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1.
Biomed Res Int ; 2022: 2239152, 2022.
Artigo em Inglês | MEDLINE | ID: mdl-35909490

RESUMO

One of the most widely used measures of scientific impact is the number of citations. However, due to its heavy-tailed distribution, citations are fundamentally difficult to predict but can be improved. This study was aimed at investigating the factors and parts influencing the citation number of a scientific paper in the otology field. Therefore, this work proposes a new solution that utilizes machine learning and natural language processing to process English text and provides a paper citation as the predicted results. Different algorithms are implemented in this solution, such as linear regression, boosted decision tree, decision forest, and neural networks. The application of neural network regression revealed that papers' abstracts have more influence on the citation numbers of otological articles. This new solution has been developed in visual programming using Microsoft Azure machine learning at the back end and Programming Without Coding Technology at the front end. We recommend using machine learning models to improve the abstracts of research articles to get more citations.


Assuntos
Aprendizado de Máquina , Otolaringologia , Algoritmos , Modelos Lineares
2.
Comput Math Methods Med ; 2022: 3336644, 2022.
Artigo em Inglês | MEDLINE | ID: mdl-35924111

RESUMO

Good health is the most important and very necessary characteristic for stress-free, skillful, and hardworking people with a cooperative environment to create a sustainable society. Validating two algorithms, namely, sequential minimal optimization for regression (SMOreg) using vector machine and linear regression (LR) and using their predicted cancer patients' cases, this study presents a patient's stress estimation model (PSEM) to forecast their families' stress for patients' sustainable health and better care with early management by under-study cancer hospitals. The year-wise predictions (1998-2010) by LR and SMOreg are verified by comparing with observed values. The statistical difference between the predictions (2021-2030) by these models is analyzed using a statistical t-test. From the data of 217067 patients, patients' stress-impacting factors are extracted to be used in the proposed PSEM. By considering the total population of under-study areas and getting the predicted population (2021-2030) of each area, the proposed PSEM forecasts overall stress for expected cancer patients (2021-2030). Root mean square error (RMSE) (1076.15.46) for LR is less than RSME for SMOreg (1223.75); hence, LR remains better than SMOreg in forecasting (2011-2020). There is no significant statistical difference between values (2021-2030) predicted by LR and SMOreg (p value = 0.767 > 0.05). The average stress for a family member of a cancer patient is 72.71%. It is concluded that under-study areas face a minimum of 2.18% stress, on average 30.98% stress, and a maximum of 94.81% overall stress because of 179561 expected cancer patients of all major types from 2021 to 2030.


Assuntos
Algoritmos , Neoplasias , Família , Previsões , Humanos , Modelos Lineares
3.
PLoS One ; 17(8): e0271766, 2022.
Artigo em Inglês | MEDLINE | ID: mdl-35925980

RESUMO

Ideally, a patient's response to medication can be monitored by measuring changes in performance of some activity. In observational studies, however, any detected association between treatment ("on-medication" vs "off-medication") and the outcome (performance in the activity) might be due to confounders. In particular, causal inferences at the personalized level are especially vulnerable to confounding effects that arise in a cyclic fashion. For quick acting medications, effects can be confounded by circadian rhythms and daily routines. Using the time-of-the-day as a surrogate for these confounders and the performance measurements as captured on a smartphone, we propose a personalized statistical approach to disentangle putative treatment and "time-of-the-day" effects, that leverages conditional independence relations spanned by causal graphical models involving the treatment, time-of-the-day, and outcome variables. Our approach is based on conditional independence tests implemented via standard and temporal linear regression models. Using synthetic data, we investigate when and how residual autocorrelation can affect the standard tests, and how time series modeling (namely, ARIMA and robust regression via HAC covariance matrix estimators) can remedy these issues. In particular, our simulations illustrate that when patients perform their activities in a paired fashion, positive autocorrelation can lead to conservative results for the standard regression approach (i.e., lead to deflated true positive detection), whereas negative autocorrelation can lead to anticonservative behavior (i.e., lead to inflated false positive detection). The adoption of time series methods, on the other hand, leads to well controlled type I error rates. We illustrate the application of our methodology with data from a Parkinson's disease mobile health study.


