Alle Papers – Machine Learning im Gesundheitswesen

ChatGPT and artificial hallucinations in stem cell research: assessing the accuracy of generated references – a preliminary study

Forecasting and explaining emergency department visits in a public hospital

SchizConnect: Virtual Data Integration in Neuroimaging

Panacea, a semantic-enabled drug recommendations discovery framework

Automatically determining cause of death from verbal autopsy narratives

Generalization of Machine Learning Approaches to Identify Notifiable Conditions from a Statewide Health Information Exchange.

Computerized clinical decision support improves mortality in intra abdominal surgical sepsis

G³SR: Global Graph Guided Session-Based Recommendation

Large Language Models in Medicine: Clinical Applications, Technical Challenges, and Ethical Considerations

Vascular memory: can we broaden the concept of the metabolic memory?

Machine learning to analyse omic-data for COVID-19 diagnosis and prognosis

Application of Data Mining Techniques to Predict the Length of Stay of Hospitalized Patients with Diabetes

A Unified Modeling Approach to Data-Intensive Healthcare

ChatGPT in Answering Queries Related to Lifestyle-Related Diseases and Disorders

Description of a Rule-based System for the i2b2 Challenge in Natural Language Processing for Clinical Data

Enabling a Pervasive Approach for Intelligent Decision Support in Critical Health Care

Unsupervised Machine Learning of Topics Documented by Nurses about Hospitalized Patients Prior to a Rapid-Response Event

Electronic health records, clinical decision support, and blood pressure control.

Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining

Probing met represser–operator recognition in solution

Improving understandability of feature contributions in model-agnostic explainable AI tools

Appropriateness of ChatGPT in Answering Heart Failure Related Questions

PhysioNet 2012 Challenge: Predicting mortality of ICU patients using a cascaded SVM-GLM paradigm

Early prediction of high-cost inpatients with ischemic heart disease using network analytics and machine learning