Objective Prediction of Pharyngeal Swallow Dysfunction in Dysphagia through Artificial Neural Network Modelling

This article may be used for non-commercial purposes in accordance With Wiley Terms and Conditions for self-archiving'. © 2016 John Wiley & Sons LtdBackground Pharyngeal pressure-flow analysis (PFA) of high resolution impedance-manometry (HRIM) with calculation of the swallow risk index (SRI) can quantify swallow dysfunction predisposing to aspiration. We explored the potential use of artificial neural networks (ANN) to model the relationship between PFA swallow metrics and aspiration and to predict swallow dysfunction. Methods Two hundred consecutive dysphagia patients referred for videofluoroscopy and HRIM were assessed. Presence of aspiration was scored and PFA software derived 13 metrics and the SRI. An ANN was created and optimized over training cycles to achieve optimal classification accuracy for matching inputs (PFA metrics) to output (presence of aspiration on videofluoroscopy). Application of the ANN returned a value between 0.00 and 1.00 reflecting the degree of swallow dysfunction. Key Results Twenty one patients were excluded due to insufficient number of swallows (<4). Of 179, 58 aspirated and 27 had aspiration pneumonia history. The SRI was higher in aspirators (aspiration 24 [9, 41] vs no aspiration 7 [2, 18], p < 0.001) and patients with pneumonia (pneumonia 27 [5, 42] vs no pneumonia 8 [3, 24], p < 0.05). The ANN Predicted Risk was higher in aspirators (aspiration 0.57 [0.38, 0.82] vs no aspiration 0.13 [0.4, 0.25], p < 0.001) and in patients with pneumonia (pneumonia 0.46 [0.18, 0.60] vs no pneumonia 0.18 [0.6, 0.49], p < 0.01). Prognostic value of the ANN was superior to the SRI. Conclusions & Inferences In a heterogeneous cohort of dysphagia patients, PFA with ANN modeling offers enhanced detection of clinically significant swallowing dysfunction, probably more accurately reflecting the complex interplay of swallow characteristics that causes aspiration

Is the metabolic cost of walking higher in people with diabetes?

People with diabetes walk slower and display biomechanical gait alterations compared with controls, but it remains unknown whether the metabolic cost of walking (CoW) is elevated. The aim of this study was to investigate the CoW and the lower limb concentric joint work as a major determinant of the CoW, in patients with diabetes and diabetic peripheral neuropathy (DPN). Thirty-one nondiabetic controls (Ctrl), 22 diabetic patients without peripheral neuropathy (DM), and 14 patients with moderate/severe DPN underwent gait analysis using a motion analysis system and force plates and treadmill walking using a gas analyzer to measure oxygen uptake. The CoW was significantly higher particularly in the DPN group compared with controls and also in the DM group (at selected speeds only) compared with controls, across a range of matched walking speeds. Despite the higher CoW in patients with diabetes, concentric lower limb joint work was significantly lower in DM and DPN groups compared with controls. The higher CoW is likely due to energetic inefficiencies associated with diabetes and DPN reflecting physiological and biomechanical characteristics. The lower concentric joint work in patients with diabetes might be a consequence of kinematic gait alterations and may represent a natural strategy aimed at minimizing the CoW

Comparaison théorique et expérimentale de la pression générée par une matrice de haut-parleurs actionnés de façon analogique et digitale

ISBN 978-2-919340-02-6 - EAN 9782919340026National audienc

Gaining insight into student satisfaction using comprehensible data mining techniques

As a consequence of the heightened competition on the education market, the management of educa- tional institutions often attempts to collect information on what drives student satisfaction by e.g. orga- nizing large scale surveys amongst the student population. Until now, this source of potentially very valuable information remains largely untapped. In this study, we address this issue by investigating the applicability of different data mining techniques to identify the main drivers of student satisfaction in two business education institutions. In the end, the resulting models are to be used by the manage- ment to support the strategic decision making process. Hence, the aspect of model comprehensibility is considered to be at least equally important as model performance. It is found that data mining tech- niques are able to select a surprisingly small number of constructs that require attention in order to man- age student satisfaction