Influence of Individual Differences in fMRI-Based Pain Prediction Models on Between-Individual Prediction Performance

AbstractReports of poor work well-being and fluctuating productivity in software engineering have been reported in both academic and popular sources. Understanding and predicting these issues through repository analysis might help manage software developers’ well-being. Our objective is to link data from software repositories, that is commit activity, communication, expressed sentiments, and job events, with measures of well-being obtained with a daily experience sampling questionnaire. To achieve our objective, we studied a single software project team for eight months in the software industry. Additionally, we performed semi-structured interviews to explain our results. The acquired quantitative data are analyzed with generalized linear mixed-effects models with autocorrelation structure. We find that individual variance accounts for most of the R2 values in models predicting developers’ experienced well-being and productivity. In other words, using software repository variables to predict developers’ well-being or productivity is challenging due to individual differences. Prediction models developed for each developer individually work better, with fixed effects R2 value of up to 0.24. The semi-structured interviews give insights into the well-being of software developers and the benefits of chat interaction. Our study suggests that individualized prediction models are needed for well-being and productivity prediction in software development.

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New Technologies in Personalized Decision Support to Enhance Patient Choice

Pharmacoinformatics and Drug Discovery Technologies - Advances in Bioinformatics and Biomedical Engineering ◽

10.4018/978-1-4666-0309-7.ch013 ◽

2012 ◽

pp. 220-235 ◽

Cited By ~ 1

Author(s):

Nananda F. Col

Keyword(s):

Risk Factors ◽

Decision Support ◽

Individual Differences ◽

New Technologies ◽

Prediction Models ◽

Decision Aids ◽

Behavioral Risk ◽

Decision Support Tools ◽

Support Tools ◽

Average Patient

Medical decisions are difficult when there are two or more reasonable options and each option has good and bad features that different people may value differently because of differences in health, risk factors, preferences, or values. Personalized decision support tools are being developed in many areas, but two particularly promising areas are patient decision aids and Risk Prediction Models (RPMs). These personalized decision support tools can help patients and/or providers make better decisions about preventing, managing, or treating disease, taking into consideration specific aspects of an individual patient that distinguish them from an ’average’ patient or the population at large. Decision aids tend to focus on individual differences in preferences and values, whereas RPM’s focus on individual differences in clinical, biological, and behavioral risk factors. There are tremendous opportunities with both approaches, and both have been shown to be able to improve clinical judgment and decision making. Decision support tools are needed that provide personalized service that addresses important individual differences in biology, values, and preferences, and that targets the provider-patient dyad.

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Maximum a posteriori Threshold Genomic Prediction Model for Ordinal Traits

G3 Genes|Genome|Genetics ◽

10.1534/g3.120.401733 ◽

2020 ◽

Vol 10 (11) ◽

pp. 4083-4102

Author(s):

Abelardo Montesinos-López ◽

Humberto Gutierrez-Pulido ◽

Osval Antonio Montesinos-López ◽

José Crossa

Keyword(s):

Support Vector Machine ◽

Prediction Model ◽

Ridge Regression ◽

Genomic Prediction ◽

Prediction Models ◽

Prediction Performance ◽

Support Vector ◽

Successful Implementation ◽

A Posteriori ◽

Ordinal Traits

Due to the ever-increasing data collected in genomic breeding programs, there is a need for genomic prediction models that can deal better with big data. For this reason, here we propose a Maximum a posteriori Threshold Genomic Prediction (MAPT) model for ordinal traits that is more efficient than the conventional Bayesian Threshold Genomic Prediction model for ordinal traits. The MAPT performs the predictions of the Threshold Genomic Prediction model by using the maximum a posteriori estimation of the parameters, that is, the values of the parameters that maximize the joint posterior density. We compared the prediction performance of the proposed MAPT to the conventional Bayesian Threshold Genomic Prediction model, the multinomial Ridge regression and support vector machine on 8 real data sets. We found that the proposed MAPT was competitive with regard to the multinomial and support vector machine models in terms of prediction performance, and slightly better than the conventional Bayesian Threshold Genomic Prediction model. With regard to the implementation time, we found that in general the MAPT and the support vector machine were the best, while the slowest was the multinomial Ridge regression model. However, it is important to point out that the successful implementation of the proposed MAPT model depends on the informative priors used to avoid underestimation of variance components.

