Article

Mixture Density Networks for the Indirect Estimation of Reference Intervals

2022-02-01

Abstract excerpt

<h4>Background: </h4> 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...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work: 0a05bc0d-c903-5dd2-bdca-67761dbfd49e
DOI: 10.21203/rs.3.rs-1259944/v1

Open publication

Related research

Semantic proximity does not establish scientific evidence. Graph snapshot: 2026-09-29.

Click a neighbor to travelStep 1 · 12 closest

Mixture Density Networks for the Indirect Estimation of Reference IntervalsDOI 10.21203/rs.3.rs-1259944/v1Read

Comparison of Bayesian approaches for developing prediction models in rare disease: application to the identification of patients with Maturity-Onset Diabetes of the YoungSemantic proximity Read

Testing calibration of phenotyping models using positive-only electronic health record data.Semantic proximity Read

Deep latent variable modelling reveals clinically significant subgroups among transfusion recipientsSemantic proximity Read

Age- and sex-specific pediatric LMS-Percentiles for Laboratory Parameters enabled by Data miningSemantic proximity Read

Reference values of respiratory impedance with impulse oscillometry in healthy Chinese adultsSemantic proximity Read

Pediatric Glucose-6-Phosphate Dehydrogenase Reference Intervals Derived from Archived Phenotype and Genotype Test Results.Semantic proximity Read

Confidence-based laboratory test reduction recommendation algorithmSemantic proximity Read

Conditional deep generative modeling of blood-based infrared spectra enables controlled in-silico phenotyping studiesSemantic proximity Read

Characterization and non-parametric modeling of the developing serum proteome during infancy and early childhood.Semantic proximity Read

Generalized Bayesian Method for Diagnostic Classification ModelsSemantic proximity Read

Generalized Bayesian Method for Diagnostic Classification ModelsSemantic proximity Read

Generalized Bayesian Method for Diagnostic Classification ModelsSemantic proximity Read