Recovery after stroke: the severely impaired are a distinct group

2021 ◽  
pp. jnnp-2021-327211
Author(s):  
Anna K Bonkhoff ◽  
Tom Hope ◽  
Danilo Bzdok ◽  
Adrian G Guggisberg ◽  
Rachel L Hawe ◽  
...  
Keyword(s):  
Distinct Group ◽  
Recovery Process ◽  
Prediction Performance ◽  
Stroke Recovery ◽  
Bayesian Hierarchical ◽  
Statistical Framework ◽  
Process Framework ◽  
Out Of Sample ◽  
Out Of Sample Prediction ◽  
Different Levels

IntroductionStroke causes different levels of impairment and the degree of recovery varies greatly between patients. The majority of recovery studies are biased towards patients with mild-to-moderate impairments, challenging a unified recovery process framework. Our aim was to develop a statistical framework to analyse recovery patterns in patients with severe and non-severe initial impairment and concurrently investigate whether they recovered differently.MethodsWe designed a Bayesian hierarchical model to estimate 3–6 months upper limb Fugl-Meyer (FM) scores after stroke. When focusing on the explanation of recovery patterns, we addressed confounds affecting previous recovery studies and considered patients with FM-initial scores <45 only. We systematically explored different FM-breakpoints between severe/non-severe patients (FM-initial=5–30). In model comparisons, we evaluated whether impairment-level-specific recovery patterns indeed existed. Finally, we estimated the out-of-sample prediction performance for patients across the entire initial impairment range.ResultsRecovery data was assembled from eight patient cohorts (n=489). Data were best modelled by incorporating two subgroups (breakpoint: FM-initial=10). Both subgroups recovered a comparable constant amount, but with different proportional components: severely affected patients recovered more the smaller their impairment, while non-severely affected patients recovered more the larger their initial impairment. Prediction of 3–6 months outcomes could be done with an R2=63.5% (95% CI=51.4% to 75.5%).ConclusionsOur work highlights the benefit of simultaneously modelling recovery of severely-to-non-severely impaired patients and demonstrates both shared and distinct recovery patterns. Our findings provide evidence that the severe/non-severe subdivision in recovery modelling is not an artefact of previous confounds. The presented out-of-sample prediction performance may serve as benchmark to evaluate promising biomarkers of stroke recovery.

scholarly journals Spatial Panel Models of Crop Yield Response to Weather: Econometric Specification Strategies and Prediction Performance

2021 ◽  
pp. 1-19
Author(s):  
Seong D. Yun ◽  
Benjamin M. Gramig
Keyword(s):  
Crop Yield ◽  
Fixed Effects ◽  
Prediction Performance ◽  
Model Specification ◽  
Yield Response ◽  
Fixed Effects Model ◽  
Spatial Econometric ◽  
Site Characteristics ◽  
Out Of Sample ◽  
Out Of Sample Prediction

Abstract This study scrutinizes spatial econometric models and specifications of crop yield response functions to provide a robust evaluation of empirical alternatives available to researchers. We specify 14 competing panel regression models of crop yield response to weather and site characteristics. Using county corn yields in the US, this study implements in-sample, out-of-sample, and bootstrapped out-of-sample prediction performance comparisons. Descriptive propositions and empirical results demonstrate the importance of spatial correlation and empirically support the fixed effects model with spatially dependent error structures. This study also emphasizes the importance of extensive model specification testing and evaluation of selection criteria for prediction.

scholarly journals WOMBAT v1.0: a fully Bayesian global flux-inversion framework

2022 ◽  
Vol 15 (1) ◽  
pp. 45-73
Author(s):  
Andrew Zammit-Mangion ◽  
Michael Bertolacci ◽  
Jenny Fisher ◽  
Ann Stavert ◽  
Matthew Rigby ◽  
...  
Keyword(s):  
Transport Model ◽  
Atmospheric Transport ◽  
Trace Gases ◽  
Remotely Sensed Data ◽  
Bayesian Hierarchical ◽  
Statistical Framework ◽  
Correlated Error ◽  
Out Of Sample ◽  
Fully Bayesian ◽  
Observing System Simulation Experiment

