Abstract Objective Many Computer Aided Prognostic (CAP) systems based on machine
learning techniques have been proposed in the field of oncology. The objective of this …

Utilization of model-agnostic explainable artificial intelligence frameworks in oncology: a
narrative review - PMC Back to Top Skip to main content NIH NLM Logo Access keys NCBI …

This paper develops a rigorous argument for why the use of Shapley values in explainable
AI (XAI) will necessarily yield provably misleading information about the relative importance …

Abstract Explainable Artificial Intelligence (XAI) is widely considered to be critical for building
trust into the deployment of systems that integrate the use of machine learning (ML) models …

Abstract Machine Learning (ML) represents a computer science capable of generating
predictive models, by exposure to raw, training data, without being rigidly programmed. Over …

Background and purpose: Machine learning (ML) is applied for outcome prediction and
treatment support. This study aims to develop different ML models to predict risk of axillary …

Purpose Dosimetric predictors of toxicity in patients treated with definitive chemoradiation for
locally advanced non-small cell lung cancer are often identified through trial and error. This …

Early detection of gynecological malignancies is vital for patient management and
prolonging the patient's survival. Molecular imaging, such as positron emission tomography …

Recent work demonstrated the existence of Boolean functions for which Shapley values
provide misleading information about the relative importance of features in rule-based …

Background Develop the best machine learning (ML) model to predict nonsentinel lymph
node metastases (NSLNM) in breast cancer patients. Methods From June 2016 to August …