Explainability for tree-based models: which SHAP approximation is best?Understanding TreeSHAP algorithms’s failure modesJan 12, 2022Jan 12, 2022
Published inGeek CultureOut-Of-Distribution Detection in Medical AIWhy it is a problem and a benchmark to find a solutionJun 21, 2021Jun 21, 2021
Causal inference for medical AI: what can we learn from observational data of COVID-19 patients?By Giovanni Cinà (Pacmed Labs)Mar 2, 2021Mar 2, 2021
Door Wouter Kroese op EmerceMachine learning helpt de zorg levensreddende stappen te makenNov 6, 2019Nov 6, 2019
From paper to patient, Pacmed’s AI newsletter #2: on interpretabilityBy Giovanni Cinà and Michele Tonutti (Data Scientists at Pacmed)May 14, 2019May 14, 2019
Healthy code, healthy patients: coding best practices in medical Data Science (Part 2)By Michele Tonutti, Data Scientist at PacmedMar 6, 2019Mar 6, 2019
Interning at Pacmed: goodbye blog by Aleide & PimBy Aleide Hoeijmakers and Pim HoevenMar 5, 2019Mar 5, 2019
Healthy code, healthy patients: coding best practices in medical Data Science (Part 1)How would you feel knowing that the quality of every single line of your code will directly impact the lives of thousands of people?Feb 19, 20191Feb 19, 20191
Persoonlijkere behandeling van prostaatkanker door machine learningdoor Daan de Bruin (Pacmed)Dec 20, 2018Dec 20, 2018
From paper to patient: Pacmed’s AI newsletter #1By Giovanni Cinà and Michele Tonutti (Data Scientists at Pacmed)Dec 19, 2018Dec 19, 2018