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Multispectral Models from Bare Soil Composites for Mapping Topsoil Properties over Europe

Remote Sensing ◽

10.3390/rs12091369 ◽

2020 ◽

Vol 12 (9) ◽

pp. 1369 ◽

Cited By ~ 4

Author(s):

José Lucas Safanelli ◽

Sabine Chabrillat ◽

Eyal Ben-Dor ◽

José A. M. Demattê

Keyword(s):

Near Infrared ◽

Prediction Models ◽

Performance Testing ◽

Bare Soil ◽

Prediction Performance ◽

Spatial And Temporal Variation ◽

Gradient Boosting ◽

Landsat Images ◽

Soil Attributes ◽

Sampling Campaign

Reflectance of light across the visible, near-infrared and shortwave infrared (VIS-NIR-SWIR, 0.4–2.5 µm) spectral region is very useful for investigating mineralogical, physical and chemical properties of soils, which can reduce the need for traditional wet chemistry analyses. As many collections of multispectral satellite data are available for environmental studies, a large extent with medium resolution mapping could be benefited from the spectral measurements made from remote sensors. In this paper, we explored the use of bare soil composites generated from the large historical collections of Landsat images for mapping cropland topsoil attributes across the European extent. For this task, we used the Geospatial Soil Sensing System (GEOS3) for generating two bare soil composites of 30 m resolution (named synthetic soil images, SYSI), which were employed to represent the median topsoil reflectance of bare fields. The first (framed SYSI) was made with multitemporal images (2006–2012) framed to the survey time of the Land-Use/Land-Cover Area Frame Survey (LUCAS) soil dataset (2009), seeking to be more compatible to the soil condition upon the sampling campaign. The second (full SYSI) was generated from the full collection of Landsat images (1982–2018), which although displaced to the field survey, yields a higher proportion of bare areas for soil mapping. For evaluating the two SYSIs, we used the laboratory spectral data as a reference of topsoil reflectance to calculate the Spearman correlation coefficient. Furthermore, both SYSIs employed machine learning for calibrating prediction models of clay, sand, soil organic carbon (SOC), calcium carbonates (CaCO3), cation exchange capacity (CEC), and pH determined in water, using the gradient boosting regression algorithm. The original LUCAS laboratory spectra and a version of the data resampled to the Landsat multispectral bands were also used as reference of prediction performance using VIS-NIR-SWIR multispectral data. Our results suggest that generating a bare soil composite displaced to the survey time of soil observations did not improve the quality of topsoil reflectance, and consequently, the prediction performance of soil attributes. Despite the lower spectral resolution and the variability of soils in Europe, a SYSI calculated from the full collection of Landsat images can be employed for topsoil prediction of clay and CaCO3 contents with a moderate performance (testing R2, root mean square error (RMSE) and ratio of performance to interquartile range (RPIQ) of 0.44, 9.59, 1.77, and 0.36, 13.99, 1.54, respectively). Thus, this study shows that although there exist some constraints due to the spatial and temporal variation of soil exposures and among the Landsat sensors, it is possible to use bare soil composites for mapping key soil attributes of croplands across the European extent.

