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

Claire Donnat, Nina Miolane, Freddy Bunbury, Jack Kreindler
Proceedings of the Machine Learning for Health NeurIPS Workshop, PMLR 136:53-84, 2020.

Abstract

The increasingly widespread use of affordable, yet often less reliable medical data and diagnostic tools poses a new challenge for the field of ComputerAided Diagnosis: how can we combine multiple sources of information with varying levels of precision and uncertainty to provide an informative diagnosis estimate with confidence bounds? Motivated by a concrete application in lateral flow 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 COVID19 immunity study.

Cite this Paper

BibTeX
@InProceedings{pmlr-v136-donnat20a, title = {A {B}ayesian Hierarchical Network for Combining Heterogeneous Data Sources in Medical Diagnoses}, author = {Donnat, Claire and Miolane, Nina and Bunbury, Freddy and Kreindler, Jack}, booktitle = {Proceedings of the Machine Learning for Health NeurIPS Workshop}, pages = {53--84}, year = {2020}, editor = {Alsentzer, Emily and McDermott, Matthew B. A. and Falck, Fabian and Sarkar, Suproteem K. and Roy, Subhrajit and Hyland, Stephanie L.}, volume = {136}, series = {Proceedings of Machine Learning Research}, month = {11 Dec}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v136/donnat20a/donnat20a.pdf}, url = {https://proceedings.mlr.press/v136/donnat20a.html}, abstract = {The increasingly widespread use of affordable, yet often less reliable medical data and diagnostic tools poses a new challenge for the field of ComputerAided Diagnosis: how can we combine multiple sources of information with varying levels of precision and uncertainty to provide an informative diagnosis estimate with confidence bounds? Motivated by a concrete application in lateral flow 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 COVID19 immunity study.} }
Endnote
%0 Conference Paper %T A Bayesian Hierarchical Network for Combining Heterogeneous Data Sources in Medical Diagnoses %A Claire Donnat %A Nina Miolane %A Freddy Bunbury %A Jack Kreindler %B Proceedings of the Machine Learning for Health NeurIPS Workshop %C Proceedings of Machine Learning Research %D 2020 %E Emily Alsentzer %E Matthew B. A. McDermott %E Fabian Falck %E Suproteem K. Sarkar %E Subhrajit Roy %E Stephanie L. Hyland %F pmlr-v136-donnat20a %I PMLR %P 53--84 %U https://proceedings.mlr.press/v136/donnat20a.html %V 136 %X The increasingly widespread use of affordable, yet often less reliable medical data and diagnostic tools poses a new challenge for the field of ComputerAided Diagnosis: how can we combine multiple sources of information with varying levels of precision and uncertainty to provide an informative diagnosis estimate with confidence bounds? Motivated by a concrete application in lateral flow 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 COVID19 immunity study.
APA
Donnat, C., Miolane, N., Bunbury, F. & Kreindler, J.. (2020). A Bayesian Hierarchical Network for Combining Heterogeneous Data Sources in Medical Diagnoses. Proceedings of the Machine Learning for Health NeurIPS Workshop, in Proceedings of Machine Learning Research 136:53-84 Available from https://proceedings.mlr.press/v136/donnat20a.html.

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