期刊
IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS
卷 26, 期 3, 页码 1285-1296出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JBHI.2021.3099745
关键词
Biomedical imaging; Magnetic resonance imaging; Imaging; Data models; Computed tomography; Predictive models; History; Traumatic brain injury; machine learning; precision medicine; mixture models; latent variable models
类别
资金
- DOE Office of Science's Advanced Computing Science Research [KJ0403020]
- United States Department of Defense -TBI Endpoints Development Initiative [W81XWH-14-2-0176]
- TRACK-TBI Precision Medicine [W81XWH-18-2-0042]
- NIH-NINDS -TRACK-TBI [U01NS086090]
- U.S. Department of Energy by Lawrence Livermore National Laboratory [DE-AC52-07NA27344]
In this study, a method for modeling heterogeneous data types relevant to TBI was developed to depict the nuanced differences in TBI patients' recovery. The model was trained on various data types and used to infer outcomes based on input data. Additionally, the performance of a likelihood scoring technique for evaluating the risk of prognosis extrapolation was quantified.
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.
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