About: A Bayesian Hierarchical Network for Combining Heterogeneous Data Sources in Medical Diagnoses

Facets (new session)
Description
Metadata
Settings
- owl:sameAs
- Inference Rule:

About: A Bayesian Hierarchical Network for Combining Heterogeneous Data Sources in Medical Diagnoses Goto Sponge NotDistinct Permalink

An Entity of Type : schema:ScholarlyArticle, within Data Space : covidontheweb.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
title	A Bayesian Hierarchical Network for Combining Heterogeneous Data Sources in Medical Diagnoses
Creator	Bunbury, Pierre De Saint, Frederick Donnat, Claire Kreindler, Jack Miolane, Nina
source	ArXiv
abstract	Computer-Aided Diagnosis has shown stellar performance in providing accurate medical diagnoses across multiple testing modalities (medical images, electrophysiological signals, etc.). While this field has typically focused on fully harvesting the signal provided by a single (and generally extremely reliable) modality, fewer efforts have utilized imprecise data lacking reliable ground truth labels. In this unsupervised, noisy setting, the robustification and quantification of the diagnosis uncertainty become paramount, thus posing a new challenge: how can we combine multiple sources of information -- often themselves with vastly varying levels of precision and uncertainty -- to provide a diagnosis estimate with confidence bounds? Motivated by a concrete application in antibody testing, we devise a Stochastic Expectation-Maximization algorithm that allows the principled integration of heterogeneous, and potentially unreliable, data types. Our Bayesian formalism is essential in (a) flexibly combining these heterogeneous data sources and their corresponding levels of uncertainty, (b) quantifying the degree of confidence associated with a given diagnostic, and (c) dealing with the missing values that typically plague medical data. We quantify the potential of this approach on simulated data, and showcase its practicality by deploying it on a real COVID-19 immunity study.
has issue date	2020-07-27(xsd:dateTime)
has license	arxiv
sha1sum (hex)	a8f2d3218e7025d47807574a8359f0887826826b
resource representing a document's title	A Bayesian Hierarchical Network for Combining Heterogeneous Data Sources in Medical Diagnoses
resource representing a document's body	covid:a8f2d3218e7025d47807574a8359f0887826826b#body_text
is schema:about of	named entity 'POTENTIAL' named entity 'PROVIDING' named entity 'BOUNDS' named entity 'SIMULATED' named entity 'ALGORITHM' »more»

Faceted Search & Find service v1.13.91 as of Mar 24 2020

Alternative Linked Data Documents: Sponger | ODE Content Formats:

RDF

ODATA

Microdata

About

OpenLink Virtuoso version 07.20.3229 as of Jul 10 2020, on Linux (x86_64-pc-linux-gnu), Single-Server Edition (94 GB total memory)
Data on this page belongs to its respective rights holders.
Virtuoso Faceted Browser Copyright © 2009-2025 OpenLink Software