A likelihood-based convolution approach to estimate major health events in longitudinal health records data: An external validation study

Lisiane Pruinelli, Jiaqi Zhou, Bethany Stai, Jesse D. Schold, Timothy Pruett, Sisi Ma, Gyorgy Simon

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


Objective: In electronic health record data, the exact time stamp of major health events, defined by significant physiologic or treatment changes, is often missing. We developed and externally validated a method that can accurately estimate these time stamps based on accurate time stamps of related data elements. Materials and Methods: A novel convolution-based change detection methodology was developed and tested using data from the national deidentified clinical claims OptumLabs data warehouse, then externally validated on a single center dataset derived from the M Health Fairview system. Results: We applied the methodology to estimate time to liver transplantation for waitlisted candidates. The median error between estimated date within the period of the actual true date was zero days, and median error was 92% and 84% of the transplants, in development and validation samples, respectively. Discussion: The proposed method can accurately estimate missing time stamps. Successful external validation suggests that the proposed method does not need to be refit to each health system; thus, it can be applied even when training data at the health system is insufficient or unavailable. The proposed method was applied to liver transplantation but can be more generally applied to any missing event that is accompanied by multiple related events that have accurate time stamps. Conclusion: Missing time stamps in electronic healthcare record data can be estimated using time stamps of related events. Since the model was developed on a nationally representative dataset, it could be successfully transferred to a local health system without substantial loss of accuracy.

Original languageEnglish (US)
Pages (from-to)1885-1891
Number of pages7
JournalJournal of the American Medical Informatics Association
Issue number9
StatePublished - Aug 13 2021

Bibliographical note

Publisher Copyright:
© 2021 The Author(s) 2021. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved.


  • electronic health records
  • event estimates
  • major health events
  • method validation, liver transplantation

PubMed: MeSH publication types

  • Journal Article
  • Research Support, Non-U.S. Gov't


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