A framework using autoencoders quantifies patient-level similarity to development data and measures predictive model performance across similarity subgroups to distinguish case-mix effects from model deficiencies in external validation.
citation dossier
Advances in neural information processing systems 2018, 31
1Pith papers citing it
1reference links
stat.MEtop field · 1 papers
UNVERDICTEDtop verdict bucket · 1 papers
why this work matters in Pith
Pith has found this work in 1 reviewed paper. Its strongest current cluster is stat.ME (1 papers). The largest review-status bucket among citing papers is UNVERDICTED (1 papers). For highly cited works, this page shows a dossier first and a bounded explorer second; it never tries to render every citing paper at once.
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stat.ME 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
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Rethinking external validation for the target population: Capturing patient-level similarity with a generative model
A framework using autoencoders quantifies patient-level similarity to development data and measures predictive model performance across similarity subgroups to distinguish case-mix effects from model deficiencies in external validation.