Best Combination Of Predictors Research Articles

Construction and evaluation of pedotransfer functions for saturated hydraulic conductivity in the granite red soil regions of southern China

Study regionSix typical regions of granite red soil in southern China. Study focusRegional large-scale Ks analysis of granite red soil in South China is still limited to a large number of repetitive and expensive workloads. It is necessary to construct Ks PTFs suitable for the granite red soil region of South China. In this study, six different regions granite red soil in South China were used to validate the prediction of classical PTFs for Ks, the best combination of predictors was screened and PTFs developed based on stepwise regression methods and machine learning algorithms. New hydrologic insights for the region(1) The coefficient of variation for Ks is 69.87% in the granite red soil region of South China, with a large spatial variability. (2) The six typical PTFs of Ks were poor predictors of Ks for soils in southern China, R2 < 0.4. (3) "Clay+Silt+Sand+BD+SOM+NCP" is the best combination of predictors of soil Ks in the granite red soil region of South China, which can be used in different machine learning algorithms. (4) The constructed MLR model “Ks= 1.501 + 0.043 ×NCP-0.995 ×BD+ 0.034 ×SOM-0.013 ×Clay” with KNN, ANN and BRT machine algorithm models have a prediction accuracy R2 greater than 0.8. This study provides different prediction models for soil Ks in the granitic regions of southern China and lays a theoretical basis for hydrological research in the these regions.

ORIENTATE: automated machine learning classifiers for oral health prediction and research

BackgroundThe application of data-driven methods is expected to play an increasingly important role in healthcare. However, a lack of personnel with the necessary skills to develop these models and interpret its output is preventing a wider adoption of these methods. To address this gap, we introduce and describe ORIENTATE, a software for automated application of machine learning classification algorithms by clinical practitioners lacking specific technical skills. ORIENTATE allows the selection of features and the target variable, then automatically generates a number of classification models and cross-validates them, finding the best model and evaluating it. It also implements a custom feature selection algorithm for systematic searches of the best combination of predictors for a given target variable. Finally, it outputs a comprehensive report with graphs that facilitates the explanation of the classification model results, using global interpretation methods, and an interface for the prediction of new input samples. Feature relevance and interaction plots provided by ORIENTATE allow to use it for statistical inference, which can replace and/or complement classical statistical studies.ResultsIts application to a dataset with healthy and special health care needs (SHCN) children, treated under deep sedation, was discussed as case study. On the example dataset, despite its small size, the feature selection algorithm found a set of features able to predict the need for a second sedation with a f1 score of 0.83 and a ROC (AUC) of 0.92. Eight predictive factors for both populations were found and ordered by the relevance assigned to them by the model. A discussion of how to derive inferences from the relevance and interaction plots and a comparison with a classical study is also provided.ConclusionsORIENTATE automatically finds suitable features and generates accurate classifiers which can be used in preventive tasks. In addition, researchers without specific skills on data methods can use it for the application of machine learning classification and as a complement to classical studies for inferential analysis of features. In the case study, a high prediction accuracy for a second sedation in SHCN children was achieved. The analysis of the relevance of the features showed that the number of teeth with pulpar treatments at the first sedation is a predictive factor for a second sedation.

Multivariate analysis based on the maximum standard unit value of 18F-fluorodeoxyglucose positron emission tomography/computed tomography and computed tomography features for preoperative predicting of visceral pleural invasion in patients with subpleural clinical stage IA peripheral lung adenocarcinoma

