Title: Mixture Model Framework for Traumatic Brain Injury Prognosis Using Heterogeneous Clinical and Outcome Data

Journal Article · · IEEE Journal of Biomedical and Health Informatics
ORCiD logo [1];  [1]; ORCiD logo [1];  [2]; ORCiD logo [3];  [4]; ORCiD logo [5]; ORCiD logo [5];  [5];  [5];  [2]; ORCiD logo [6];  [1];  [5];  [5]
  1. Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
  2. Medical College of Wisconsin, Milwaukee, WI (United States)
  3. Univ. of California San Diego, La Jolla, CA (United States)
  4. Baylor College of Medicine, Houston, TX (United States)
  5. Univ. of California, San Francisco, CA (United States)
  6. Massachusetts General Hospital, Boston, MA (United States)
+ Show Author Affiliations

Prognoses of Traumatic Brain Injury (TBI) outcomes are neither easily nor accurately determined from clinical indicators. This is due in part to the heterogeneity of damage inflicted to the brain, ultimately resulting in diverse and complex outcomes. Using a data-driven approach on many distinct data elements may be necessary to describe this large set of outcomes and thereby robustly depict the nuanced differences among TBI patients’ recovery. In this work, we develop a method for modeling large heterogeneous data types relevant to TBI. Our approach is geared toward the probabilistic representation of mixed continuous and discrete variables with missing values. The model is trained on a dataset encompassing a variety of data types, including demographics, blood-based biomarkers, and imaging findings. In addition, it includes a set of clinical outcome assessments at 3, 6, and 12 months post-injury. The model is used to stratify patients into distinct groups in an unsupervised learning setting. We use the model to infer outcomes using input data, and show that the collection of input data reduces uncertainty of outcomes over a baseline approach. In addition, we quantify the performance of a likelihood scoring technique that can be used to self-evaluate the extrapolation risk of prognosis on unseen patients.

Research Organization:
Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States)
Sponsoring Organization:
National Institutes of Health (NIH); US Army Research Office (ARO); USDOE National Nuclear Security Administration (NNSA)
Grant/Contract Number:
AC52-07NA27344
OSTI ID:
1860779
Report Number(s):
LLNL-JRNL-815329; 1024473
Journal Information:
IEEE Journal of Biomedical and Health Informatics, Journal Name: IEEE Journal of Biomedical and Health Informatics Journal Issue: 3 Vol. 26; ISSN 2168-2194
Publisher:
IEEECopyright Statement
Country of Publication:
United States
Language:
English

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