Abstract:
Investigating disease pathogenesis and personalized prognostics are major biomedical
needs. Because patients sharing the same diagnosis can experience different outcomes,
such as survival or death, physicians need new personalized tools, including those
that rapidly differentiate several inflammatory phases. To address these topics, a
pattern recognition-based method (PRM) that follows an inverse problem approach
was designed to assess, in <10min, eight concepts: synergy, pleiotropy, complexity,
dynamics, ambiguity, circularity, personalized outcomes, and explanatory prognostics
(pathogenesis). By creating thousands of secondary combinations derived from blood
leukocyte data, the PRM measures synergic, pleiotropic, complex and dynamic
data interactions, which provide personalized prognostics while some undesirable
features—such as false results and the ambiguity associated with data circularity-are
prevented. Here, this method is compared to Principal Component Analysis (PCA) and
evaluated with data collected from hantavirus-infected humans and birds that appeared
to be healthy. When human data were examined, the PRM predicted 96.9 % of all
surviving patients while PCA did not distinguish outcomes. Demonstrating applications
in personalized prognosis, eight PRM data structures sufficed to identify all but one of
the survivors. Dynamic data patterns also distinguished survivors from non-survivors, as
well as one subset of non-survivors, which exhibited chronic inflammation. When the
PRM explored avian data, it differentiated immune profiles consistent with no, early, or
late inflammation. Yet, PCA did not recognize patterns in avian data. Findings support the
notion that immune responses, while variable, are rather deterministic: a low number of
complex and dynamic data combinations may be enough to, rapidly, unmask conditions
that are neither directly observable nor reliably forecasted.
