Purpose
Lung ultrasound (LUS) examination is used to evaluate patients with acute respiratory failure. The physiological LUS pattern during labor in healthy parturients has not been well described. The aim of this study was to evaluate the LUS pattern in a cohort of healthy women during uncomplicated labor.
Methods
We used the 8‐point LUS assessment protocol and investigated lung sliding, A‐lines, B‐lines, interstitial syndrome, lung consolidation, and pleural effusion according to the International Consensus Document with two additional supradiaphragmatic projections. All patients were screened twice; once during the first stage of labor and again within 2 hours after delivery.
Results
We included 24 patients in this study from February 2014 to August 2015. A total of 480 LUS records were retained for further analysis. Overall, 16 of 24 patients (67%) had at least one positive region (three or more B‐lines) during the peridelivery LUS evaluation. Interstitial syndrome was detected in five patients (21%). There were no differences in A‐line (P = 0.38) or B‐line (P = 0.68) prevalence between LUS examinations before and after delivery.
Conclusions
Women in uncomplicated labor can present abnormal LUS findings, which may affect the interpretation of LUS results in patients with respiratory deterioration. Further studies should address this topic in larger cohort of patients.
The article is focused on the issue of complexity of Fuzzy Cognitive Maps designed to model time series. Large Fuzzy Cognitive Maps are impractical to use. Since Fuzzy Cognitive Maps are graphbased models, when we increase the number of nodes, the number of connections grows quadratically. Therefore, we posed a question how to simplify trained FCM without substantial loss in map's quality. We proposed evaluation of nodes' and weights' relevance based on their influence in the map. The article presents the method first on synthetic time series of different complexity, next on several real-world time series. We illustrate how simplification procedure influences MSE. It turned out that with just a small increase of MSE we can remove up to 1 3 of nodes and up to 1 6 of weights for real-world time series. For regular data sets, like the synthetic time series, FCM-based models can be simplified even more.
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