Mixture density networks for the indirect estimation of reference intervals.

ABSTRACT:

Background

Reference intervals represent the expected range of physiological test results in a healthy population and are essential to support medical decision making. Particularly in the context of pediatric reference intervals, where recruitment regulations make prospective studies challenging to conduct, indirect estimation strategies are becoming increasingly important. Established indirect methods enable robust identification of the distribution of "healthy" samples from laboratory databases, which include unlabeled pathologic cases, but are currently severely limited when adjusting for essential patient characteristics such as age. Here, we propose the use of mixture density networks (MDN) to overcome this problem and model all parameters of the mixture distribution in a sing

SUBMITTER: Hepp T 

PROVIDER: S-EPMC9336034 | biostudies-literature | 2022 Jul

REPOSITORIES: biostudies-literature