Assuntos
Medicina de Precisão , Telemedicina , Causalidade , Humanos , Modelos Lineares , Smartphone
4.
BMC Oral Health ; 22(1): 270, 2022 07 04.
Artigo em Inglês | MEDLINE | ID: mdl-35787289

RESUMO

BACKGROUND: Prediction of susceptibility to Orthodontically Induced External Apical Root Resorption (OIEARR) has been hampered by the complex architecture of this multifactorial phenotype. The aim of this study was to analyze the impact of the interaction of multiple variables in the susceptibility to OIEARR. METHODS: The study evaluated 195 patients requiring orthodontic treatment. Nine clinical and treatment variables, single nucleotide polymorphisms (SNPs) from five genes and variables interactions were analyzed as risk factors for OIEARR using a multiple linear regression model. RESULTS: The model explained 29% of OIEARR variability (ANOVA: p < 0.01). Duration of treatment was the most important predictor and gender was the second, closely followed by premolar extraction. For genes encoding osteoprotegerin (OPG), the receptor activator of nuclear factor κ B (RANK) and the IL1 receptor antagonist (IL1RN), the effect of analyzed variants changed from protective to deleterious depending on the duration of treatment and the age of the patient. CONCLUSIONS: This work shows that in OIEARR the impact of genetic susceptibility factors is dynamic changing according to clinical variables.


Assuntos
Reabsorção da Raiz , Predisposição Genética para Doença/genética , Humanos , Modelos Lineares , Polimorfismo de Nucleotídeo Único/genética , Reabsorção da Raiz/genética
5.
Artigo em Inglês | MEDLINE | ID: mdl-35805486

RESUMO

Groundwater is a significant component of water resources, but drinking groundwater with excessive heavy metals (HMs) is harmful to human health. Currently, quantitative source apportionment and probabilistic health risk assessment of HMs in groundwater are relatively limited. In this study, 60 groundwater samples containing seven HMs were collected from Hainan Island and analyzed by the coupled absolute principal component scores/multiple linear regression (APCS/MLR), the health risk assessment (HRA) and the Monte Carlo simulation (MCS) to quantify the pollution sources of HMs and the health risks. The results show that the high-pollution-value areas of HMs are mainly located in the industry-oriented western region, but the pollution level by HMs in the groundwater in the study area is generally low. The main sources of HMs in the groundwater are found to be the mixed sources of agricultural activities and traffic emissions (39.16%), industrial activities (25.57%) and natural sources (35.27%). Although the non-carcinogenic risks for adults and children are negligible, the carcinogenic risks are at a high level. Through analyzing the relationship between HMs, pollution sources, and health risks, natural sources contribute the most to the health risks, and Cr is determined as the priority control HM. This study emphasizes the importance of quantitative evaluation of the HM pollution sources and probabilistic health risk assessment, which provides an essential basis for water pollution prevention and control in Hainan Island.


Assuntos
Água Subterrânea , Metais Pesados , Poluentes do Solo , Adulto , Criança , China , Monitoramento Ambiental , Humanos , Modelos Lineares , Metais Pesados/análise , Método de Monte Carlo , Medição de Risco , Poluentes do Solo/análise
6.
Sensors (Basel) ; 22(13)2022 Jun 30.
Artigo em Inglês | MEDLINE | ID: mdl-35808457

RESUMO

Unlimited access to information and data sharing wherever and at any time for anyone and anything is a fundamental component of fifth-generation (5G) wireless communication and beyond. Therefore, it has become inevitable to exploit the super-high frequency (SHF) and millimeter-wave (mmWave) frequency bands for future wireless networks due to their attractive ability to provide extremely high data rates because of the availability of vast amounts of bandwidth. However, due to the characteristics and sensitivity of wireless signals to the propagation effects in these frequency bands, more accurate path loss prediction models are vital for the planning, evaluating, and optimizing future wireless communication networks. This paper presents and evaluates the performance of several well-known machine learning methods, including multiple linear regression (MLR), polynomial regression (PR), support vector regression (SVR), as well as the methods using decision trees (DT), random forests (RF), K-nearest neighbors (KNN), artificial neural networks (ANN), and artificial recurrent neural networks (RNN). RNNs are mainly based on long short-term memory (LSTM). The models are compared based on measurement data to provide the best fitting machine-learning-based path loss prediction models. The main results obtained from this study show that the best root-mean-square error (RMSE) performance is given by the ANN and RNN-LSTM methods, while the worst is for the MLR method. All the RMSE values for the given learning techniques are in the range of 0.0216 to 2.9008 dB. Furthermore, this work shows that the models (except for the MLR model) perform excellently in fitting actual measurement data for wireless communications in enclosed indoor environments since they provide R-squared and correlation values higher than 0.91 and 0.96, respectively. The paper shows that these learning methods could be used as accurate and stable models for predicting path loss in the mmWave frequency regime.


