Neural Hypernetwork Approach for Pulmonary Embolism diagnosis
This work addresses the problem of improving diagnosis accuracy for pulmonary embolism, a frequently fatal condition, to reduce unnecessary CT-angiography scans, though it appears incremental as it builds on existing hypernetwork and Q-analysis concepts.
The authors tackled pulmonary embolism diagnosis by introducing a Neural Hypernetwork approach based on Q-analysis, which correctly recognized 94% of cases in a dataset of 1,427 at-risk individuals, outperforming previous methods.
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).