scholarly journals A Combination of Generalized Linear Mixed Model and LASSO Methods for Estimating Number of Patients Covid 19 in the Intensive Care Units

CAUCHY ◽  
2021 ◽  
Vol 7 (1) ◽  
pp. 13-21
Author(s):  
Alona Dwinata ◽  
Khairil Anwar Notodiputro ◽  
Bagus Sartono
Keyword(s):  
Linear Mixed Model ◽  
Random Effect ◽  
Vital Signs ◽  
Predictor Variable ◽  
Predictor Variables ◽  
Congenital Disease ◽  
Congenital Diseases ◽  
Blood Tests ◽  
Number Of Patients ◽  
Level Of Significance

Generalized linear mixed models (GLMM) combined with the L1 penalty (Least Absolute Shrinkage and Selection Operator/LASSO) is called LASSO GLMM. LASSO GLMM reduces overfitting and selects predictor variables in modeling. The aim of this study is to evaluate the model's performance for predicting Covid-19 patients with certain congenital disease that require ICU based on the results of blood tests laboratory and patient’s vital signs. This study used binary response variables, 1 if the patient was admitted to the ICU and 0 if the patient was not admitted to the ICU. The fixed effect predictor variables are the results of blood tests laboratory and patient’s vital signs. The random effect predictor variable is patient's congenital disease. The result showed that the average of accuracy and AUC from LASSO GLMM is more than the average of accuracy and AUC from LASSO GLM by using 5% level of significance. Respiratory rate and Lactate show a significance effect to predict the ICU needs of Covid-19 patients. The random effects patient's congenital disease has significance effect at 5% level of significance. It means that the ICU needs for Covid-19 patients varies among patient's congenital disease. We can conclude that GLMM LASSO with the random effect of patient’s congenital diseases has better modeling performance to predict the ICU needs of Covid-19 patients based on the results of blood tests laboratory and patient’s vital signs. The results of this modeling can quickly detect Covid-19 patients who need the ICU and can help medical staff use ICU resources optimally

scholarly journals Analysis of Multidimensional Stunting Intervention Factor Using Mixed Model

2021 ◽  
Vol 948 (1) ◽  
pp. 012067
Author(s):  
D N Agustina ◽  
B Sartono ◽  
K A Notodiputro
Keyword(s):  
Maternal Education ◽  
Social Protection ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Random Effect ◽  
Predictor Variables ◽  
Poor People ◽  
The Government ◽  
Ease Of Access ◽  
Selection Operator

Abstract The mixed model combines fixed effect for all groups and random effect representing the diversity inter groups in the model (province) to increase the model precision. This study provides information on the significance of multidimensional stunting intervention factors (predictor variables) on stunting prevalence (response variables as indicator 2.2.1 Sustainable Development Goals/SDGs) with district/city as observation units. Using official data from Statistics Indonesia (National Socio Economic Survey) and Ministry of Health (Basic Health Research), this study expects to be one basis of information for the government, stakeholders, and further research to accelerate Indonesia’s SDGs targets in 2030. Comparison of classical linear mixed model method and linear mixed model with Least Absolute Shrinkage and Selection Operator (Lasso) variable selection conduct with relatively better results of mixed linear modelling with Lasso. The results showed that the predictor variables, namely complete immunization, ease of access to health facilities, diversity of food intake, improve water, food expenditure per capita, children’s participation in early childhood education, maternal education, and ownership of National Health Insurance for toddlers, significantly affected the stunting prevalence decrease. The predictor variables, namely low birth weight, households with social protection cards, and the percentage of poor people, significantly increase the stunting prevalence.

scholarly journals The generalizability crisis

2020 ◽  
pp. 1-37
Author(s):  
Tal Yarkoni
Keyword(s):  
Quantitative Analysis ◽  
Statistical Inference ◽  
Random Effects ◽  
False Positive ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Random Effect ◽  
Statistical Procedures ◽  
Subject Variability ◽  
Replication Crisis

Abstract Most theories and hypotheses in psychology are verbal in nature, yet their evaluation overwhelmingly relies on inferential statistical procedures. The validity of the move from qualitative to quantitative analysis depends on the verbal and statistical expressions of a hypothesis being closely aligned—that is, that the two must refer to roughly the same set of hypothetical observations. Here I argue that many applications of statistical inference in psychology fail to meet this basic condition. Focusing on the most widely used class of model in psychology—the linear mixed model—I explore the consequences of failing to statistically operationalize verbal hypotheses in a way that respects researchers' actual generalization intentions. I demonstrate that whereas the "random effect" formalism is used pervasively in psychology to model inter-subject variability, few researchers accord the same treatment to other variables they clearly intend to generalize over (e.g., stimuli, tasks, or research sites). The under-specification of random effects imposes far stronger constraints on the generalizability of results than most researchers appreciate. Ignoring these constraints can dramatically inflate false positive rates, and often leads researchers to draw sweeping verbal generalizations that lack a meaningful connection to the statistical quantities they are putatively based on. I argue that failure to take the alignment between verbal and statistical expressions seriously lies at the heart of many of psychology's ongoing problems (e.g., the replication crisis), and conclude with a discussion of several potential avenues for improvement.

