A likelihood-based convolution approach to estimate event occurrences in large longitudinal incomplete clinical data

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Registry and clinical trials data have been the preferred data source for vast amounts of research effort. However, these data sources offer limited sample sizes, follow-up time, temporal granularity and often focus on a single disease, making them insufficient for modern research. Recent years has seen the emergence of commercial data sets, aggregated from multiple health system, including pharmacy, clinical and claims data. Unlike registry data, these large-scale data sources can contain inaccuracies and may be incomplete, often missing the dates of major health events. In this paper, we develop a method for estimating the dates of major health events that are defined by significant changes in physiology and treatment regimen before and after the event. Our methodology synthesizes information from multiple sources (labs, medications, procedures, billing and claims diagnoses) using a novel convolution-based change detection methodology. We apply our methodology to identifying liver transplant dates for patients who underwent liver transplant from the OptumLabs® Data Warehouse totaling 31,000 transplant patients. For a cohort of 4,000 patients with known transplant date, our method managed to estimate the transplant date within the two-week window for 92% of the cohort. On the vast majority in the data set (84%), the error (absolute difference between the predicted and true transplant dates in days) is less than one week and for almost the entire data set (92%), the errors fall within two weeks of the true date.

Original languageEnglish (US)
Title of host publication2019 IEEE International Conference on Healthcare Informatics, ICHI 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781538691380
DOIs
StatePublished - Jun 2019
Event7th IEEE International Conference on Healthcare Informatics, ICHI 2019 - Xi'an, China
Duration: Jun 10 2019Jun 13 2019

Publication series

Name2019 IEEE International Conference on Healthcare Informatics, ICHI 2019

Conference

Conference7th IEEE International Conference on Healthcare Informatics, ICHI 2019
CountryChina
CityXi'an
Period6/10/196/13/19

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Keywords

  • Clinical data
  • Convolution-based change detection
  • Incomplete data
  • Liver transplantation

Cite this

Pruinelli, L., Stai, B., Ma, S., Pruett, T., & Simon, G. J. (2019). A likelihood-based convolution approach to estimate event occurrences in large longitudinal incomplete clinical data. In 2019 IEEE International Conference on Healthcare Informatics, ICHI 2019 [8904622] (2019 IEEE International Conference on Healthcare Informatics, ICHI 2019). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICHI.2019.8904622