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Machine Learning Methods to Identify Hidden Phenotypes in the Electronic Health Record.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	Machine Learning Methods to Identify Hidden Phenotypes in the Electronic Health Record./
Author:	Beaulieu-Jones, Brett Kreigh.
Description:	1 online resource (176 pages)
Notes:	Source: Dissertation Abstracts International, Volume: 79-07(E), Section: B.
Subject:	Bioinformatics. -
Online resource:	click for full text (PQDT)
ISBN:	9780355618037

Machine Learning Methods to Identify Hidden Phenotypes in the Electronic Health Record.
Beaulieu-Jones, Brett Kreigh.

Machine Learning Methods to Identify Hidden Phenotypes in the Electronic Health Record. - 1 online resource (176 pages)

Source: Dissertation Abstracts International, Volume: 79-07(E), Section: B.

Thesis (Ph.D.)--University of Pennsylvania, 2017.

Includes bibliographical references

The widespread adoption of Electronic Health Records (EHRs) means an unprecedented amount of patient treatment and outcome data is available to researchers. Research is a tertiary priority in the EHR, where the priorities are patient care and billing. Because of this, the data is not standardized or formatted in a manner easily adapted to machine learning approaches. Data may be missing for a large variety of reasons ranging from individual input styles to differences in clinical decision making, for example, which lab tests to issue. Few patients are annotated at a research quality, limiting sample size and presenting a moving gold standard. Patient progression over time is key to understanding many diseases but many machine learning algorithms require a snapshot, at a single time point, to create a usable vector form. In this dissertation, we develop new machine learning methods and computational workflows to extract hidden phenotypes from the Electronic Health Record (EHR). In Part 1, we use a semi-supervised deep learning approach to compensate for the low number of research quality labels present in the EHR. In Part 2, we examine and provide recommendations for characterizing and managing the large amount of missing data inherent to EHR data. In Part 3, we present an adversarial approach to generate synthetic data that closely resembles the original data while protecting subject privacy. We also introduce a workflow to enable reproducible research even when data cannot be shared. In Part 4, we introduce a novel strategy to first extract sequential data from the EHR and then demonstrate the ability to model these sequences with deep learning.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2018

Mode of access: World Wide Web

ISBN: 9780355618037Subjects--Topical Terms:

583857
Bioinformatics.
Index Terms--Genre/Form:

554714
Electronic books.

Machine Learning Methods to Identify Hidden Phenotypes in the Electronic Health Record.
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500 $a Source: Dissertation Abstracts International, Volume: 79-07(E), Section: B.
500 $a Advisers: Jason H. Moore; Casey S. Greene.
502 $a Thesis (Ph.D.)--University of Pennsylvania, 2017.
504 $a Includes bibliographical references
520 $a The widespread adoption of Electronic Health Records (EHRs) means an unprecedented amount of patient treatment and outcome data is available to researchers. Research is a tertiary priority in the EHR, where the priorities are patient care and billing. Because of this, the data is not standardized or formatted in a manner easily adapted to machine learning approaches. Data may be missing for a large variety of reasons ranging from individual input styles to differences in clinical decision making, for example, which lab tests to issue. Few patients are annotated at a research quality, limiting sample size and presenting a moving gold standard. Patient progression over time is key to understanding many diseases but many machine learning algorithms require a snapshot, at a single time point, to create a usable vector form. In this dissertation, we develop new machine learning methods and computational workflows to extract hidden phenotypes from the Electronic Health Record (EHR). In Part 1, we use a semi-supervised deep learning approach to compensate for the low number of research quality labels present in the EHR. In Part 2, we examine and provide recommendations for characterizing and managing the large amount of missing data inherent to EHR data. In Part 3, we present an adversarial approach to generate synthetic data that closely resembles the original data while protecting subject privacy. We also introduce a workflow to enable reproducible research even when data cannot be shared. In Part 4, we introduce a novel strategy to first extract sequential data from the EHR and then demonstrate the ability to model these sequences with deep learning.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2018
538 $a Mode of access: World Wide Web
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650 4 $a Artificial intelligence. $3 559380
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