Assuntos
Aprendizado de Máquina , Redes Neurais de Computação , Algoritmos , Previsões , Modelos Lineares
7.
BMC Genomics ; 23(1): 491, 2022 Jul 06.
Artigo em Inglês | MEDLINE | ID: mdl-35794534

RESUMO

BACKGROUND: To detect changes in biological processes, samples are often studied at several time points. We examined expression data measured at different developmental stages, or more broadly, historical data. Hence, the main assumption of our proposed methodology was the independence between the examined samples over time. In addition, however, the examinations were clustered at each time point by measuring littermates from relatively few mother mice at each developmental stage. As each examination was lethal, we had an independent data structure over the entire history, but a dependent data structure at a particular time point. Over the course of these historical data, we wanted to identify abrupt changes in the parameter of interest - change points. RESULTS: In this study, we demonstrated the application of generalized hypothesis testing using a linear mixed effects model as a possible method to detect change points. The coefficients from the linear mixed model were used in multiple contrast tests and the effect estimates were visualized with their respective simultaneous confidence intervals. The latter were used to determine the change point(s). In small simulation studies, we modelled different courses with abrupt changes and compared the influence of different contrast matrices. We found two contrasts, both capable of answering different research questions in change point detection: The Sequen contrast to detect individual change points and the McDermott contrast to find change points due to overall progression. We provide the R code for direct use with provided examples. The applicability of those tests for real experimental data was shown with in-vivo data from a preclinical study. CONCLUSION: Simultaneous confidence intervals estimated by multiple contrast tests using the model fit from a linear mixed model were capable to determine change points in clustered expression data. The confidence intervals directly delivered interpretable effect estimates representing the strength of the potential change point. Hence, scientists can define biologically relevant threshold of effect strength depending on their research question. We found two rarely used contrasts best fitted for detection of a possible change point: the Sequen and McDermott contrasts.


Assuntos
Modelos Lineares , Animais , Simulação por Computador , Camundongos
8.
Scanning ; 2022: 4105169, 2022.
Artigo em Inglês | MEDLINE | ID: mdl-35844265

RESUMO

The objective of this research is to study the effect of obstructive sleep apnea-hypopnea syndrome on cognitive function of stroke. Based on linear regression equation and Montreal Cognitive Assessment Scale, the degree of cognitive impairment in OSAHS patients was evaluated and the influencing factors of OSAHS-induced cognitive impairment and the correlation between the degree of OSAHS and cognitive impairment were explored. The results are as follows: about 68% of OSAHS patients have cognitive dysfunction, and the incidence of cognitive dysfunction is positively correlated with OSAHS; cognitive impairment of OSAHS patients was associated with age, obesity, years of schooling, and intermittent nocturnal hypoxia or hypoventilation; the severity of cognitive dysfunction of OSAHS patients was positively correlated with age and obesity but negatively correlated with education level; Logistic regression analysis results showed that there were three factors that were finally entered into the regression equation, namely, LSaO2, BMI, and AHI, and the Logistic regression equation obtained was as follows: LogistP = -0.109X 1 + 0.785X 2 + 1.228X 3. This study helps clinical workers to detect and intervene the impaired cognitive ability of patients with OSAHS early, so as to reduce the incidence and mortality of related complications and improve the quality of life of patients.


