Readmission After Stroke Using Machine Learning and Natural Language Processing

Electronic Health Records for Drug Repurposing: Current Status, Challenges, and Future Directions

Prediction Of Diabetes Using Machine Learning Classification Algorithms

Enhancing Heart Attack Prediction with Machine Learning: A Study at Jordan University Hospital

OpenSDE: A strategy for expressive and flexible structured data entry

Connections between Various Disorders: Combination Pattern Mining Using Apriori Algorithm Based on Diagnosis Information from Electronic Medical Records

Prognostic physiology: modeling patient severity in Intensive Care Units using radial domain folding.

Using interpretability approaches to update “black-box” clinical prediction models: an external validation study in nephrology

Sinkhorn AutoEncoders

Performance Based Evaluation of Various Machine Learning Classification Techniques for Chronic Kidney Disease Diagnosis

Explaining Deep Classification of Time-Series Data with Learned\n Prototypes

Predicting Australian Adults at High Risk of Cardiovascular Disease Mortality Using Standard Risk Factors and Machine Learning

Measurement and application of patient similarity in personalized predictive modeling based on electronic medical records

DeepFaceLIFT: Interpretable Personalized Models for Automatic Estimation of Self-Reported Pain

Using Electronic Health Records to Facilitate Precision Psychiatry

Main factors influencing recovery in MERS Co-V patients using machine learning

A novel interpretable machine learning system to generate clinical risk scores: An application for predicting early mortality or unplanned readmission in a retrospective cohort study