Alle Papers – Machine Learning im Gesundheitswesen

Evaluating and Enhancing Large Language Models’ Performance in Domain-Specific Medicine: Development and Usability Study With DocOA

Examining the Role of Large Language Models in Orthopedics: Systematic Review

MetaSelector: Meta-Learning for Recommendation with User-Level Adaptive Model Selection

RETRACTED ARTICLE: Designing statistical assessment healthcare information system for diabetics analysis using big data

Forecasting COVID-19 cases using time series modeling and association rule mining

Prediction Rules for Patients With Suspected Myocardial Infarction

Artificial intelligence in medical diagnosis: the INTERNIST/CADUCEUS approach.

Modeling multivariate clinical event time-series with recurrent temporal mechanisms

Application of neuroanatomical ontologies for neuroimaging data annotation

Extracting Biomedical Events with Parallel Multi-Pooling Convolutional Neural Networks

Diabetes prediction model based on GA-XGBoost and stacking ensemble algorithm

Early Sepsis Prediction Using Ensemble Learning With Deep Features and Artificial Features Extracted From Clinical Electronic Health Records

Logistic regression and machine learning predicted patient mortality from large sets of diagnosis codes comparably

Avoiding Latent Variable Collapse With Generative Skip Models

An Accurate Deep Learning Model for Clinical Entity Recognition From Clinical Notes

The Identification of Regions of Significance in the Effect of Multimorbidity on Depressive Symptoms Using Longitudinal Data: An Application of the Johnson-Neyman Technique

Adversarial Learning to Compare

Leveraging generative AI for clinical evidence synthesis needs to ensure trustworthiness

Measure clinical drug–drug similarity using Electronic Medical Records

Statistical Reasoning in Medicine

Performance Evaluation of Levenberg-Marquardt Technique in Error Reduction for Diabetes Condition Classification

survivalROC: Time-Dependent ROC Curve Estimation from Censored Survival Data

A Machine Learning–Based Algorithm for the Prediction of Intensive Care Unit Delirium (PRIDE): Retrospective Study

Evaluating Large Language Models for Drafting Emergency Department Discharge Summaries

Integration of artificial intelligence in clinical laboratory medicine: Advancements and challenges