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2112.13210
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Explainable Artificial Intelligence for Pharmacovigilance: What Features Are Important When Predicting Adverse Outcomes?
25 December 2021
I. Ward
Ling Wang
Juan Lu
M. Bennamoun
Girish Dwivedi
Frank M. Sanfilippo
Re-assign community
ArXiv (abs)
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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
1