A Proposed Machine Learning Based Collective Disease Model to Enable Predictive Diagnostics in Necrotising Enterocolitis

Despite 60 years of research into necrotising
enterocolitis (NEC), our understanding of the disease has not
improved enough to achieve better outcomes. Even though
NEC has remained the leading cause of death and poor
outcomes in preterm infants, there remain vital questions on
how to define, differentiate and detect the condition. Numerous
international groups have recently highlighted NEC as a
research priority and called for broader engagement of the
scientific community to move the field forward. The three
foremost barriers at present are lack of suitable definition(s),
lack of clean datasets and consequently a lack of scope to gain
sufficient insights from data. This research paper proposes a
new direction of travel to advance neonatal gastro-intestinal
monitoring and strengthen our efforts to gain better insights
from global databases. An integrated machine learning based
model is recommended to produce a comprehensive disease
model to manage the complexity of this multi-variate disease.
This intelligent disease model would be used in the daily
neonatal settings to help aggregate data to support clinical
decision making, better capture the complexity of each patient
to enrich global datasets to create bigger and better data. This
paper reviews current machine learning and CAD technologies
in neonatology and suggests an innovative approach for an
NEC disease model.

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