Mining multi-site clinical data to develop machine learning MRI biomarkers: application to neonatal hypoxic ischemic encephalopathy.
作者： Rebecca J. Weiss , Sara V. Bates , Ya’nan Song , Yue Zhang , Emily M. Herzberg
DOI: 10.1186/S12967-019-2119-5
关键词:
摘要: Secondary and retrospective use of hospital-hosted clinical data provides a time- cost-efficient alternative to prospective trials for biomarker development. This study aims create dataset Magnetic Resonance Images (MRI) records neonatal hypoxic ischemic encephalopathy (HIE), from which clinically-relevant analytic algorithms can be developed MRI-based HIE lesion detection outcome prediction. will registries big informatics tools build multi-site that contains structural diffusion MRI, information including hospital course, short-term outcomes (during infancy), long-term (~ 2 years age) at least 300 patients multiple hospitals. Within machine learning frameworks, we test whether the quantified deviation our recently-developed normative brain atlases detect abnormal regions predict individual as accurately as, or even more accurately, than human experts. Trial Registration Not applicable. protocol mines existing thus does not meet ICMJE definition trial requires registration
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