PacmedExplainability for tree-based models: which SHAP approximation is best?Understanding TreeSHAP algorithms’s failure modes11 min read·Jan 12, 2022----
PacmedinGeek CultureOut-Of-Distribution Detection in Medical AIWhy it is a problem and a benchmark to find a solution8 min read·Jun 21, 2021----
PacmedCausal inference for medical AI: what can we learn from observational data of COVID-19 patients?By Giovanni Cinà (Pacmed Labs)5 min read·Mar 2, 2021----
PacmedDoor Wouter Kroese op EmerceMachine learning helpt de zorg levensreddende stappen te maken3 min read·Nov 6, 2019----
PacmedFrom paper to patient, Pacmed’s AI newsletter #2: on interpretabilityBy Giovanni Cinà and Michele Tonutti (Data Scientists at Pacmed)8 min read·May 14, 2019----
PacmedHealthy code, healthy patients: coding best practices in medical Data Science (Part 2)By Michele Tonutti, Data Scientist at Pacmed9 min read·Mar 6, 2019----
PacmedInterning at Pacmed: goodbye blog by Aleide & PimBy Aleide Hoeijmakers and Pim Hoeven4 min read·Mar 5, 2019----
PacmedHealthy 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?7 min read·Feb 19, 2019--1--1
PacmedPersoonlijkere behandeling van prostaatkanker door machine learningdoor Daan de Bruin (Pacmed)1 min read·Dec 20, 2018----
PacmedFrom paper to patient: Pacmed’s AI newsletter #1By Giovanni Cinà and Michele Tonutti (Data Scientists at Pacmed)9 min read·Dec 19, 2018----
