Introduction: Artificial Intelligence (AI) and machine learning (ML) are used extensively in HICs to detect and control antibiotic resistance (AMR) in laboratories and clinical institutions. ML is ...designed to predict outcome variables using an algorithm to enable "machines" to learn the "rules" from the data. ML is increasingly being applied in intensive care units to identify AMR and to assist empiric antibiotic therapy. This study aimed to evaluate the performance of ML models for predicting AMR bacteria and resistance to antibiotics in two Vietnamese hospitals. Patients and Methods: A cross-sectional study combined with retrospective was conducted from 1st January 2020 to 30th June 2022. Five models were developed to predict antibiotic resistance of bacterial infections of ICU patients. Two datasets were prepared to predict AMR bacteria and antibiotics with ML models. The performance of the prediction models was evaluated by various indicators (sensitivity, specificity, precision, accuracy, F1-score, PRC, AuROC, and NormMCC) to determine the optimal time point for data selection. Python version 3.8 was used for statistical analyses. Results: The accuracy, F1-score, AuROC, and normMMC of LightGBM, XGBoost, and Random Forest models were higher than those of other models in both datasets. In both datasets 1 and 2, accuracy, F1- score, AuROC and normMCC of the XGBoost model were the highest among five models (from 0.890 to 1.000). Only Random Forest models had specificity scores higher than 0.850. High scores of sensitivity, accuracy, precision, F1-score, and normMCC indicated that the models were making accurate predictions for datasets 1 and 2. Conclusion: XGBoost, LightGBM, and Random Forest were the best-performed machine learning models to predict antibiotic resistance of bacterial infections of ICUs patients using the patients' EMRs. Keywords: antibiotic resistance, machine learning, XGBoost, LightGBM, random forest
In this short note, we focus on the use of the generalized Kullback–Leibler (KL) divergence in the problem of non-negative matrix factorization (NMF). We will show that when using the generalized KL ...divergence as cost function for NMF, the row sums and the column sums of the original matrix are preserved in the approximation. We will use this special characteristic in several approximation problems.
The CreditRisk
+ model is one of the industry standards for estimating the credit default risk for a portfolio of credit loans. The natural parameterization of this model requires the default ...probability to be apportioned using a number of (non-negative) factor loadings. However, in practice only default correlations are often available but not the factor loadings. In this paper we investigate how to deduce the factor loadings from a given set of default correlations. This is a novel approach and it requires the non-negative factorization of a positive semi-definite matrix which is by no means trivial. We also present a numerical optimization algorithm to achieve this.
We provide an efficient method to calculate the pseudo-inverse of the
Laplacian of a bipartite graph, which is based on the pseudo-inverse of the
normalized Laplacian.
In this paper, we present several descent methods that can be applied to nonnegative matrix factorization and we analyze a recently developped fast block coordinate method called Rank-one Residue ...Iteration (RRI). We also give a comparison of these different methods and show that the new block coordinate method has better properties in terms of approximation error and complexity. By interpreting this method as a rank-one approximation of the residue matrix, we prove that it \emph{converges} and also extend it to the nonnegative tensor factorization and introduce some variants of the method by imposing some additional controllable constraints such as: sparsity, discreteness and smoothness.
Data-mining has become a hot topic in recent years. It consists of extracting relevant information or structures from data such as: pictures, textual material, networks, etc. Such information or ...structures are usually not trivial to obtain and many techniques have been proposed to address this problem, including Independent Component Analysis, Latent Sematic Analysis, etc.
Nonnegative Matrix Factorization is yet another technique that relies on the nonnegativity of the data and the nonnegativity assumption of the underlying model. The main advantage of this technique is that nonnegative objects are modeled by a combination of some basic nonnegative parts, which provides a physical interpretation of the construction of the objects. This is an exclusive feature that is known to be useful in many areas such as Computer Vision, Information Retrieval, etc.
In this thesis, we look at several aspects of Nonnegative Matrix Factorization, focusing on numerical algorithms and their applications to different kinds of data and constraints. This includes Tensor Nonnegative Factorization, Weighted Nonnegative Matrix Factorization, Symmetric Nonnegative Matrix Factorization, Stochastic Matrix Approximation, etc. The recently proposed Rank-one Residue Iteration (RRI) is the common thread in all of these factorizations. It is shown to be a fast method with good convergence properties which adapts well to many situations.