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Striatal structure and function predict individual biases in learning to avoid pain

Proceedings of the National Academy of Sciences ◽

10.1073/pnas.1519829113 ◽

2016 ◽

Vol 113 (17) ◽

pp. 4812-4817 ◽

Cited By ~ 32

Author(s):

Eran Eldar ◽

Tobias U. Hauser ◽

Peter Dayan ◽

Raymond J. Dolan

Keyword(s):

Individual Differences ◽

Prediction Error ◽

Outcome Prediction ◽

Structure And Function ◽

Pain Modulation ◽

Prediction Errors ◽

Learning Biases ◽

Individual Traits ◽

And Function ◽

Pain Prediction

Pain is an elemental inducer of avoidance. Here, we demonstrate that people differ in how they learn to avoid pain, with some individuals refraining from actions that resulted in painful outcomes, whereas others favor actions that helped prevent pain. These individual biases were best explained by differences in learning from outcome prediction errors and were associated with distinct forms of striatal responses to painful outcomes. Specifically, striatal responses to pain were modulated in a manner consistent with an aversive prediction error in individuals who learned predominantly from pain, whereas in individuals who learned predominantly from success in preventing pain, modulation was consistent with an appetitive prediction error. In contrast, striatal responses to success in preventing pain were consistent with an appetitive prediction error in both groups. Furthermore, variation in striatal structure, encompassing the region where pain prediction errors were expressed, predicted participants’ predominant mode of learning, suggesting the observed learning biases may reflect stable individual traits. These results reveal functional and structural neural components underlying individual differences in avoidance learning, which may be important contributors to psychiatric disorders involving pathological harm avoidance behavior.

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Vis-NIR Hyperspectral Imaging for Online Quality Evaluation during Food Processing: A Case Study of Hot Air Drying of Purple-Speckled Cocoyam (Colocasia esculenta (L.) Schott)

Processes ◽

10.3390/pr9101804 ◽

2021 ◽

Vol 9 (10) ◽

pp. 1804

Author(s):

John Ndisya ◽

Ayub Gitau ◽

Duncan Mbuge ◽

Arman Arefi ◽

Liliana Bădulescu ◽

...

Keyword(s):

Hyperspectral Imaging ◽

Prediction Models ◽

Prediction Performance ◽

Good Prediction ◽

Hot Air Drying ◽

Full Spectrum ◽

Air Drying ◽

Hot Air ◽

Good Prediction Performance ◽

Hue Angle

In this study, hyperspectral imaging (HSI) and chemometrics were implemented to develop prediction models for moisture, colour, chemical and structural attributes of purple-speckled cocoyam slices subjected to hot-air drying. Since HSI systems are costly and computationally demanding, the selection of a narrow band of wavelengths can enable the utilisation of simpler multispectral systems. In this study, 19 optimal wavelengths in the spectral range 400–1700 nm were selected using PLS-BETA and PLS-VIP feature selection methods. Prediction models for the studied quality attributes were developed from the 19 wavelengths. Excellent prediction performance (RMSEP < 2.0, r2P > 0.90, RPDP > 3.5) was obtained for MC, RR, VS and aw. Good prediction performance (RMSEP < 8.0, r2P = 0.70–0.90, RPDP > 2.0) was obtained for PC, BI, CIELAB b*, chroma, TFC, TAA and hue angle. Additionally, PPA and WI were also predicted successfully. An assessment of the agreement between predictions from the non-invasive hyperspectral imaging technique and experimental results from the routine laboratory methods established the potential of the HSI technique to replace or be used interchangeably with laboratory measurements. Additionally, a comparison of full-spectrum model results and the reduced models demonstrated the potential replacement of HSI with simpler imaging systems.

