Explainable Artificial Intelligence for Pharmacovigilance: What Features Are Important When Predicting Adverse Outcomes?

25 December 2021

Papers citing "Explainable Artificial Intelligence for Pharmacovigilance: What Features Are Important When Predicting Adverse Outcomes?"

6 / 6 papers shown

Title
LEMDA: A Novel Feature Engineering Method for Intrusion Detection in IoT Systems Ali Ghubaish Zebo Yang A. Erbad Rajkumar Jain 31 3 0 20 Apr 2024
Detecting Anomalies in Blockchain Transactions using Machine Learning Classifiers and Explainability Analysis Mohammad Hasan Mohammad Shahriar Rahman Helge Janicke Iqbal H. Sarker 82 26 0 07 Jan 2024
Explainable AI for clinical risk prediction: a survey of concepts, methods, and modalities Munib Mesinovic Peter Watkinson Ting Zhu FaML 74 3 0 16 Aug 2023
Explainable AI for Malnutrition Risk Prediction from m-Health and Clinical Data Flavio Di Martino Franca Delmastro C. Dolciotti 54 1 0 31 May 2023
Explainable AI for clinical and remote health applications: a survey on tabular and time series data Flavio Di Martino Franca Delmastro AI4TS 66 98 0 14 Sep 2022
"Why Should I Trust You?": Explaining the Predictions of Any Classifier Marco Tulio Ribeiro Sameer Singh Carlos Guestrin FAtt FaML 1.3K 17,225 0 16 Feb 2016