Introduction: Malnutrition is an important reason for consultation in Mali’s health facilities and remains a major public health problem. The aim of this study was to describe the epidemioclinical profile and associa...Introduction: Malnutrition is an important reason for consultation in Mali’s health facilities and remains a major public health problem. The aim of this study was to describe the epidemioclinical profile and associated factors with performance indicators of integrated management of severe acute malnutrition in children aged 06 to 59 months. Methodology: this was a cross-sectional study with retrospective data collection (January 2021 to December 2022). All children hospitalized for severe acute malnutrition in the pediatric department and whose medical records were usable were included. Data collected using a standardized questionnaire was analyzed with SPSS Version 20 software. Results: A total of 534 children were included. The 12 to 23 months age group (49.1%) and the female sex (53.18%) were the most affected. Fully vaccinated children by age represented 49.4%. The predominant form of malnutrition was marasmus (77.7%). Diarrhea/vomiting (30.3%), fever (18.4%) and cough (15.5%) were the main reasons for consultations. Cure, discontinuation and death rates were 78.5%, 2.1% and 9.2%, respectively. On univariate analysis, the factors statistically associated with performance indicators (cure, drop-out, death) were gastroenteritis (P-value Conclusion: This study reveals that the frequency of severe acute malnutrition remains high at the Kalaban Coro reference health center. Better prevention of illnesses such as malaria, gastroenteritis, and respiratory infections, as well as timely referral, could help facilitate its management.展开更多
<strong>Purpose:</strong><span style="font-family:;" "=""><span style="font-family:Verdana;"> This study sought to review the characteristics, strengths, weak...<strong>Purpose:</strong><span style="font-family:;" "=""><span style="font-family:Verdana;"> This study sought to review the characteristics, strengths, weaknesses variants, applications areas and data types applied on the various </span><span><span style="font-family:Verdana;">Dimension Reduction techniques. </span><b><span style="font-family:Verdana;">Methodology: </span></b><span style="font-family:Verdana;">The most commonly used databases employed to search for the papers were ScienceDirect, Scopus, Google Scholar, IEEE Xplore and Mendeley. An integrative review was used for the study where </span></span></span><span style="font-family:Verdana;">341</span><span style="font-family:;" "=""><span style="font-family:Verdana;"> papers were reviewed. </span><b><span style="font-family:Verdana;">Results:</span></b><span style="font-family:Verdana;"> The linear techniques considered were Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), Singular Value Decomposition (SVD), Latent Semantic Analysis (LSA), Locality Preserving Projections (LPP), Independent Component Analysis (ICA) and Project Pursuit (PP). The non-linear techniques which were developed to work with applications that ha</span></span><span style="font-family:Verdana;">ve</span><span style="font-family:Verdana;"> complex non-linear structures considered were Kernel Principal Component Analysis (KPC</span><span style="font-family:Verdana;">A), Multi</span><span style="font-family:Verdana;">-</span><span style="font-family:;" "=""><span style="font-family:Verdana;">dimensional Scaling (MDS), Isomap, Locally Linear Embedding (LLE), Self-Organizing Map (SOM), Latent Vector Quantization (LVQ), t-Stochastic </span><span style="font-family:Verdana;">neighbor embedding (t-SNE) and Uniform Manifold Approximation and Projection (UMAP). DR techniques can further be categorized into supervised, unsupervised and more recently semi-supervised learning methods. The supervised versions are the LDA and LVQ. All the other techniques are unsupervised. Supervised variants of PCA, LPP, KPCA and MDS have </span><span style="font-family:Verdana;">been developed. Supervised and semi-supervised variants of PP and t-SNE have also been developed and a semi supervised version of the LDA has been developed. </span><b><span style="font-family:Verdana;">Conclusion:</span></b><span style="font-family:Verdana;"> The various application areas, strengths, weaknesses and variants of the DR techniques were explored. The different data types that have been applied on the various DR techniques were also explored.</span></span>展开更多
文摘Introduction: Malnutrition is an important reason for consultation in Mali’s health facilities and remains a major public health problem. The aim of this study was to describe the epidemioclinical profile and associated factors with performance indicators of integrated management of severe acute malnutrition in children aged 06 to 59 months. Methodology: this was a cross-sectional study with retrospective data collection (January 2021 to December 2022). All children hospitalized for severe acute malnutrition in the pediatric department and whose medical records were usable were included. Data collected using a standardized questionnaire was analyzed with SPSS Version 20 software. Results: A total of 534 children were included. The 12 to 23 months age group (49.1%) and the female sex (53.18%) were the most affected. Fully vaccinated children by age represented 49.4%. The predominant form of malnutrition was marasmus (77.7%). Diarrhea/vomiting (30.3%), fever (18.4%) and cough (15.5%) were the main reasons for consultations. Cure, discontinuation and death rates were 78.5%, 2.1% and 9.2%, respectively. On univariate analysis, the factors statistically associated with performance indicators (cure, drop-out, death) were gastroenteritis (P-value Conclusion: This study reveals that the frequency of severe acute malnutrition remains high at the Kalaban Coro reference health center. Better prevention of illnesses such as malaria, gastroenteritis, and respiratory infections, as well as timely referral, could help facilitate its management.
文摘<strong>Purpose:</strong><span style="font-family:;" "=""><span style="font-family:Verdana;"> This study sought to review the characteristics, strengths, weaknesses variants, applications areas and data types applied on the various </span><span><span style="font-family:Verdana;">Dimension Reduction techniques. </span><b><span style="font-family:Verdana;">Methodology: </span></b><span style="font-family:Verdana;">The most commonly used databases employed to search for the papers were ScienceDirect, Scopus, Google Scholar, IEEE Xplore and Mendeley. An integrative review was used for the study where </span></span></span><span style="font-family:Verdana;">341</span><span style="font-family:;" "=""><span style="font-family:Verdana;"> papers were reviewed. </span><b><span style="font-family:Verdana;">Results:</span></b><span style="font-family:Verdana;"> The linear techniques considered were Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), Singular Value Decomposition (SVD), Latent Semantic Analysis (LSA), Locality Preserving Projections (LPP), Independent Component Analysis (ICA) and Project Pursuit (PP). The non-linear techniques which were developed to work with applications that ha</span></span><span style="font-family:Verdana;">ve</span><span style="font-family:Verdana;"> complex non-linear structures considered were Kernel Principal Component Analysis (KPC</span><span style="font-family:Verdana;">A), Multi</span><span style="font-family:Verdana;">-</span><span style="font-family:;" "=""><span style="font-family:Verdana;">dimensional Scaling (MDS), Isomap, Locally Linear Embedding (LLE), Self-Organizing Map (SOM), Latent Vector Quantization (LVQ), t-Stochastic </span><span style="font-family:Verdana;">neighbor embedding (t-SNE) and Uniform Manifold Approximation and Projection (UMAP). DR techniques can further be categorized into supervised, unsupervised and more recently semi-supervised learning methods. The supervised versions are the LDA and LVQ. All the other techniques are unsupervised. Supervised variants of PCA, LPP, KPCA and MDS have </span><span style="font-family:Verdana;">been developed. Supervised and semi-supervised variants of PP and t-SNE have also been developed and a semi supervised version of the LDA has been developed. </span><b><span style="font-family:Verdana;">Conclusion:</span></b><span style="font-family:Verdana;"> The various application areas, strengths, weaknesses and variants of the DR techniques were explored. The different data types that have been applied on the various DR techniques were also explored.</span></span>