scholarly journals The Effect of Dual Language Activation on L2-Induced Changes in L1 Speech within a Code-Switched Paradigm

Languages ◽  
2021 ◽  
Vol 6 (3) ◽  
pp. 114
Author(s):  
Ulrich Reubold ◽  
Sanne Ditewig ◽  
Robert Mayr ◽  
Ineke Mennen
Keyword(s):  
Dual Language ◽  
Native Speakers ◽  
Predictor Variable ◽  
Predictor Variables ◽  
Acoustic Measures ◽  
Before And After ◽  
Language Activation ◽  
Dual Activation ◽  
Induced Changes

The present study sought to examine the effect of dual language activation on L1 speech in late English–Austrian German sequential bilinguals, and to identify relevant predictor variables. To this end, we compared the English speech patterns of adult migrants to Austria in a code-switched and monolingual condition alongside those of monolingual native speakers in England in a monolingual condition. In the code-switched materials, German words containing target segments known to trigger cross-linguistic interaction in the two languages (i.e., [v–w], [ʃt(ʁ)-st(ɹ)] and [l-ɫ]) were inserted into an English frame; monolingual materials comprised English words with the same segments. To examine whether the position of the German item affects L1 speech, the segments occurred either before the switch (“He wants a Wienerschnitzel”) or after (“I like Würstel with mustard”). Critical acoustic measures of these segments revealed no differences between the groups in the monolingual condition, but significant L2-induced shifts in the bilinguals’ L1 speech production in the code-switched condition for some sounds. These were found to occur both before and after a code-switch, and exhibited a fair amount of individual variation. Only the amount of L2 use was found to be a significant predictor variable for shift size in code-switched compared with monolingual utterances, and only for [w]. These results have important implications for the role of dual activation in the speech of late sequential bilinguals.

scholarly journals Response transformations for random effect and variance component models

2020 ◽  
pp. 1471082X2096691
Author(s):  
Amani Almohaimeed ◽  
Jochen Einbeck
Keyword(s):  
Maximum Likelihood ◽  
Random Effects ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Random Effect ◽  
Statistical Technique ◽  
Response Distribution ◽  
Level Data ◽  
Variance Component Models ◽  
Response Transformation

Random effect models have been popularly used as a mainstream statistical technique over several decades; and the same can be said for response transformation models such as the Box–Cox transformation. The latter aims at ensuring that the assumptions of normality and of homoscedasticity of the response distribution are fulfilled, which are essential conditions for inference based on a linear model or a linear mixed model. However, methodology for response transformation and simultaneous inclusion of random effects has been developed and implemented only scarcely, and is so far restricted to Gaussian random effects. We develop such methodology, thereby not requiring parametric assumptions on the distribution of the random effects. This is achieved by extending the ‘Nonparametric Maximum Likelihood’ towards a ‘Nonparametric profile maximum likelihood’ technique, allowing to deal with overdispersion as well as two-level data scenarios.

scholarly journals Bayesian spatial and spatio-temporal approaches to modelling dengue fever: a systematic review

2018 ◽  
Vol 147 ◽  
Author(s):  
A. Aswi ◽  
S. M. Cramb ◽  
P. Moraga ◽  
K. Mengersen
Keyword(s):  
Dengue Fever ◽  
Random Effects ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Assessment Tool ◽  
Temporal Dynamics ◽  
Random Effect ◽  
Temperature And Precipitation ◽  
Generalised Linear Mixed Model ◽  
Spatio Temporal

AbstractDengue fever (DF) is one of the world's most disabling mosquito-borne diseases, with a variety of approaches available to model its spatial and temporal dynamics. This paper aims to identify and compare the different spatial and spatio-temporal Bayesian modelling methods that have been applied to DF and examine influential covariates that have been reportedly associated with the risk of DF. A systematic search was performed in December 2017, using Web of Science, Scopus, ScienceDirect, PubMed, ProQuest and Medline (via Ebscohost) electronic databases. The search was restricted to refereed journal articles published in English from January 2000 to November 2017. Thirty-one articles met the inclusion criteria. Using a modified quality assessment tool, the median quality score across studies was 14/16. The most popular Bayesian statistical approach to dengue modelling was a generalised linear mixed model with spatial random effects described by a conditional autoregressive prior. A limited number of studies included spatio-temporal random effects. Temperature and precipitation were shown to often influence the risk of dengue. Developing spatio-temporal random-effect models, considering other priors, using a dataset that covers an extended time period, and investigating other covariates would help to better understand and control DF transmission.

