JornLotsch/pain-threshold-analysis: Eur J Pain – accompanying code for article on feature selection in biomedical pain data

Complete code used to reproduce all results and figures (except Figure 2) from the scientific report by Lötsch J, Himmelspach A, and Kringel D: "Resolving interpretation challenges in machine learning feature selection with an iterative approach in biomedical pain data." Eur J Pain, 2026. This release accompanies the cited paper and has been approved by the journal for publication. The repository includes data processing, model training, evaluation scripts, and figure generation (excluding Figure 2).

Impact byBIP!

	selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).	0
	popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.	Average
	influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).	Average
	impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.	Average

Found an issue? Give us feedback

0

Average