  This work introduces an integrative approach based on Q-analysis with machine
learning. The new approach, called Neural Hypernetwork, has been applied to a
case study of pulmonary embolism diagnosis. The objective of the application of
neural hyper-network to pulmonary embolism (PE) is to improve diagnose for
reducing the number of CT-angiography needed. Hypernetworks, based on
topological simplicial complex, generalize the concept of two-relation to
many-body relation. Furthermore, Hypernetworks provide a significant
generalization of network theory, enabling the integration of relational
structure, logic and analytic dynamics. Another important results is that
Q-analysis stays close to the data, while other approaches manipulate data,
projecting them into metric spaces or applying some filtering functions to
highlight the intrinsic relations. A pulmonary embolism (PE) is a blockage of
the main artery of the lung or one of its branches, frequently fatal. Our study
uses data on 28 diagnostic features of 1,427 people considered to be at risk of
PE. The resulting neural hypernetwork correctly recognized 94% of those
developing a PE. This is better than previous results that have been obtained
with other methods (statistical selection of features, partial least squares
regression, topological data analysis in a metric space).
