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126245 |
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|a dc
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|a Weiss, Rebecca J
|e author
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|a Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
|e contributor
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|a Bates, Sara V
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|a Song, Ya'nan
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|a Zhang, Yue
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|a Herzberg, Emily M
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|a Chen, Yih-Chieh
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|a Gong, Maryann M.
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|a Chien, Isabel
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|a Zhang, Lily
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|a Murphy, Shawn N
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|a Gollub, Randy L
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|a Grant, P. E
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|a Ou, Yangming
|e author
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|a Mining multi-site clinical data to develop machine learning MRI biomarkers: application to neonatal hypoxic ischemic encephalopathy
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|b BioMed Central,
|c 2020-07-17T19:27:08Z.
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|z Get fulltext
|u https://hdl.handle.net/1721.1/126245
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|a 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.
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|a en
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|a Article
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|t 10.1186/s12967-019-2119-5
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|t Journal of Translational Medicine
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