Assuntos
AVC Isquêmico , Apneia Obstrutiva do Sono , Cognição , Humanos , Modelos Lineares , Obesidade/complicações , Polissonografia , Qualidade de Vida , Apneia Obstrutiva do Sono/complicações , Apneia Obstrutiva do Sono/diagnóstico
9.
Front Public Health ; 10: 922563, 2022.
Artigo em Inglês | MEDLINE | ID: mdl-35844875

RESUMO

Objectives: This study investigates the trends of blood lead levels in US pregnant women based on the National Health and Nutrition Examination Survey from 2001 to 2018. Methods: A total of 1,230 pregnant women were included in this study. The weighted logistic regression was applied to analyze the association between sociodemographic characteristics with high blood levels. We computed the blood lead levels for each survey period from 2001-2002 to 2017-2018. Moreover, we used the adjusted linear regression model to investigate the time-related change in blood lead level. The odds ratio (OR) with a 95% confidence interval (CI) was calculated accordingly. Results: The mean blood lead was 0.73 ± 0.03 ug/dL, and high blood lead was observed in 2.53% of individuals. The Mexican Americans were more associated with high blood lead than the non-Hispanic white (OR, 1.072; 95% CI, 1.032-1.112). The mean blood lead level has decreased from 0.97 ug/dL in 2001-2002 to 0.46 ug/dL in 2013-2014. Afterward, a slight increase was observed with the mean blood lead of 0.55 ug/dL in 2015-2016 and 0.53 ug/dL in 2017-2018. In the adjusted linear regression model, each year's increase would lead to a 0.029 ug/dL decrease in blood lead (P < 0.001). However, no significant change was observed in the 2017-2018 cycle compared with 2009-2010 (P = 0.218). Conclusion: This study summarized the trend of blood lead levels in US pregnant women over 2001-2018. Continued effort is still required to control lead sources better and protect this population from lead exposure.


Assuntos
Chumbo , Gestantes , Feminino , Humanos , Modelos Lineares , Inquéritos Nutricionais , Gravidez , Inquéritos e Questionários
10.
Traffic Inj Prev ; 23(7): 434-439, 2022.
Artigo em Inglês | MEDLINE | ID: mdl-35878003

RESUMO

OBJECTIVE: Pedestrian fatalities in the United States increased 51% from 2009 to 2019. During that time, pedestrian fatalities occurring at night increased by 63.7%, compared to a 17.6% increase for pedestrian fatalities occurring during daylight conditions. Have there also been increases in serious, minor, and possible pedestrian injuries (i.e., have all pedestrian collisions been occurring more frequently)? Have pedestrian collisions been getting more severe (i.e., are there now higher proportions of more severe injuries)? Have trends differed between night and day? What role does street lighting play in the nighttime trends? METHODS: We analyzed pedestrian fatalities, serious injuries, minor injuries, and possible injuries that occurred in California, North Carolina, and Texas from 2010 to 2019 using linear regressions to explore the strength and statistical significance of trends. We then parsed these trends by lighting condition, exploring outcomes during the day and night and with and without street lighting. RESULTS: Findings suggest that increases in daytime minor (7.9%) and possible (7.5%) injuries closely mirrored increases in population (9.8%). Increases in daytime fatal/serious injuries were significantly higher (43.1% and 35.1%, respectively), suggesting worsening severities during the day. Increases in nighttime minor/possible injuries (31.9% and 27.6%, respectively) were significantly larger than those during the day, suggesting that pedestrian collisions are occurring more frequently at night. Substantial increases in nighttime fatal/serious injuries (78.0% and 74.7%, respectively) likely reflect a combination of worsening severity (seen throughout the day) and increasing frequency (seen particularly at night). A pedestrian injured in the dark was found to be 5.0 times more likely to be killed than a pedestrian injured during the day. While a lack of street lighting does not seem to be the cause of the disproportionate increase in pedestrian injuries at night, pedestrians struck without a street light were 2.4 times more likely to be killed than those struck in the presence of a street light. CONCLUSIONS: As we find ourselves in the midst of a pedestrian safety crisis, understanding that severities have increased throughout the entire day and frequencies have increased particularly at night helps illuminate a path forward.