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Employee Satisfaction in Online Reviews

Lecture Notes in Computer Science - Social Informatics ◽

10.1007/978-3-030-60975-7_12 ◽

2020 ◽

pp. 152-167

Author(s):

Philipp Koncar ◽

Denis Helic

Keyword(s):

Influencing Factors ◽

Employee Satisfaction ◽

Employment Status ◽

Prediction Models ◽

Online Reviews ◽

Prediction Performance ◽

Accurate Prediction ◽

Individual Factors ◽

Multiple Prediction

Abstract Employee satisfaction impacts the efficiency of businesses as well as the lives of employees spending substantial amounts of their time at work. As such, employee satisfaction attracts a lot of attention from researchers. In particular, a lot of effort has been previously devoted to the question of how to positively influence employee satisfaction, for example, through granting benefits. In this paper, we start by empirically exploring a novel dataset comprising two million online employer reviews. Notably, we focus on the analysis of the influencing factors for employee satisfaction. In addition, we leverage our empirical insights to predict employee satisfaction and to assess the predictive strengths of individual factors. We train multiple prediction models and achieve accurate prediction performance (ROC AUC of best model $$=0.89$$ = 0.89 ). We find that the number of benefits received and employment status of reviewers are most predictive, while employee position has less predictive strengths for employee satisfaction. Our work complements existing studies and sheds light on the influencing factors for employee satisfaction expressed in online employer reviews. Employers may use these insights, for example, to correct for biases when assessing their reviews.

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An Improved Nonequidistant Grey Model Based on Simpson Formula and Its Application

Complexity ◽

10.1155/2021/6654324 ◽

2021 ◽

Vol 2021 ◽

pp. 1-11

Author(s):

Zhiming Hu ◽

Chong Liu

Keyword(s):

Prediction Accuracy ◽

Prediction Models ◽

Prediction Performance ◽

Grey System Theory ◽

Grey Model ◽

Grey Prediction ◽

Grey System ◽

Proposed Model ◽

Grey Models ◽

High Prediction

Grey prediction models have been widely used in various fields of society due to their high prediction accuracy; accordingly, there exists a vast majority of grey models for equidistant sequences; however, limited research is focusing on nonequidistant sequence. The development of nonequidistant grey prediction models is very slow due to their complex modeling mechanism. In order to further expand the grey system theory, a new nonequidistant grey prediction model is established in this paper. To further improve the prediction accuracy of the NEGM (1, 1, t2) model, the background values of the improved nonequidistant grey model are optimized based on Simpson formula, which is abbreviated as INEGM (1, 1, t2). Meanwhile, to verify the validity of the proposed model, this model is applied in two real-world cases in comparison with three other benchmark models, and the modeling results are evaluated through several commonly used indicators. The results of two cases show that the INEGM (1, 1, t2) model has the best prediction performance among these competitive models.

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Time series analysis of hemorrhagic fever with renal syndrome in mainland China by using an XGBoost forecasting model

BMC Infectious Diseases ◽

10.1186/s12879-021-06503-y ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Cai-Xia Lv ◽

Shu-Yi An ◽

Bao-Jun Qiao ◽

Wei Wu

Keyword(s):

Prediction Model ◽

Prediction Models ◽

Arima Model ◽

Mainland China ◽

Hemorrhagic Fever ◽

Prediction Performance ◽

Forecasting Model ◽

Incidence Data ◽

One Step ◽

The One

Abstract Background Hemorrhagic fever with renal syndrome (HFRS) is still attracting public attention because of its outbreak in various cities in China. Predicting future outbreaks or epidemics disease based on past incidence data can help health departments take targeted measures to prevent diseases in advance. In this study, we propose a multistep prediction strategy based on extreme gradient boosting (XGBoost) for HFRS as an extension of the one-step prediction model. Moreover, the fitting and prediction accuracy of the XGBoost model will be compared with the autoregressive integrated moving average (ARIMA) model by different evaluation indicators. Methods We collected HFRS incidence data from 2004 to 2018 of mainland China. The data from 2004 to 2017 were divided into training sets to establish the seasonal ARIMA model and XGBoost model, while the 2018 data were used to test the prediction performance. In the multistep XGBoost forecasting model, one-hot encoding was used to handle seasonal features. Furthermore, a series of evaluation indices were performed to evaluate the accuracy of the multistep forecast XGBoost model. Results There were 200,237 HFRS cases in China from 2004 to 2018. A long-term downward trend and bimodal seasonality were identified in the original time series. According to the minimum corrected akaike information criterion (CAIC) value, the optimal ARIMA (3, 1, 0) × (1, 1, 0)12 model is selected. The index ME, RMSE, MAE, MPE, MAPE, and MASE indices of the XGBoost model were higher than those of the ARIMA model in the fitting part, whereas the RMSE of the XGBoost model was lower. The prediction performance evaluation indicators (MAE, MPE, MAPE, RMSE and MASE) of the one-step prediction and multistep prediction XGBoost model were all notably lower than those of the ARIMA model. Conclusions The multistep XGBoost prediction model showed a much better prediction accuracy and model stability than the multistep ARIMA prediction model. The XGBoost model performed better in predicting complicated and nonlinear data like HFRS. Additionally, Multistep prediction models are more practical than one-step prediction models in forecasting infectious diseases.

