Edinburgh Postpartum Depression (EPDS) and Breastfeeding Self-Efficacy (BSES) scales are standardized questionnaires to screen for postpartum depression and breastfeeding performance self-perception. On the other hand, Natural Language Processing (NLP) is a machine learning technique that analyses the human language to extract relevant and computer-interpretable information. In this work we proposed the application of an NLP toolchain that includes a typical preprocessing stage and the probabilistic topic modeling performed through the Latent Dirichlet Allocation (LDA) to find out the two most relevant topics within each of six study groups (low, medium, and high scores of BSES and EPDS). Each topic LDA-modeled consisted of 30-word/terms (tokens) which are organized in Venn diagrams, contrasting the mutually exclusive tokens within the low and high scores on each scale. Coherence and log-Perplexity topic modeling performance metrics, were computed. We found that LDA-models have distinguishable tokens between low and high scores of the BSES and EPDS. However, the most remarkable findings were two subset of tokens, one related to newborn care and another to newborn intake, respectively correlated to low and high postpartum depression risk according to EPDS.
A previously introduced variation of a conventional P300 speller, consisting on a modifiable image background and asymmetrically arranged stimulation markers for controlling wheelchair navigation, was used in this study. Five commonly used classifiers for solving P300 speller-like tasks, namely, Linear-SVM, RBF-SVM, LASSO-LDA, Shrinkage-LDA and SWLDA, were designed and trained and their performances contrasted, seeking the classifier with highest performance on our proposed screen. 19 able-bodied subjects participated in this study. The highest median sensitivity and specificity were respectively 1.00 (IQR = 0.61-1.00) and 1.00 (IQR = 0.96-1.00), which were obtained with the LASSO approach. These performances are suitable for the planned application and they are comparable with the conventional P300 speller performances reported, despite of our speller variation. Friedman tests showed that there are no statistical differences on the sensitivity and specificity performances among the five classifiers evaluated. However, the customized selection of the classifier approach improves the sensitivity by 66.7% in some cases.
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