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Neural Event Prediction for Clinical Event Time-Series

Lee, Jeong Min (2022) Neural Event Prediction for Clinical Event Time-Series. Doctoral Dissertation, University of Pittsburgh. (Unpublished)

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Massive clinical event time-series data collected in Electronic Health Records (EHR) offer great potential for improving patient care as they contain in-depth information about patient conditions, relevant diagnoses, and treatment strategies. With event prediction models, we can identify temporal associations among various types of clinical events in EHR, such as symptoms and patient management actions on one side and symptoms and outcomes with or without patient management actions on the other side. Further, we could predict the future occurrence of adverse events and help healthcare practitioners to intervene ahead of time or prepare resources to get ready for their occurrence.

However, building clinical event prediction models has unique challenges posed by inherent characteristics of EHR data: (1) Different temporal characteristics. Each event in the multivariate time-series has different temporal behaviors (e.g., repetitively occurring with certain time gaps) and different temporal ranges of dependencies for precursor events. To accurately predict future events from the multiple event time series with different temporal characteristics, we need more flexible and expressive models. (2) Patient-specific variability. Based on underlying clinical conditions, each patient's sequence may consist of different sets of clinical events (observations, lab results, medications, procedures). Hence, simple population-based models learned from event sequences for many different patients may not accurately predict patient-specific dynamics of event sequences and their differences.

In this thesis, we propose novel autoregressive event prediction models that can address the aforementioned issues. First, we propose new models that handle different temporal dependencies using multiple temporal mechanisms covering various time scales and temporal behaviors such as recurrence of events and multi-time-scale dependencies. Second, we develop new personalized event prediction models that let us better adjust the prediction for individual patients and their specific conditions. They pursue refinement of population-wide models to subpopulations, patient-specific model adaptation, and a meta-level model switching that can adaptively select the model with the best chance to support the immediate prediction. We evaluate our proposed models on the real-world clinical data derived from EHR of critical care patients. We show that our new models lead to improved prediction performance compared to multiple baselines.


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Item Type: University of Pittsburgh ETD
Status: Unpublished
CreatorsEmailPitt UsernameORCID
Lee, Jeong Minjlee@cs.pitt.edujel1580000-0001-8630-0546
ETD Committee:
TitleMemberEmail AddressPitt UsernameORCID
Committee ChairHauskrecht, Milosmilos@cs.pitt.edumilos
Committee MemberKovashka, Adrianakovashka@cs.pitt.edukovashka
Committee MemberWalker, Erineawalker@pitt.edueawalker
Committee MemberVisweswaran, Shyamshv3@pitt.edushv3
Date: 12 October 2022
Date Type: Publication
Defense Date: 27 April 2022
Approval Date: 12 October 2022
Submission Date: 26 May 2022
Access Restriction: No restriction; Release the ETD for access worldwide immediately.
Number of Pages: 159
Institution: University of Pittsburgh
Schools and Programs: Dietrich School of Arts and Sciences > Computer Science
Degree: PhD - Doctor of Philosophy
Thesis Type: Doctoral Dissertation
Refereed: Yes
Uncontrolled Keywords: Clinical Event Prediction, Sequence Modeling, Event Time-Series Modeling
Date Deposited: 12 Oct 2022 15:06
Last Modified: 12 Oct 2022 15:06

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  • Neural Event Prediction for Clinical Event Time-Series. (deposited 12 Oct 2022 15:06) [Currently Displayed]


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