A second cluster of COVID-19 cases imported from Europe occured in Vietnam from early March 2020. We describe 44 SARS-CoV-2 RT-PCR positive patients (cycle threshold value <30) admitted to the ...National Hospital for Tropical Diseases in Hanoi between March 6 and April 15 2020. Whole SARS-CoV-2 genomes from these patients were sequenced using Illumina Miseq and analysed for common genetic variants and relationships to local and globally circulating strains. Results showed that 32 cases were Vietnamese with a median age of 37 years (range 15-74 years), and 23 were male. Most cases were acquired outside Vietnam, mainly from the UK (n = 15), other European countries (n = 14), Russia (n = 6) and countries in Asia (n = 3). No cases had travelled from China. Forty-one cases had symptoms at admission, typically dry cough (n = 36), fever (n = 20), sore throat (n = 14) and diarrhoea (n = 12). Hospitalisation was long with a median of 25 days, most commonly from 20-29 days. All SARS-CoV-2 genomes were similar (92-100% sequence homology) to the reference sequence Wuhan_1 (NC_045512), and 32 strains belonged to the B.1.1 lineage. The three most common variants were linked, and included C3037T, C14408T (nsp12: P323L) and A23403G (S: D614G) mutations. This group of mutations often accompanied variant C241T (39/44 genomes) or GGG 28881..28883 AAC (33/44 genomes). The prevalence of the former reflected probable European origin of viruses, and the transition D614G was dominant in Vietnam. New variants were identified; however, none could be associated with disease severity.
In 2016, the World Health Organization (WHO) updated the glioma classification by incorporating molecular biology parameters, including low‐grade glioma (LGG). In the new scheme, LGGs have three ...molecular subtypes: isocitrate dehydrogenase (IDH)‐mutated 1p/19q‐codeleted, IDH‐mutated 1p/19q‐noncodeleted, and IDH‐wild type 1p/19q‐noncodeleted entities. This work proposes a model prediction of LGG molecular subtypes using magnetic resonance imaging (MRI). MR images were segmented and converted into radiomics features, thereby providing predictive information about the brain tumor classification. With 726 raw features obtained from the feature extraction procedure, we developed a hybrid machine learning‐based radiomics by incorporating a genetic algorithm and eXtreme Gradient Boosting (XGBoost) classifier, to ascertain 12 optimal features for tumor classification. To resolve imbalanced data, the synthetic minority oversampling technique (SMOTE) was applied in our study. The XGBoost algorithm outperformed the other algorithms on the training dataset by an accuracy value of 0.885. We continued evaluating the XGBoost model, then achieved an overall accuracy of 0.6905 for the three‐subtype classification of LGGs on an external validation dataset. Our model is among just a few to have resolved the three‐subtype LGG classification challenge with high accuracy compared with previous studies performing similar work.
This work proposes a prediction model for low‐grade glioma molecular subtypes using MRI. It is a hybrid machine learning‐based radiomics model developed by incorporating XGBoost and genetic algorithm. It is among just a few studies that have resolves the three‐subtype LGG classification challenge with high accuracy compared with previous studies performing similar work.
Youths and adolescents are vulnerable to HIV/STIs from unprotected sex. Promotion of young population's awareness about risky sexual behaviors is essential to develop contextualized interventions. A ...cross-sectional study was conducted in five Vietnamese provinces to document current attitudes and practices regarding sexual behaviors among youths. The information on sociodemographic characteristics, substance use, and sexual behaviors was collected via self-reported questionnaires. The factors associated with risky sexual behaviors were identified by the multivariate logistic regression. Among the 1200 participants, 73.5% reported having sex in their lifetime, and 48.1% used condoms at their latest sexual intercourse. Participants in urban areas were more likely not to intend to use condoms and had a higher unintended pregnancy rate than in rural areas. Older age was positively associated with not wanting to use and not using condoms. Substance-using participants were more likely to not use condoms. The participants taking alcohol or other stimulants before sex had a higher likelihood of unintended pregnancy. Respondents' attitudes and practices regarding sexual behaviors were associated with gender and employment. This study indicated that young population's awareness in Vietnam is high, however, risky sexual behaviors also remain common. Sex-related educational programs about the consequences of substance use, multiple sex partners, and unprotected sex should be developed.
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