Publications (4)20.27 Total impact
- SourceAvailable from: Matteo Rucco[Show abstract] [Hide abstract]
ABSTRACT: 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).
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ABSTRACT: Pulmonary Embolism (PE) is a common and potentially lethal condition. Most patients die within the first few hours from the event. Despite diagnostic advances, delays and underdiagnosis in PE are common.To increase the diagnostic performance in PE, current diagnostic work-up of patients with suspected acute pulmonary embolism usually starts with the assessment of clinical pretest probability using plasma d-Dimer measurement and clinical prediction rules. The most validated and widely used clinical decision rules are the Wells and Geneva Revised scores. We aimed to develop a new clinical prediction rule (CPR) for PE based on topological data analysis and artificial neural network. Filter or wrapper methods for features reduction cannot be applied to our dataset: the application of these algorithms can only be performed on datasets without missing data. Instead, we applied Topological data analysis (TDA) to overcome the hurdle of processing datasets with null values missing data. A topological network was developed using the Iris software (Ayasdi, Inc., Palo Alto). The PE patient topology identified two ares in the pathological group and hence two distinct clusters of PE patient populations. Additionally, the topological netowrk detected several sub-groups among healthy patients that likely are affected with non-PE diseases. TDA was further utilized to identify key features which are best associated as diagnostic factors for PE and used this information to define the input space for a back-propagation artificial neural network (BP-ANN). It is shown that the area under curve (AUC) of BP-ANN is greater than the AUCs of the scores (Wells and revised Geneva) used among physicians. The results demonstrate topological data analysis and the BP-ANN, when used in combination, can produce better predictive models than Wells or revised Geneva scores system for the analyzed cohort
Conference Paper: A data-driven clinical prediction rule for pulmonary embolism[Show abstract] [Hide abstract]
ABSTRACT: Purpose: Pulmonary embolism (PE), a life-threatening emergency is underdiagnosed because of a non-specific presentation.First-level exams (clinical exhamination, electrocardiography, blood gas analysis and laboratory tests)have low sensitivity and specificity.Clinical prediction rulers (CPRs)such as Wells and Geneva Revised, have been derived from different combinations of these exams. Our aim was to perform a comparison between the two score systems in our population and to derive a new CPR using an Artificial Neural Network (ANN). Methods: We enrolled 755 consecutive outpatients with suspect of PE (351 males, mean age 71±14years) and analyzed 24 clinical,instrumental and laboratoristic variables and Wells and Geneva scores.Logistic regression with ROC curves was used to evaluate the the diagnostic reliability of the scores. To derive the new classifier,the dataset was first split (in supervised classification step)into a train and a test subset containing 2/3 ad 1/3 of the patients' dataset,respectively.To find the optimal configuration of the new classifier we tested two different ANNs:a non-linear feed-forward ANN with back-propagation and a Levenberg-Marquardt network.For both we fixed the topological configurations of the network (one hidden layer,one output neuron)and stressed the system to find the optimal number of neurons in the hidden layer for the best configuration among highest AUC with the highest number of hit in the validation process and the minimum epochs.We repeated this study changing the dimension of the input dataset in two ways:excluding interactively some features or performing the reduction of the dimensionality of the feature space with principal component analysis. The application of the trained ANN to a "map set" gave,for each patient,the probability of belonging to the "pathological" or "healthy" class, obtaining the new CPR.Automatic classifications were compared with the manual ones, calculating the Jaccard coefficient, giving a measure of the quality. The system was implemented in Matlab using Neural Network toolboxes and PRTools. Results: In our population,Wells performed better than Revised Geneva (AUC 0.75%,0.63%,respectively),while our CPR (feed-forward ANN with back-propagation) obtained an average AUC of 0.86% from the train set and Jaccard coefficient 0.86 from the map set.The optimal ANN configuration was with 3 neurons in the hidden layer.The difference among the three ROC curves resulted statistically significant. Conclusions: An ANN-based CPR performs better in the clinical prediction of PE than classical rulers without increasing the number of items required for the analysis.European Society of Cardiology, 2013, Amsterdam; 08/2013
- Journal of Thrombosis and Haemostasis 03/2011; 9(5):1081-3. DOI:10.1111/j.1538-7836.2011.04259.x · 5.55 Impact Factor