MODELING UNCERTAINTY IN DEEP LEARNING MODELS OF ELECTRONIC HEALTH RECORDS

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Qiu, R. (2020). MODELING UNCERTAINTY IN DEEP LEARNING MODELS OF ELECTRONIC HEALTH RECORDS. Unc Charlotte Electronic Theses And Dissertations.
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Abstract

Recent research development has demonstrated the advantages of deep learning models in prediction tasks on electronic health records (EHR) in the medical domain. However, the prediction results tend to be difficult to explain due to the complex neuron structures. Without explainability and transparency, deep learning models are not trustworthy or reliable for making real-world decisions, especially the high-stakes ones in the healthcare domain. To improve the trustworthiness of the deep learning model, quantifying the uncertainty is crucial.In this dissertation work, we proposed several Bayesian Neural Network (BNN) structures to estimate the data uncertainty and model uncertainty associated with the EHR data and deep learning models, respectively. We also proposed Variational Neural Network (VNN) algorithms to estimate the uncertainty of the variables to investigate the medical and temporal features that contribute the most to the patient-level uncertainty. In order to verify the validity of the uncertainty estimations, we designed a series of experiments to examine the computational results against widely accepted facts about uncertainty. We also conducted post-hoc analysis to evaluate whether the proposed models tend to specialize in one or more patient subgroups, at the cost of model performance on others, as well as whether the treatment (improving uncertainty in one subgroup) will mitigate such performance cost. The experiment results have confirmed the validity of our computational approaches. Finally, we conducted a user study to understand the clinicians' perception of the proposed uncertainty models.

Details
Author

Qiu, Riyi

Title

MODELING UNCERTAINTY IN DEEP LEARNING MODELS OF ELECTRONIC HEALTH RECORDS

Physical Description

1 online resource (103 pages) : PDF

Date

2020

Degree Granting Institution

University of North Carolina at Charlotte

Abstract

Recent research development has demonstrated the advantages of deep learning models in prediction tasks on electronic health records (EHR) in the medical domain. However, the prediction results tend to be difficult to explain due to the complex neuron structures. Without explainability and transparency, deep learning models are not trustworthy or reliable for making real-world decisions, especially the high-stakes ones in the healthcare domain. To improve the trustworthiness of the deep learning model, quantifying the uncertainty is crucial.In this dissertation work, we proposed several Bayesian Neural Network (BNN) structures to estimate the data uncertainty and model uncertainty associated with the EHR data and deep learning models, respectively. We also proposed Variational Neural Network (VNN) algorithms to estimate the uncertainty of the variables to investigate the medical and temporal features that contribute the most to the patient-level uncertainty. In order to verify the validity of the uncertainty estimations, we designed a series of experiments to examine the computational results against widely accepted facts about uncertainty. We also conducted post-hoc analysis to evaluate whether the proposed models tend to specialize in one or more patient subgroups, at the cost of model performance on others, as well as whether the treatment (improving uncertainty in one subgroup) will mitigate such performance cost. The experiment results have confirmed the validity of our computational approaches. Finally, we conducted a user study to understand the clinicians' perception of the proposed uncertainty models.

Genre

doctoral dissertations

Subjects--Topics

Information technology

Degree

Ph.D.

Keywords

Deep Learning
Electronic Health Records
Risk Prediction
Uncertainty

Subject Area

Information Technology

Advisor(s)

Hadzikadic, Mirsad
Dulin, Michael

Committee Members

Ge, Yaorong
Niu, Xi
Kimble, Jeffery

Degree Note

Thesis (Ph.D.)--University of North Carolina at Charlotte, 2020.

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Identifier

Qiu_uncc_0694D_12591

Permalink

http://hdl.handle.net/20.500.13093/etd:2467