Abstract. WOMBAT (the WOllongong Methodology for Bayesian Assimilation of Trace-gases) is a fully Bayesian hierarchical statistical framework for flux inversion of trace gases from flask, in situ, and remotely sensed data. WOMBAT extends the conventional Bayesian synthesis framework through the consideration of a correlated error term, the capacity for online bias correction, and the provision of uncertainty quantification on all unknowns that appear in the Bayesian statistical model. We show, in an observing system simulation experiment (OSSE), that these extensions are crucial when the data are indeed biased and have errors that are spatio-temporally correlated. Using the GEOS-Chem atmospheric transport model, we show that WOMBAT is able to obtain posterior means and variances on non-fossil-fuel CO2 fluxes from Orbiting Carbon Observatory-2 (OCO-2) data that are comparable to those from the Model Intercomparison Project (MIP) reported in Crowell et al. (2019). We also find that WOMBAT's predictions of out-of-sample retrievals obtained from the Total Column Carbon Observing Network (TCCON) are, for the most part, more accurate than those made by the MIP participants.

Machine Learning Prediction of Smoking Risk from Repeated Sampling of Environmental Images (Preprint)

2021 ◽  
Author(s):  
Matthew M Engelhard ◽  
Joshua D'Arcy ◽  
Jason A Oliver ◽  
Rachel Kozink ◽  
F Joseph McClernon
Keyword(s):  
Risk Model ◽  
Characteristic Curve ◽  
Image Features ◽  
Prediction Performance ◽  
Smoking Habits ◽  
Daily Smoking ◽  
Habitual Smoking ◽  
Out Of Sample ◽  
Repeated Sampling ◽  
Out Of Sample Prediction

BACKGROUND Viewing their habitual smoking environments increases smokers’ craving and smoking behaviors in laboratory settings. A deep learning approach can differentiate between habitual smoking versus nonsmoking environments, but its ability to predict smoking risk associated with the broader range of environments smokers encounter in their daily lives is unknown. OBJECTIVE To predict environment-associated smoking risk from continuously acquired images of smokers’ daily environments. METHODS Smokers from the Durham, NC area completed ecological momentary assessments both immediately smoking and at randomly selected times throughout the day, for two weeks. At each assessment, participants took a picture of their current environment and completed a questionnaire on smoking, craving, and the environmental setting. A convolutional neural network (CNN)-based model was trained to predict smoking, craving, whether smoking was allowed, and whether the participant was outside based on images of participants’ daily environments, the time since their last cigarette, and baseline data on daily smoking habits. Prediction performance, quantified using the area under the receiver operating characteristic curve (AUC) and average precision (AP), was assessed for (a) out-of-sample prediction, and (b) personalized models trained on images from days 1-10. Models were optimized for mobile devices and implemented as a smartphone app. RESULTS Forty-eight participants completed the study, and 8008 images were acquired. The personalized models were highly effective in predicting smoking risk (AUC=0.827; AP=0.882), craving (AUC=0.837; AP=0.798), whether smoking was allowed in the current environment (AUC=0.932, AP=0.981), and whether the participant was outside (AUC=0.977, AP=0.956). The out-of-sample models were also effective in predicting smoking risk (AUC=0.723, AP=0.785), whether smoking was allowed in the current environment (AUC=0.815, AP=0.937), and whether the participant was outside (AUC=0.949, AP=0.922); but were not effective in predicting craving (AUC=0.522, AP=0.427). Omitting image features reduced performance (p<0.05) when predicting all outcomes except craving (p>0.05). Smoking prediction was more effective in participants whose self-reported location type was more variable (Spearman’s ρ=0.48, p=0.001). CONCLUSIONS Images of daily environments can be used to effectively predict smoking risk. Model personalization, achieved by incorporating information about daily smoking habits and training on participant-specific images, further improves prediction performance. Environment-associated smoking risk can be assessed in real time on a mobile device, and could be incorporated in device-based smoking cessation interventions.