Assuntos
Pedestres , Ferimentos e Lesões , Acidentes de Trânsito , Humanos , Iluminação , Modelos Lineares , North Carolina , Ferimentos e Lesões/epidemiologia
11.
BMC Pregnancy Childbirth ; 22(1): 597, 2022 Jul 26.
Artigo em Inglês | MEDLINE | ID: mdl-35883058

RESUMO

BACKGROUND: Infant mortality is defined as the death of a child at any time after birth and before the child's first birthday. Sub-Saharan Africa has the highest infant and child mortality rate in the world. Infant and child mortality rates are higher in Ethiopia. A study was carried out to estimate the risk factors that affect infant mortality in Ethiopia. METHOD: The EDHS- 2016 data set was used for this study. A total of 10,547 mothers from 11 regions were included in the study's findings. To estimate the risk factors associated with infant mortality in Ethiopia, several count models (Poisson, Negative Binomial, Zero-Infated Poisson, Zero-Infated Negative Binomial, Hurdle Poisson, and Hurdle Negative Binomial) were considered. RESULT: The average number of infant deaths was 0.526, with a variance of 0.994, indicating over-dispersion. The highest mean number of infant death occurred in Somali (0.69) and the lowest in Addis Ababa (0.089). Among the multilevel log linear models, the ZINB regression model with deviance (17,868.74), AIC (17,938.74), and BIC (1892.97) are chosen as the best model for estimating the risk factors affecting infant mortality in Ethiopia. However, the results of a multilevel ZINB model with a random intercept and slope model revealed that residence, mother's age, household size, mother's age at first birth, breast feeding, child weight, contraceptive use, birth order, wealth index, father education level, and birth interval are associated with infant mortality in Ethiopia. CONCLUSION: Infant deaths remains high and infant deaths per mother differ across regions. An optimal fit was found to the data based on a multilevel ZINB model. We suggest fitting the ZINB model to count data with excess zeros originating from unknown sources such as infant mortality.


Assuntos
Morte do Lactente , Mortalidade Infantil , Criança , Etiópia/epidemiologia , Feminino , Humanos , Lactente , Modelos Lineares , Análise Multinível , Fatores de Risco
12.
Sci Rep ; 12(1): 12430, 2022 Jul 20.
Artigo em Inglês | MEDLINE | ID: mdl-35859042

RESUMO

This paper considers a linear regression model with stochastic restrictions,we propose a new mixed Kibria-Lukman estimator by combining the mixed estimator and the Kibria-Lukman estimator.This new estimator is a general estimation, including OLS estimator, mixed estimator and Kibria-Lukman estimator as special cases. In addition, we discuss the advantages of the new estimator based on MSEM criterion, and illustrate the theoretical results through examples and simulation analysis.


Assuntos
Modelos Lineares , Simulação por Computador
13.
Comput Intell Neurosci ; 2022: 3687598, 2022.
Artigo em Inglês | MEDLINE | ID: mdl-35860635

RESUMO

A divorce is a legal step taken by married people to end their marriage. It occurs after a couple decides to no longer live together as husband and wife. Globally, the divorce rate has more than doubled from 1970 until 2008, with divorces per 1,000 married people rising from 2.6 to 5.5. Divorce occurs at a rate of 16.9 per 1,000 married women. According to the experts, over half of all marriages ends in divorce or separation in the United States. A novel ensemble learning technique based on advanced machine learning algorithms is proposed in this study. The support vector machine (SVM), passive aggressive classifier, and neural network (MLP) are applied in the context of divorce prediction. A question-based dataset is created by the field specialist. The responses to the questions provide important information about whether a marriage is likely to turn into divorce in the future. The cross-validation is applied in 5 folds, and the performance results of the evaluation metrics are examined. The accuracy score is 100%, and Receiver Operating Characteristic (ROC) curve accuracy score, recall score, the precision score, and the F1 accuracy score are close to 97% confidently. Our findings examined the key indicators for divorce and the factors that are most significant when predicting the divorce.


