New paper from @patientled!

If you run different clustering algorithms against the same set of patient data, you don't get the same sub-groups!

The abstract conclusion:
"The strong dependence of patient clusters on algorithm choice suggests that single-method Long COVID phenotyping may produce incomplete or unstable subgroup definitions. Clustering methods may impose artificial boundaries on a smoothly varying symptom landscape, especially in studies capturing fewer symptoms. Phenotyping efforts should assess clustering robustness and avoid overinterpreting single-method results. Our multi-method analysis highlights the importance of considering the full breadth of patient symptoms when evaluating treatments."

https://academic.oup.com/ooim/article/7/1/iqag010/8707854

#LongCovid #Phenotype #MachineLearning #UnsupervisedClustering

@longcovid