Predicting the trend of stock index based on feature engineering and CatBoost model

2021 ◽  
pp. 2150027
Author(s):  
Renzhe Xu ◽  
Yudong Chen ◽  
Tenglong Xiao ◽  
Jingli Wang ◽  
Xiong Wang
Keyword(s):  
Time Scale ◽  
Stock Index ◽  
Feature Engineering ◽  
Multiple Time ◽  
Multiple Time Scale ◽  
Back Testing ◽  
Price Series ◽  
Out Of Sample ◽  
Out Of Sample Prediction ◽  
Sharp Ratio

As an important tool to measure the current situation of the whole stock market, the stock index has always been the focus of researchers, especially for its prediction. This paper uses trend types, which are received by clustering price series under multiple time scale, combined with the day-of-the-week effect to construct a categorical feature combination. Based on the historical data of six kinds of Chinese stock indexes, the CatBoost model is used for training and predicting. Experimental results show that the out-of-sample prediction accuracy is 0.55, and the long–short trading strategy can obtain average annualized return of 34.43%, which is a great improvement compared with other classical classification algorithms. Under the rolling back-testing, the model can always obtain stable returns in each period of time from 2012 to 2020. Among them, the SSESC’s long–short strategy has the best performance with an annualized return of 40.85% and a sharp ratio of 1.53. Therefore, the trend information on multiple time-scale features based on feature engineering can be learned by the CatBoost model well, which has a guiding effect on predicting stock index trends.

scholarly journals Enhancing Recovery after Stroke with Noradrenergic Pharmacotherapy: A New Frontier?

2000 ◽  
Vol 27 (2) ◽  
pp. 97-105 ◽  
Author(s):  
David J. Gladstone ◽  
Sandra E. Black
Keyword(s):  
Animal Studies ◽  
Recovery Of Function ◽  
Recovery Process ◽  
Stroke Recovery ◽  
Therapeutic Interventions ◽  
New Frontier ◽  
Focal Brain Injury ◽  
Completed Stroke ◽  
Scientific Attention ◽  
Irreversible Injury

ABSTRACT:Despite much progress in stroke prevention and acute intervention, recovery and rehabilitation have traditionally received relatively little scientific attention. There is now increasing interest in the development of stroke recovery drugs and innovative rehabilitation techniques to promote functional recovery after completed stroke. Experimental work over the past two decades indicates that pharmacologic intervention to enhance recovery may be possible in the subacute stage, days to weeks poststroke, after irreversible injury has occurred. This paper discusses the concept of “rehabilitation pharmacology” and reviews the growing literature from animal studies and pilot clinical trials on noradrenergic pharmacotherapy, a new experimental strategy in stroke rehabilitation. Amphetamine, a monoamine agonist that increases brain norepinephrine levels, is the most extensively studied drug shown to promote recovery of function in animal models of focal brain injury. Further research is needed to investigate the mechanisms and clinical efficacy of amphetamine and other novel therapeutic interventions on the recovery process.

First-stroke recovery process: The role of family social support

2000 ◽  
Vol 81 (7) ◽  
pp. 881-887 ◽  
Author(s):  
Evie Tsouna-Hadjis ◽  
Kostas N. Vemmos ◽  
Nikolaos Zakopoulos ◽  
Stamatis Stamatelopoulos
Keyword(s):  
Social Support ◽  
Recovery Process ◽  
Stroke Recovery ◽  
Family Social Support

Customizing persuasive messages; the value of operative measures

2018 ◽  
Vol 35 (2) ◽  
pp. 208-217 ◽  
Author(s):  
Maurits Kaptein
Keyword(s):  
Interactive Media ◽  
Database Systems ◽  
Customer Behavior ◽  
Customer Relationship ◽  
Persuasive Messages ◽  
Psychological Traits ◽  
Content Type ◽  
Out Of Sample ◽  
Out Of Sample Prediction ◽  
Operative Measures

Purpose This paper aims to examine whether estimates of psychological traits obtained using meta-judgmental measures (as commonly present in customer relationship management database systems) or operative measures are most useful in predicting customer behavior. Design/methodology/approach Using an online experiment (N = 283), the study collects meta-judgmental and operative measures of customers. Subsequently, it compares the out-of-sample prediction error of responses to persuasive messages. Findings The study shows that operative measures – derived directly from measures of customer behavior – are more informative than meta-judgmental measures. Practical implications Using interactive media, it is possible to actively elicit operative measures. This study shows that practitioners seeking to customize their marketing communication should focus on obtaining such psychographic observations. Originality/value While currently both meta-judgmental measures and operative measures are used for customization in interactive marketing, this study directly compares their utility for the prediction of future responses to persuasive messages.

scholarly journals Network controllability in transmodal cortex predicts psychosis spectrum symptoms

