Mining multi-site clinical data to develop machine learning MRI biomarkers: application to neonatal hypoxic ischemic encephalopathy
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12967_2019_Article_2119.pdf
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1.9 MB
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Author(s)
Weiss, Rebecca J
Bates, Sara V
Song, Ya’nan
Zhang, Yue
Herzberg, Emily M
Chen, Yih-Chieh
Gong, Maryann M.
Chien, Isabel
Zhang, Lily
Murphy, Shawn N
Date Issued
November 2019
Journal
Journal of Translational Medicine
Publisher
BioMed Central
Citation
Weiss, Rebecca J. et al. "Mining multi-site clinical data to develop machine learning MRI biomarkers: application to neonatal hypoxic ischemic encephalopathy." Journal of Translational Medicine 17 (Nov. 2019): no. 385 doi 10.1186/s12967-019-2119-5 ©2019 Author(s)
Version
Final published version
Abstract
BACKGROUND: Secondary and retrospective use of hospital-hosted clinical data provides a time- and cost-efficient alternative to prospective clinical trials for biomarker development. This study aims to create a retrospective clinical dataset of Magnetic Resonance Images (MRI) and clinical records of neonatal hypoxic ischemic encephalopathy (HIE), from which clinically-relevant analytic algorithms can be developed for MRI-based HIE lesion detection and outcome prediction. METHODS: This retrospective study will use clinical registries and big data informatics tools to build a multi-site dataset that contains structural and diffusion MRI, clinical information including hospital course, short-term outcomes (during infancy), and long-term outcomes (~ 2 years of age) for at least 300 patients from multiple hospitals. DISCUSSION: Within machine learning frameworks, we will test whether the quantified deviation from our recently-developed normative brain atlases can detect abnormal regions and predict outcomes for individual patients as accurately as, or even more accurately, than human experts.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
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Creative Commons Attribution
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DOI of Published Version
https://doi.org/10.1186/s12967-019-2119-5