Assuntos
Divórcio , Máquina de Vetores de Suporte , Países Desenvolvidos , Feminino , Humanos , Modelos Lineares , Redes Neurais de Computação , Estados Unidos
14.
PLoS One ; 17(7): e0271201, 2022.
Artigo em Inglês | MEDLINE | ID: mdl-35816484

RESUMO

Plant leaf area (LA) is a key metric in plant monitoring programs. Machine learning methods were used in this study to estimate the LA of four plum genotypes, including three greengage genotypes (Prunus domestica [subsp. italica var. claudiana.]) and a single myrobalan plum (prunus ceracifera), using leaf length (L) and width (W) values. To develop reliable models, 5548 leaves were subjected to experiments in two different years, 2019 and 2021. Image processing technique was used to extract dimensional leaf features, which were then fed into Linear Multivariate Regression (LMR), Support Vector Regression (SVR), Artificial Neural Networks (ANN), and the Adaptive Neuro-Fuzzy Inference System (ANFIS). Model evaluation on 2019 data revealed that the LMR structure LA = 0.007+0.687 L×W was the most accurate among the various LMR structures, with R2 = 0.9955 and Root Mean Squared Error (RMSE) = 0.404. In this case, the linear kernel-based SVR yielded an R2 of 0.9955 and an RMSE of 0.4871. The ANN (R2 = 0.9969; RMSE = 0.3420) and ANFIS (R2 = 0.9971; RMSE = 0.3240) models demonstrated greater accuracy than the LMR and SVR models. Evaluating the models mentioned above on data from various genotypes in 2021 proved their applicability for estimating LA with high accuracy in subsequent years. In another research segment, LA prediction models were developed using data from 2021, and evaluations demonstrated the superior performance of ANN and ANFIS compared to LMR and SVR models. ANFIS, ANN, LMR, and SVR exhibited R2 values of 0.9971, 0.9969, 0.9950, and 0.9948, respectively. It was concluded that by combining image analysis and modeling through ANFIS, a highly accurate smart non-destructive LA measurement system could be developed.


Assuntos
Lógica Fuzzy , Prunus domestica , Modelos Lineares , Redes Neurais de Computação , Folhas de Planta/genética
15.
Sci Rep ; 12(1): 12478, 2022 Jul 21.
Artigo em Inglês | MEDLINE | ID: mdl-35864287

RESUMO

This study aims to compare the performance of multiple linear regression and machine learning algorithms for predicting manure nitrogen excretion in lactating dairy cows, and to develop new machine learning prediction models for MN excretion. Dataset used were collated from 43 total diet digestibility studies with 951 lactating dairy cows. Prediction models for MN were developed and evaluated using MLR technique and three machine learning algorithms, artificial neural networks, random forest regression and support vector regression. The ANN model produced a lower RMSE and a higher CCC, compared to the MLR, RFR and SVR model, in the tenfold cross validation. Meanwhile, a hybrid knowledge-based and data-driven approach was developed and implemented to selecting features in this study. Results showed that the performance of ANN models were greatly improved by the turning process of selection of features and learning algorithms. The proposed new ANN models for prediction of MN were developed using nitrogen intake as the primary predictor. Alternative models were also developed based on live weight and milk yield for use in the condition where nitrogen intake data are not available (e.g., in some commercial farms). These new models provide benchmark information for prediction and mitigation of nitrogen excretion under typical dairy production conditions managed within grassland-based dairy systems.


Assuntos
Lactação , Nitrogênio , Algoritmos , Animais , Bovinos , Dieta/veterinária , Feminino , Modelos Lineares , Aprendizado de Máquina , Leite/química , Nitrogênio/análise
16.
PLoS Comput Biol ; 18(7): e1010108, 2022 Jul.
Artigo em Inglês | MEDLINE | ID: mdl-35793382