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Near-Infrared Technology in Agriculture: Non-Destructive Determination of Inner Quality Parameters in Intact Cocoa Beans

Trends in Sciences ◽

10.48048/tis.2021.31 ◽

2021 ◽

Vol 18 (20) ◽

pp. 31

Author(s):

Zulfahrizal Zulfahrizal ◽

Agus Arip Munawar

Keyword(s):

Prediction Models ◽

Quality Attributes ◽

Quality Parameters ◽

Fat Content ◽

Prediction Performance ◽

Partial Least Square ◽

Least Square ◽

Partial Least Square Regression ◽

Cocoa Beans ◽

Orthogonal Signal Correction

This present study aimed to apply the near-infrared technology based on reflectance spectroscopy or NIRS in determining 2 main quality attributes on intact cocoa beans namely fat content (FC) and moisture content (MC). Absorbance spectral data, in a wavelength range from 1000 to 2500 nm were acquired and recorded for a total of 110 bulk cocoa bean samples. Meanwhile, actual reference FC and MC were obtained using standard laboratory approaches and Soxhlet and Gravimetry methods. Samples were split onto calibration and validation datasets. The prediction models, used to determine both quality attributes were developed from the calibration dataset using 2 regression methods: Principal component regression (PCR) and partial least square regression (PLSR). To obtain more accurate and robust prediction performance, 4 different spectra correction methods namely baseline shift correction (BSC), mean normalization (MN), standard normal variate (SNV), and orthogonal signal correction (OSC) were employed. The results showed that PLSR was better than PCR for both quality parameters prediction. Moreover, spectra corrections enhanced the prediction accuracy and robustness from which OSC was found to be the best correction method for FC and MC determination. The prediction performance using validation dataset generated a correlation coefficient (r), ratio prediction to deviation (RPD), and ratio error to range (RER) indexes for FC were 0.93, 3.16 and 7.12, while for MC prediction, the r coefficient, RPD and RER indexes were 0.96, 3.43 and 9.25, respectively. Based on obtained results, it may conclude that NIRS combined with proper spectra correction and regression approaches can be used to determine inner quality attributes of intact cocoa beans rapidly and simultaneously. HIGHLIGHTS We study and apply NIRS technology as a fast and novel method to predict inner quality parameters of intact cocoa beans in form of moisture and fat content Prediction models, used to determine both quality attributes were developed using 2 regression methods: Principal component regression (PCR) and partial least square regression (PLSR) To obtain more accurate and robust prediction performance, 4 different spectra correction methods namely baseline shift correction (BSC), mean normalization (MN), standard normal variate (SNV), and orthogonal signal correction (OSC) The best prediction performance was obtained when the models are constructed using PLSR in combination with OSC correction approach The maximum correlation coefficient (r) and ratio prediction to deviation (RPD) indexes for Fat content were 0.93 and 3.16, while for moisture content prediction, the r coefficient and RPD indexes were 0.96 and 3.43, respectively GRAPHICAL ABSTRACT

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