[HTML][HTML] Unbox the black-box for the medical explainable AI via multi-modal and multi-centre data fusion: A mini-review, two showcases and beyond
G Yang, Q Ye, J ** review
Objective To conduct a systematic sco** review of explainable artificial intelligence (XAI)
models that use real-world electronic health record data, categorize these techniques …
models that use real-world electronic health record data, categorize these techniques …
Diagnosis of temporomandibular disorders using artificial intelligence technologies: A systematic review and meta-analysis
Background Artificial intelligence (AI) algorithms have been applied to diagnose
temporomandibular disorders (TMDs). However, studies have used different patient …
temporomandibular disorders (TMDs). However, studies have used different patient …
Artificial intelligence in bariatric surgery: current status and future perspectives
M Bektaş, BMM Reiber, JC Pereira, GL Burchell… - Obesity surgery, 2022 - Springer
Background Machine learning (ML) has been successful in several fields of healthcare,
however the use of ML within bariatric surgery seems to be limited. In this systematic review …
however the use of ML within bariatric surgery seems to be limited. In this systematic review …
Decision tree post-pruning without loss of accuracy using the SAT-PP algorithm with an empirical evaluation on clinical data
A decision tree (DT) is one of the most popular and efficient techniques in data mining.
Specifically, in the clinical domain, DTs have been widely used thanks to their relatively easy …
Specifically, in the clinical domain, DTs have been widely used thanks to their relatively easy …
Machine learning for bioinformatics
Abstract Machine learning (ML) deals with the automated learning of machines without
being programmed explicitly. It focuses on performing data-based predictions and has …
being programmed explicitly. It focuses on performing data-based predictions and has …
[PDF][PDF] An empirical study on hyperparameter tuning of decision trees
Abstract Machine learning algorithms often contain many hyperparameters whose values
affect the predictive performance of the induced models in intricate ways. Due to the high …
affect the predictive performance of the induced models in intricate ways. Due to the high …