scholarly journals A comparative study to ascertain the efficacy of intravenous dexmedetomidine versus intravenous tramadol to prevent post-operative shivering after laparoscopic surgery

2019 ◽  
Vol 6 (3) ◽  
pp. 713
Author(s):  
Kishore K. ◽  
Syed Ali Aasim ◽  
Manish Kumar J.
Keyword(s):  
Laparoscopic Surgery ◽  
Side Effects ◽  
Comparative Study ◽  
General Anaesthesia ◽  
Power Number ◽  
Group Iii ◽  
Group I ◽  
Number Of Patients ◽  
Envelope Method ◽  
Level Of Significance

Background: Shivering is commonly encountered both after regional and general anaesthesia (GA) with a little higher incidence in patients receiving GA. The aim of study was to compare the effectiveness of dexmedetomidine and tramadol in decreasing postoperative shivering in patients undergoing laparoscopic surgery.Methods: Total 120 patients were included in this study. In order to get a 5% level of significance and 80% power number of patients required in each group was 40, with a total of 120 patients. Randomization of groups was done based on closed envelope method. Patients were allocated into three groups group I, II and III of 40 patients each. Patients in group I and group II were administered 0.75 μg/kg of dexmedetomidine and 1.5 mg /kg of tramadol in 100 ml NS respectively half a before extubation, while patients in group III did not receive any pharmacological intervention.Results: All three groups were comparable regarding distribution of age, gender, ASA grade and temperature at beginning and end of surgery and were non-significant.Conclusions: Dexmedetomidine seems to possess anti-shivering properties and was found to reduce the occurrence of shivering in patients undergoing general anaesthesia with minimal side effects although its anti-shivering effect was not superior to tramadol.

scholarly journals Factors to Consider in the Use of Vital Signs Wearables to Minimize Contact With Stable COVID-19 Patients: Experience of Its Implementation During the Pandemic

2021 ◽  
Vol 3 ◽  
Author(s):  
Esther Monica Pei Jin Fan ◽  
Shin Yuh Ang ◽  
Ghee Chee Phua ◽  
Lee Chen Ee ◽  
Kok Cheong Wong ◽  
...  
Keyword(s):  
Personal Protective Equipment ◽  
Vital Signs ◽  
Healthcare Facility ◽  
Wearable Device ◽  
Protective Equipment ◽  
Healthcare Industry ◽  
Patient Contact ◽  
Vital Signs Monitoring ◽  
Number Of Patients ◽  
Patients Experience

The COVID-19 pandemic has created a huge burden on the healthcare industry worldwide. Pressures to increase the isolation healthcare facility to cope with the growing number of patients led to an exploration of the use of wearables for vital signs monitoring among stable COVID-19 patients. Vital signs wearables were chosen for use in our facility with the purpose of reducing patient contact and preserving personal protective equipment. The process of deciding on the wearable solution as well as the implementation of the solution brought much insight to the team. This paper presents an overview of factors to consider in implementing a vital signs wearable solution. This includes considerations before deciding on whether or not to use a wearable device, followed by key criteria of the solution to assess. With the use of wearables rising in popularity, this serves as a guide for others who may want to implement it in their institutions.

scholarly journals Machine Learning Models of COVID-19 Cases in the United States: A Study of Initial Lockdown and Reopen Regimes

2021 ◽  
Vol 11 (23) ◽  
pp. 11227
Author(s):  
Arnold Kamis ◽  
Yudan Ding ◽  
Zhenzhen Qu ◽  
Chenchen Zhang
Keyword(s):  
United States ◽  
Machine Learning ◽  
Additive Model ◽  
Regression Tree ◽  
Predictor Variable ◽  
The United States ◽  
Predictor Variables ◽  
Future Research ◽  
Machine Learning Methods ◽  
Variance Explained

The purpose of this paper is to model the cases of COVID-19 in the United States from 13 March 2020 to 31 May 2020. Our novel contribution is that we have obtained highly accurate models focused on two different regimes, lockdown and reopen, modeling each regime separately. The predictor variables include aggregated individual movement as well as state population density, health rank, climate temperature, and political color. We apply a variety of machine learning methods to each regime: Multiple Regression, Ridge Regression, Elastic Net Regression, Generalized Additive Model, Gradient Boosted Machine, Regression Tree, Neural Network, and Random Forest. We discover that Gradient Boosted Machines are the most accurate in both regimes. The best models achieve a variance explained of 95.2% in the lockdown regime and 99.2% in the reopen regime. We describe the influence of the predictor variables as they change from regime to regime. Notably, we identify individual person movement, as tracked by GPS data, to be an important predictor variable. We conclude that government lockdowns are an extremely important de-densification strategy. Implications and questions for future research are discussed.

