- Fulfilling the potential of AI🔍
- Explainable Machine Learning Model for Alzheimer Detection Using ...🔍
- Personalized Explanations for Early Diagnosis of Alzheimer's ...🔍
- ExplaiNAble BioLogical Age 🔍
- Explainable Machine Learning for Refeeding Hypophosphatemia🔍
- Ordo Fraterna Fibonacci on X🔍
- Explainable Deep Learning Framework🔍
- Explainable deep learning for disease activity prediction in chronic ...🔍
Explainable Deep Learning for Personalized Age Prediction With ...
Fulfilling the potential of AI: towards explainable deep learning
In the case of deep learning, these algorithms are inspired by the structure of the human brain with millions of neurons that are connected in many layers. This ...
Explainable Machine Learning Model for Alzheimer Detection Using ...
The disease usually affects individuals over the age of 65, with symptoms appearing in their mid-60s [3]. However, a rare form of the disease, ...
Personalized Explanations for Early Diagnosis of Alzheimer's ... - OUCI
Ocasio, Deep learning prediction of mild cognitive impairment conversion to ... Explainable deep learning models in medical image analysis. J. Imaging ...
ExplaiNAble BioLogical Age (ENABL Age): an artificial intelligence ...
Contrasting with other techniques, ENABL Age uses an approach that combines complex machine-learning models with explainable artificial ...
Explainable Machine Learning for Refeeding Hypophosphatemia
In the context of predicting refeeding hypophosphatemia, explainable AI (XAI) techniques play a crucial role in enhancing the interpretability ...
Ordo Fraterna Fibonacci on X: "Explainable Deep Learning for ...
Explainable Deep Learning for Personalized Age Prediction With Brain Morphology https://t.co/CxikRmiNWv.
Explainable Deep Learning Framework: Decoding Brain Task and ...
We observed a change in the model's performance from the 7-yrs age group with an accuracy of 68% with an F1-score of 0.69 for 12 ROIs and ...
Explainable deep learning for disease activity prediction in chronic ...
To this end, we explored multiple feature attribution methods including SHAP, attention attribution and feature weighting using case-based similarity. Our model ...
Explainable machine learning in outcome prediction of high-grade ...
Objective: Accurate prognostic prediction in patients with high-grade aneruysmal subarachnoid hemorrhage (aSAH) is essential for personalized treatment.
Improving trust and confidence in medical skin lesion diagnosis ...
A key issue in critical contexts such as medical diagnosis is the interpretability of the deep learning models adopted in decision-making ...
Explainable machine learning for predicting conversion to ...
This study assesses the application of interpretable machine learning modeling using electronic medical record data for the prediction of conversion to ...
The Promise of Explainable Deep Learning for Omics Data Analysis
[73] optimised a deep learning model to predict cardiovascular risk factors, including age, gender and smoking status, not previously quantified ...
RiskPath: Explainable deep learning for multistep biomedical ...
Like most deep learning algorithms, LSTMs do not natively return the identity of predictors. To provide multistep prediction algorithms suitable ...
Explainable Artificial Intelligence–A New Step towards the Trust in ...
the explainability of AI, machine learning, and deep learning techniques. ... Explainable deep learning for personalized age prediction with brain morphology.
Abstract 10437: “Explainable Artificial Intelligence (ai) in Cardiology ...
... machine learning models ... Abstract 10437: “Explainable Artificial Intelligence (ai) in Cardiology”: A Tool to Provide Personalized Predictions on Cardiac Health ...
Personalized prediction of mortality in patients with acute ischemic ...
We aim to employ interpretable machine learning (ML) models to study AIS and clarify its decision-making process in identifying the risk of mortality.
An explainable machine learning model for predicting the outcome ...
We also employed the SHAP method to interpret how the model makes personalized predictions for each specific instance. Online supplemental figure 5A displays a ...
Explainable machine learning for predicting 30-day readmission in ...
Five machine learning algorithms with good performance were applied to develop models, and the discrimination ability was comprehensively evaluated by ...
Explainable Deep-Learning Prediction for Brain–Computer ...
By providing personalized recovery factors specific to each patient, along with elucidating the influence of various factors on motor recovery, we aim to ...
Why Are We Using Black Box Models in AI When We Don't Need To ...
... Explainable Machine Learning Challenge. The goal of the competition ... For instance, an interpretable machine learning model for predicting ...