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

• URL: http://arxiv.org/abs/2012.12310v2
• Date: Sun, 4 Apr 2021 02:21:30 GMT
• Title: Mixture Model Framework for Traumatic Brain Injury Prognosis Using Heterogeneous Clinical and Outcome Data
• Authors: Alan D. Kaplan, Qi Cheng, K. Aditya Mohan, Lindsay D. Nelson, Sonia Jain, Harvey Levin, Abel Torres-Espin, Austin Chou, J. Russell Huie, Adam R. Ferguson, Michael McCrea, Joseph Giacino, Shivshankar Sundaram, Amy J. Markowitz, Geoffrey T. Manley
• Abstract summary: We develop a method for modeling large heterogeneous data types relevant to TBI. The model is trained on a dataset encompassing a variety of data types, including demographics, blood-based biomarkers, and imaging findings. It is used to stratify patients into distinct groups in an unsupervised learning setting.
• Score: 3.7363119896212478
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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