- Development and Validation of a Machine Learning Approach for ...🔍
- Machine Learning for Healthcare🔍
- A machine learning approach to identifying important features for ...🔍
- Systemic lupus in the era of machine learning medicine🔍
- Interpretable machine learning for dementia🔍
- Machine Learning Model to Identify Patients at High Risk for ...🔍
- What is Artificial Intelligence in Medicine?🔍
- [PDF] POS0592|HPR IDENTIFYING IMPORTANT CLINICAL ...🔍
Machine learning model for identifying important clinical features for ...
Development and Validation of a Machine Learning Approach for ...
We compared the predictive power of the clinical and imaging data from multiple machine learning models and further explored the use of four ...
Machine Learning for Healthcare: On the Verge of a Major Shift in ...
Machine learning (ML), the study of tools and methods for identifying patterns in data, can help. The appropriate application of ML to these ...
A machine learning approach to identifying important features for ...
Methods We analyzed data from 268 participants with stroke that included 25 demographic, performance-based and self-report variables. Step 1 of ...
Systemic lupus in the era of machine learning medicine
Additionally, models such as RF and LASSO have capabilities for feature importance, which helps with explainability such as identifying important clinical and ...
Interpretable machine learning for dementia: A systematic review
Model-level (or global) explanations describe the overall model and can be used to identify the most important features across all classes.
Machine Learning Model to Identify Patients at High Risk for ...
This prognostic study evaluates the accuracy of a machine learning model for identifying patients undergoing surgery who were at high risk ...
What is Artificial Intelligence in Medicine? | IBM
Unlike humans, AI never needs to sleep. Machine learning models could be used to observe the vital signs of patients receiving critical care and alert ...
[PDF] POS0592-HPR IDENTIFYING IMPORTANT CLINICAL ...
A proposed machine learning model system successfully identified clinical features that were predictive of remission in each of the bDMARDs and may be ...
A comparison of deep learning performance against health-care ...
Deep learning offers considerable promise for medical diagnostics. We aimed to evaluate the diagnostic accuracy of deep learning algorithms ...
Machine learning in healthcare: Uses, benefits and pioneers in the ...
For example, in radiology, machine learning models are trained to examine medical images such as X-rays, MRIs, and CT scans, detecting anomalies ...
Machine Learning Identification of Obstructive Sleep Apnea Severity ...
Our study demonstrated how artificial intelligence can be useful in assessing patients with OSA-related symptoms and determining the risk of ...
Machine learning models for predicting critical illness risk in ...
Conclusions: A XGBoost-based based clinical model on admission might be used as an effective tool to identify patients at high risk of critical illness.
A survey of artificial intelligence in rheumatoid arthritis - De Gruyter
Machine learning model for iden- tifying important clinical features for predicting remission in pa-.
Delving into Machine Learning's Influence on Disease Diagnosis ...
The findings reinforce the pivotal role of machine learning in transforming medical diagnostics. The variability in algorithm performance ...
Definitions, methods, and applications in interpretable machine ...
Machine-learning models have demonstrated great success in learning complex patterns that enable them to make predictions about unobserved data. In addition to ...
Predictive Modeling for Clinical Features Associated With ...
Naive machine learning techniques can be potentially used to develop and validate predictive phenotype complexes applicable to risk stratification and disease ...
A Machine-Learning Framework to Identify Distinct Phenotypes of ...
We hypothesized that our machine-learning approach would improve the classification of disease severity in AS and the prediction of adverse ...
Machine Learning Techniques to Predict Mental Health Diagnoses
specifically employed Decision Tree, Neural Network, Support Vector Machine, Naive Bayes, and logistic regression algorithms to categorize ...
An efficient machine learning framework to identify important clinical ...
To address this difficulty, we presented an effective framework using the deep neural network (DNN) model and the permutation-based feature ...
the use of AI/ML in predictive modeling and counterfactual simulation to inform clinical ... The use of the model may be important to consider in evaluating AI/ML ...