Alle Papers – Machine Learning im Gesundheitswesen
104.164 Papers insgesamt · Seite 264 von 400
From big data to better patient outcomes
The computational prediction of drug-disease interactions using the dual-network L2,1-CMF method
A Gaussian mixture model based discretization algorithm for associative classification of medical data
Transforming clinical data into wisdom
MixEHR-Guided: A guided multi-modal topic modeling approach for large-scale automatic phenotyping using the electronic health record
Interpretable Classification of Pneumonia Infection Using eXplainable AI (XAI-ICP)
Current Trends in Readmission Prediction: An Overview of Approaches
Comparing machine learning and regression models for mortality prediction based on the Hungarian Myocardial Infarction Registry
DeepNote-GNN
The utility of ChatGPT for cancer treatment information
The Electronic Health Record for Translational Research
Big Data for cardiology: novel discovery?
Unintended consequences of machine learning in medicine?
Novel Machine Learning Algorithms for Predicting COVID-19 Clinical Outcomes with Gender Analysis
12 Plagues of AI in Healthcare: A Practical Guide to Current Issues With Using Machine Learning in a Medical Context
Improving the Applicability of AI for Psychiatric Applications through Human-in-the-loop Methodologies
Using Gaussian process based kernel classifiers for credit rating forecasting
Prediction of the risk of developing end-stage renal diseases in newly diagnosed type 2 diabetes mellitus using artificial intelligence algorithms
The 30-days hospital readmission risk in diabetic patients: predictive modeling with machine learning classifiers
Dynamic Bayesian network modeling for longitudinal brain morphometry
Using StyleGAN for Visual Interpretability of Deep Learning Models on Medical Images
Prompt engineering on leveraging large language models in generating response to InBasket messages
Improving the Efficiency and Effectiveness for BERT-based Entity Resolution
The next paradigm shift? ChatGPT, artificial intelligence, and medical education
Big data and clinical research: perspective from a clinician.