scholarly journals Goats Given Transdermal Flunixin Meglumine Displayed Less Pain Behavior After Castration

2020 ◽  
Author(s):  
Amanda Lee ◽  
Meggan Graves ◽  
Andrea Lear ◽  
Sherry Cox ◽  
Marc Caldwell ◽  
...  
Keyword(s):  
Fixed Effects ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Body Position ◽  
Random Effect ◽  
Pain Behavior ◽  
Bout Duration ◽  
Flunixin Meglumine ◽  
Meat Goats ◽  
Transdermal Application

AbstractPain management should be utilized with castration to reduce physiological and behavioral changes. Transdermal application of drugs require less animal management and fewer labor risks, which can occur with oral administration or injections. The objective was to determine the effects of transdermal flunixin meglumine on meat goats’ behavior post-castration. Male goats (N = 18; mean body weight ± standard deviation: 26.4 ± 1.6 kg) were housed individually in pens and randomly assigned to 1 of 3 treatments: (1) castrated, dosed with transdermal flunixin meglumine; (2) castrated, dosed with transdermal placebo; and (3) sham castrated, dosed with transdermal flunixin meglumine. Body position, rumination, and head- pressing were observed for 1 h ± 10 minutes twice daily on days −1, 0, 1, 2, and 5 around castration. Each goat was observed once every 5-minutes (scan samples) and reported as percentage of observations. Accelerometers were used to measure standing, lying, and laterality (total time, bouts, and bout duration). A linear mixed model was conducted using GLIMMIX. Fixed effects of treatment, day relative to castration, and treatment*day relative to castration and random effect of date and goat nested within treatment were included. Treatment 1 goats (32.7 ± 2.8%) and treatment 2 goats (32.5 ± 2.8%) ruminated less than treatment 3 goats (47.4 ± 2.8%, P = 0.0012). Head pressing was greater on day of castration in treatment 2 goats (P < 0.001). Standing bout duration was greatest in treatment 2 goats on day 1 post-castration (P < 0.001). Lying bout duration was greatest in treatment 2 goats on day 1 post-castration compared to treatment 1 and treatment 3 goats(P < 0.001). Transdermal flunixin meglumine improved goats’ fluidity of movement post-castration and decreased head pressing, indicating a mitigation of pain behavior.

scholarly journals Susceptibility of pine stands to bark stripping by chacma baboons Papio ursinus in the Eastern Highlands of Zimbabwe

2009 ◽  
Vol 55 (6) ◽  
pp. 389-395 ◽  
Author(s):  
C.A.T. Katsvanga ◽  
L. Jimu ◽  
J.F. Mupangwa ◽  
D. Zinner
Keyword(s):  
Rainy Season ◽  
Predictor Variables ◽  
Bark Stripping ◽  
Conservation Areas ◽  
Papio Ursinus ◽  
Crop Fields ◽  
Pine Trees ◽  
Chacma Baboons ◽  
Level Of Significance ◽  
Eastern Highlands

Abstract The aim of this study was to determine the susceptibility, intensity and distribution of pine trees to bark stripping by chacma baboons Papio ursinus in three plantations in the Eastern Highlands of Zimbabwe. The number of plots/ha, stripped trees/plot and stripped trees/ha was recorded during the pre-rainy, rainy and post-rainy seasons from August 2006 to May 2007. During data collection, altitude, aspect, season and other site predictor variables (e.g., roads and fire traces, water points, indigenous vegetation conservation areas, crop fields, human settlements, wattle scrubs, rocky areas, open grasslands, earlier stripped sites and roost sites) were recorded for each plot in association with selected predictor variables within plantation estates. Data on the number of stripped plots/ha, stripped trees/plot and stripped trees/ha were analysed as dependent variables using the Generalised linear Model (GLM) through SPSS version 15 (2006) to determine which predictor variables were significantly related to bark stripping. Differences between means were tested using Bonferroni tests with a 5% level of significance. Our findings show that bark stripping of pine trees by baboons occurred at all altitudes and aspects. Overall, the number of bark stripped trees/ha did not significantly vary by season. The number of bark stripped plots/ha was lower during the pre-rainy season than the rainy season, whereas the number of bark stripped trees/plot was higher during the pre-rainy than the rainy season. Bark stripping of pines occurred more often in the vicinities of areas with abundant food and water.

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