RESUMO

Determining associations between intestinal bacteria and continuously measured physiological outcomes is important for understanding the bacteria-host relationship but is not straightforward since abundance data (compositional data) are not normally distributed. To address this issue, we developed a fully Bayesian linear regression model (BRACoD; Bayesian Regression Analysis of Compositional Data) with physiological measurements (continuous data) as a function of a matrix of relative bacterial abundances. Bacteria can be classified as operational taxonomic units or by taxonomy (genus, family, etc.). Bacteria associated with the physiological measurement were identified using a Bayesian variable selection method: Stochastic Search Variable Selection. The output is a list of inclusion probabilities ([Formula: see text]) and coefficients that indicate the strength of the association ([Formula: see text]) for each bacterial taxa. Tests with simulated communities showed that adopting a cut point value of [Formula: see text] ≥ 0.3 for identifying included bacteria optimized the true positive rate (TPR) while maintaining a false positive rate (FPR) of ≤ 5%. At this point, the chances of identifying non-contributing bacteria were low and all well-established contributors were included. Comparison with other methods showed that BRACoD (at [Formula: see text] ≥ 0.3) had higher precision and a higher TPR than a commonly used center log transformed LASSO procedure (clr-LASSO) as well as higher TPR than an off-the-shelf Spike and Slab method after center log transformation (clr-SS). BRACoD was also less likely to include non-contributing bacteria that merely correlate with contributing bacteria. Analysis of a rat microbiome experiment identified 47 operational taxonomic units that contributed to fecal butyrate levels. Of these, 31 were positively and 16 negatively associated with butyrate. Consistent with their known role in butyrate metabolism, most of these fell within the Lachnospiraceae and Ruminococcaceae. We conclude that BRACoD provides a more precise and accurate method for determining bacteria associated with a continuous physiological outcome compared to clr-LASSO. It is more sensitive than a generalized clr-SS algorithm, although it has a higher FPR. Its ability to distinguish genuine contributors from correlated bacteria makes it better suited to discriminating bacteria that directly contribute to an outcome. The algorithm corrects for the distortions arising from compositional data making it appropriate for analysis of microbiome data.


Assuntos
Bactérias , Microbiota , Animais , Teorema de Bayes , Butiratos , Clostridiales , Modelos Lineares , Ratos
17.
Sci Rep ; 12(1): 12276, 2022 Jul 19.
Artigo em Inglês | MEDLINE | ID: mdl-35853908

RESUMO

To analyze species count data when detection is imperfect, ecologists need models to estimate relative abundance in the presence of unknown sources of heterogeneity. Two candidate models are generalized linear mixed models (GLMMs) and hierarchical N-mixture models. GLMMs are computationally robust but do not explicitly separate detection from abundance patterns. N-mixture models separately estimate detection and abundance via a latent state but are sensitive to violations in assumptions and subject to practical estimation issues. When one can assume that detection is not systematically confounded with ecological patterns of interest, these two models can be viewed as sharing a heuristic framework for relative abundance estimation. Model selection can then determine which predicts observed counts best, for example by AIC. We compared four N-mixture model variants and two GLMM variants for predicting bird counts in local subsets of a citizen science dataset, eBird, based on model selection and goodness-of-fit measures. We found that both GLMMs and N-mixture models-especially N-mixtures with beta-binomial detection submodels-were supported in a moderate number of datasets, suggesting that both tools are useful and that relative fit is context-dependent. We provide faster software implementations of N-mixture likelihood calculations and a reparameterization to interpret unstable estimates for N-mixture models.


Assuntos
Ciência do Cidadão , Animais , Aves , Modelos Lineares , Modelos Estatísticos , Probabilidade , Software
18.
J Am Acad Orthop Surg ; 30(14): 669-675, 2022 Jul 15.
Artigo em Inglês | MEDLINE | ID: mdl-35797680

RESUMO

INTRODUCTION: Out-of-pocket (OOP) costs for medical and surgical care can result in substantial financial burden for patients and families. Relatively little is known regarding OOP costs for commercially insured patients receiving orthopaedic surgery. The aim of this study is to analyze the trends in OOP costs for common, elective orthopaedic surgeries performed in the hospital inpatient setting. METHODS: This study used an employer-sponsored insurance claims database to analyze billing data of commercially insured patients who underwent elective orthopaedic surgery between 2014 and 2019. Patients who received single-level anterior cervical diskectomy and fusion (ACDF), single-level posterior lumbar fusion (PLF), total knee arthroplasty (TKA), and total hip arthroplasty (THA) were identified. OOP costs associated with the surgical episode were calculated as the sum of deductible payments, copayments, and coinsurance. Monetary data were adjusted to 2019 dollars. General linear regression, Wilcoxon-Mann-Whitney, and Kruskal-Wallis tests were used for analysis, as appropriate. RESULTS: In total, 10,225 ACDF, 28,841 PLF, 70,815 THA, and 108,940 TKA patients were analyzed. Most patients in our study sample had preferred provider organization insurance plans (ACDF 70.3%, PLF 66.9%, THA 66.2%, and TKA 67.0%). The mean OOP costs for patients, by procedure, were as follows: ACDF $3,180 (SD = 2,495), PLF $3,166 (SD = 2,529), THA $2,884 (SD = 2,100), and TKA $2,733 (SD = 1,994). Total OOP costs increased significantly from 2014 to 2019 for all procedures (P < 0.0001). Among the insurance plans examined, patients with high-deductible health plans had the highest episodic OOP costs. The ratio of patient contribution (OOP costs) to total insurer contribution (payments from insurers to providers) was 0.07 for ACDF, 0.04 for PLF, 0.07 for THA, and 0.07 for TKA. CONCLUSION: Among commercially insured patients who underwent elective spinal fusion and major lower extremity joint arthroplasty surgery, OOP costs increased from 2014 to 2019. The OOP costs for elective orthopaedic surgery represent a substantial and increasing financial burden for patients.


