Frontiers

Comparative Machine Learning to Predict Acute Kidney Injury in Traumatic Brain Injury: A MIMIC-IV Cohort with SHAP Interpretation

The final, formatted version of the article will be published soon. Background:AKI is a frequent and severe complication among TBI patients. Accurate early prediction is critical but remains ...
IEEE

Machine Learning for Transparent Cervical Cancer Diagnosis: A SHAP-Guided Framework

Abstract: Early detection and greater mortality rates are caused by a lack of access to screening and diagnostic services, which is particularly problematic in low-resource locations where cervical ...
Frontiers

An Interpretable Machine Learning Model with SHAP Explanations Predicts Spontaneous Bleeding in Pediatric Acute Liver Failure

The final, formatted version of the article will be published soon. Background: Pediatric acute liver failure (PALF) is a severe clinical syndrome associated with a high risk of spontaneous bleeding, ...
IEEE

Interpretable QSAR Modelling for Biodegradability Classification Using Machine Learning and SHAP Explainability

Abstract: Predicting biodegradability is crucial in green chemistry and environmental risk research for regulatory purposes and sustainable design of chemicals. In this research, machine learning (ML) ...
marktechpost

A Coding Implementation to Train Safety-Critical Reinforcement Learning Agents Offline Using Conservative Q-Learning with d3rlpy and Fixed Historical Data

In this tutorial, we build a safety-critical reinforcement learning pipeline that learns entirely from fixed, offline data rather than live exploration. We design a custom environment, generate a ...