2020 ◽  
Author(s):  
Linden Parkes ◽  
Tyler M. Moore ◽  
Monica E. Calkins ◽  
Matthew Cieslak ◽  
David R. Roalf ◽  
...  
Keyword(s):  
Control Theory ◽  
Structural Connectivity ◽  
Prediction Performance ◽  
Network Control ◽  
Association Cortex ◽  
Out Of Sample ◽  
Sample Testing ◽  
Functional Hierarchy ◽  
Structural Connections ◽  
Network Controllability

ABSTRACTBackgroundThe psychosis spectrum is associated with structural dysconnectivity concentrated in transmodal association cortex. However, understanding of this pathophysiology has been limited by an exclusive focus on the direct connections to a region. Using Network Control Theory, we measured variation in both direct and indirect structural connections to a region to gain new insights into the pathophysiology of the psychosis spectrum.MethodsWe used psychosis symptom data and structural connectivity in 1,068 youths aged 8 to 22 years from the Philadelphia Neurodevelopmental Cohort. Applying a Network Control Theory metric called average controllability, we estimated each brain region’s capacity to leverage its direct and indirect structural connections to control linear brain dynamics. Next, using non-linear regression, we determined the accuracy with which average controllability could predict negative and positive psychosis spectrum symptoms in out-of-sample testing. We also compared prediction performance for average controllability versus strength, which indexes only direct connections to a region. Finally, we assessed how the prediction performance for psychosis spectrum symptoms varied over the functional hierarchy spanning unimodal to transmodal cortex.ResultsAverage controllability outperformed strength at predicting positive psychosis spectrum symptoms, demonstrating that indexing indirect structural connections to a region improved prediction performance. Critically, improved prediction was concentrated in association cortex for average controllability, whereas prediction performance for strength was uniform across the cortex, suggesting that indexing indirect connections is crucial in association cortex.ConclusionsExamining inter-individual variation in direct and indirect structural connections to association cortex is crucial for accurate prediction of positive psychosis spectrum symptoms.

Variance and covariance heterogeneity analysis for detection of metabolites associated with cadmium exposure

2014 ◽  
Vol 13 (2) ◽  
Author(s):  
Beatriz Valcarcel Salamanca ◽  
Timothy M.D. Ebbels ◽  
Maria De Iorio
Keyword(s):  
Hierarchical Models ◽  
Urinary Metabolites ◽  
Potential Effect ◽  
Environmental Pollutant ◽  
Cadmium Exposure ◽  
Urinary Cadmium ◽  
Bayesian Hierarchical ◽  
Metabolic Traits ◽  
Statistical Framework ◽  
Heterogeneity Analysis

AbstractIn this study, we propose a novel statistical framework for detecting progressive changes in molecular traits as response to a pathogenic stimulus. In particular, we propose to employ Bayesian hierarchical models to analyse changes in mean level, variance and correlation of metabolic traits in relation to covariates. To illustrate our approach we investigate changes in urinary metabolic traits in response to cadmium exposure, a toxic environmental pollutant. With the application of the proposed approach, previously unreported variations in the metabolism of urinary metabolites in relation to urinary cadmium were identified. Our analysis highlights the potential effect of urinary cadmium on the variance and correlation of a number of metabolites involved in the metabolism of choline as well as changes in urinary alanine. The results illustrate the potential of the proposed approach to investigate the gradual effect of pathogenic stimulus in molecular traits.

Microstructure in the Machine Age

2020 ◽  
Author(s):  
David Easley ◽  
Marcos López de Prado ◽  
Maureen O’Hara ◽  
Zhibai Zhang
Keyword(s):  
Machine Learning ◽  
Market Efficiency ◽  
Predictive Power ◽  
Explanatory Power ◽  
Futures Contracts ◽  
Machine Age ◽  
Price Process ◽  
Out Of Sample ◽  
Asset Classes ◽  
Out Of Sample Prediction

Abstract Understanding modern market microstructure phenomena requires large amounts of data and advanced mathematical tools. We demonstrate how machine learning can be applied to microstructural research. We find that microstructure measures continue to provide insights into the price process in current complex markets. Some microstructure features with high explanatory power exhibit low predictive power, while others with less explanatory power have more predictive power. We find that some microstructure-based measures are useful for out-of-sample prediction of various market statistics, leading to questions about market efficiency. We also show how microstructure measures can have important cross-asset effects. Our results are derived using 87 liquid futures contracts across all asset classes.

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