Neural Hypernetwork Approach for Pulmonary Embolism diagnosis

The purpose of the work is to introduce an integrative approach for the analysis of par-tial and incomplete datasets that is based on Q-analysis with machine learning. The new approach, called Neural Hypernetwork, has been applied to a case study of pul-monary embolism diagnosis. The objective of the application of neural hypernetwork to pulmonary embolism (PE) is to improve diagnose, henceforth helping in reducing the number of CT-angiography scans needed for confirmation of the condition. Hyper-networks are based on topological simplicial complexes and generalize the concept of two-body relation to many-body relation. Furthermore, Hypernetworks provide a signif-icant generalisation 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, fre-quently 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 recognised 94 % of those developing Pulmonary Embolism. This is better than previous results obtained with other methods (statistical selection of features, partial least squares regression, topolog-ical data analysis in a metric space)