Harry Perkins Institute of Medical Research

output

20202024

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

60 citations

researchers with a paper here

Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.

G. Dwivedi2 · h 34
Yanbin Liu2 profiles2 · h 11
Agnimitra Dasgupta1 · h 6
Arjun Balaji1 · h 5
Assad A. Oberai1 · h 6
B. Doyle1 · h 36
Brendan Kennedy1 · h 2
C. Schultz1 · h 43
Farid Boussaïd1
F. Boussaid1 · h 32
Frank Sanfilippo1
F. Sanfilippo1 · h 37

collaborating institutions

The University of Western AustraliaAU5 papers
Fiona Stanley HospitalAU3 papers
Australian National UniversityAU1 paper
British Heart Foundation Centre for Cardiovascular ScienceGB1 paper
Institute of PhysicsPL1 paper
Massachusetts Institute of TechnologyUS1 paper
Nicolaus Copernicus UniversityPL1 paper
Okinawa Institute of Science and Technology Graduate UniversityJP1 paper
Ottawa Heart InstituteCA1 paper
Queen Elizabeth II Medical CentreAU1 paper
Royal Perth HospitalAU1 paper
Stanley FoundationUS1 paper

Showing 2021 · q-bio.QMShow all

1 paper · 2 filters

q-bio.QM2021★ 60 cited

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

Isaac Ronald Ward, Ling Wang, Juan lu +3

Explainable Artificial Intelligence (XAI) has been identified as a viable method for determining the importance of features when making predictions using Machine Learning (ML) mode…