Assuntos
Artroplastia de Quadril/economia , Artroplastia do Joelho/economia , Discotomia/economia , Procedimentos Cirúrgicos Eletivos/economia , Gastos em Saúde , Fusão Vertebral/economia , Discotomia/métodos , Humanos , Modelos Lineares , Estudos Retrospectivos , Estatísticas não Paramétricas
19.
Pharm Stat ; 21(4): 720-728, 2022 07.
Artigo em Inglês | MEDLINE | ID: mdl-35819119

RESUMO

The Acute Stroke Therapy by Inhibition of Neutrophils (ASTIN) study, initiated in November of the year 2000, is now widely recognized as having been a landmark study in the history of clinical trials. We look at why this is the case by considering its key features and impact. These key features are: the use of Bayesian design and analysis; the use of the normal dynamic linear model; the response adaptive nature of the study; the use of real-time dosing decisions; and the use of an integrated model to predict 90-day response on the Scandinavian Stroke Scale. Our overall conclusion is that the ASTIN study's main impact came from showing the clinical trial community the feasibility of the novel design and analysis used when most of these key features were rarely used in industry trials, let alone used together in one trial in a disease area with a tremendous unmet medical need.


Assuntos
Neutrófilos , Acidente Vascular Cerebral , Teorema de Bayes , Humanos , Modelos Lineares , Acidente Vascular Cerebral/tratamento farmacológico
20.
PLoS Negl Trop Dis ; 16(7): e0010594, 2022 Jul.
Artigo em Inglês | MEDLINE | ID: mdl-35853042

RESUMO

BACKGROUND: Chagas disease is a long-lasting disease with a prolonged asymptomatic period. Cumulative indices of infection such as prevalence do not shed light on the current epidemiological situation, as they integrate infection over long periods. Instead, metrics such as the Force-of-Infection (FoI) provide information about the rate at which susceptible people become infected and permit sharper inference about temporal changes in infection rates. FoI is estimated by fitting (catalytic) models to available age-stratified serological (ground-truth) data. Predictive FoI modelling frameworks are then used to understand spatial and temporal trends indicative of heterogeneity in transmission and changes effected by control interventions. Ideally, these frameworks should be able to propagate uncertainty and handle spatiotemporal issues. METHODOLOGY/PRINCIPAL FINDINGS: We compare three methods in their ability to propagate uncertainty and provide reliable estimates of FoI for Chagas disease in Colombia as a case study: two Machine Learning (ML) methods (Boosted Regression Trees (BRT) and Random Forest (RF)), and a Linear Model (LM) framework that we had developed previously. Our analyses show consistent results between the three modelling methods under scrutiny. The predictors (explanatory variables) selected, as well as the location of the most uncertain FoI values, were coherent across frameworks. RF was faster than BRT and LM, and provided estimates with fewer extreme values when extrapolating to areas where no ground-truth data were available. However, BRT and RF were less efficient at propagating uncertainty. CONCLUSIONS/SIGNIFICANCE: The choice of FoI predictive models will depend on the objectives of the analysis. ML methods will help characterise the mean behaviour of the estimates, while LM will provide insight into the uncertainty surrounding such estimates. Our approach can be extended to the modelling of FoI patterns in other Chagas disease-endemic countries and to other infectious diseases for which serosurveys are regularly conducted for surveillance.


Assuntos
Doença de Chagas , Aprendizado de Máquina , Doença de Chagas/epidemiologia , Colômbia , Humanos , Modelos Lineares , Prevalência
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