1 code implementation • 7 Aug 2024 • Michael Staniek, Marius Fracarolli, Michael Hagmann, Stefan Riezler
Machine learning for early syndrome diagnosis aims to solve the intricate task of predicting a ground truth label that most often is the outcome (effect) of a medical consensus definition applied to observed clinical measurements (causes), given clinical measurements observed several hours before.
1 code implementation • 6 Nov 2023 • Michael Hagmann, Shigehiko Schamoni, Stefan Riezler
We demonstrate a validity problem of machine learning in the vital application area of disease diagnosis in medicine.
no code implementations • 8 Feb 2023 • Michael Hagmann, Philipp Meier, Stefan Riezler
Instead of removing noise, we propose to incorporate several sources of variance, including their interaction with data properties, into an analysis of significance and reliability of machine learning evaluation, with the aim to draw inferences beyond particular instances of trained models.
1 code implementation • 1 Sep 2022 • Shigehiko Schamoni, Michael Hagmann, Stefan Riezler
Ensembling neural networks is a long-standing technique for improving the generalization error of neural networks by combining networks with orthogonal properties via a committee decision.
no code implementations • 23 Jun 2021 • Michael Hagmann, Stefan Riezler
This paper is an excerpt of an early version of Chapter 2 of the book "Validity, Reliability, and Significance.