Preoperative prediction of visceral pleural invasion (VPI) is important because it enables thoracic surgeons to choose appropriate surgical plans. This study aimed to develop and validate a multivariate logistic regression model incorporating the maximum standardized uptake value (SUVmax) and valuable computed tomography (CT) signs for the non-invasive prediction of VPI status in subpleural clinical stage IA lung adenocarcinoma patients before surgery. A total of 140 patients with subpleural clinical stage IA peripheral lung adenocarcinoma were recruited and divided into a training set (n = 98) and a validation set (n = 42), according to the positron emission tomography/CT examination temporal sequence, with a 7:3 ratio. Next, VPI-positive and VPI-negative groups were formed based on the pathological results. In the training set, the clinical information, the SUVmax, the relationship between the tumor and the pleura, and the CT features were analyzed using univariate analysis. The variables with significant differences were included in the multivariate analysis to construct a prediction model. A nomogram based on multivariate analysis was developed, and its predictive performance was verified in the validation set. The size of the solid component, the consolidation-to-tumor ratio, the solid component pleural contact length, the SUVmax, the density type, the pleural indentation, the spiculation, and the vascular convergence sign demonstrated significant differences between VPI-positive (n = 40) and VPI-negative (n = 58) cases on univariate analysis in the training set. A multivariate logistic regression model incorporated the SUVmax [odds ratio (OR): 1.753, P = 0.002], the solid component pleural contact length (OR: 1.101, P = 0.034), the pleural indentation (OR: 5.075, P = 0.041), and the vascular convergence sign (OR: 13.324, P = 0.025) as the best combination of predictors, which were all independent risk factors for VPI in the training group. The nomogram indicated promising discrimination, with an area under the curve value of 0.892 [95% confidence interval (CI), 0.813-0.946] in the training set and 0.885 (95% CI, 0.748-0.962) in the validation set. The calibration curve demonstrated that its predicted probabilities were in acceptable agreement with the actual probability. The decision curve analysis illustrated that the current nomogram would add more net benefit. The nomogram integrating the SUVmax and the CT features could non-invasively predict VPI status before surgery in subpleural clinical stage IA lung adenocarcinoma patients.

A locally weighted linear ridge regression framework for spatial interpolation of monthly precipitation over an orographically complex area

AbstractThis article aimed to investigate the potential of using three large‐scale precipitation products (PPs), including TRMM, ERA5‐Land, and MSWEP, and two land surface characteristics, including NDVI and Land Surface Temperature (LST), to improve the accuracy of an elevation‐based spatial non‐stationary, Locally Weighted Linear Regression (LWLR) method. A two‐step approach consisting of downscaling and merging was proposed and assessed across an orographically complex region in Iran to construct monthly precipitation maps for the 2001–2015 period. In the first step, three large‐scale PPs were downscaled to 1 km spatial resolution via the Area To Point Kriging (ATPK) method to address the spatial resolution discrepancy between the coarse pixels of PPs and pointwise gauge data. In the second step, five regression models with different combinations of predictors were developed and compared with the benchmark precipitation‐elevation regression model. The L2 regularization method was adopted to overcome the potential multicollinearity issue. The proposed framework could successfully diagnose the multicollinearity issues and dynamically estimate the regularization parameter in space and time. We utilized the holdout method to validate and compare the developed models, in which 50% of gauges were used for fitting the regression functions, and the remaining gauges were assigned to the validation dataset. The validation results showed that the overall monthly accuracy of the benchmark univariate elevation‐based model was improved where the MAE metric was decreased by 16.5%, and CC and KGE were increased by 3.4 and 5.8%, respectively. Also, the rBias was enhanced from −5.72 to 1.24%. Mean monthly precipitation maps of the 2001–2015 period generated by the model with the best combination of predictors and the benchmark model displayed the same spatial pattern consistent with the topography. Further analysis under different validation scenarios revealed that the contribution of PPs and auxiliary variables is the greatest when the training gauge network is sparse. Also, the developed multivariate model has a higher extrapolation ability and is more robust than the benchmark model.

Classification of Alzheimer's disease and frontotemporal dementia using routine clinical and cognitive measures across multicentric underrepresented samples: A cross sectional observational study.

Global brain health initiatives call for improving methods for the diagnosis of Alzheimer's disease (AD) and frontotemporal dementia (FTD) in underrepresented populations. However, diagnostic procedures in upper-middle-income countries (UMICs) and lower-middle income countries (LMICs), such as Latin American countries (LAC), face multiple challenges. These include the heterogeneity in diagnostic methods, lack of clinical harmonisation, and limited access to biomarkers. This cross-sectional observational study aimed to identify the best combination of predictors to discriminate between AD and FTD using demographic, clinical and cognitive data among 1794 participants [904 diagnosed with AD, 282 diagnosed with FTD, and 606 healthy controls (HCs)] collected in 11 clinical centres across five LAC (ReDLat cohort). A fully automated computational approach included classical statistical methods, support vector machine procedures, and machine learning techniques (random forest and sequential feature selection procedures). Results demonstrated an accurate classification of patients with AD and FTD and HCs. A machine learning model produced the best values to differentiate AD from FTD patients with an accuracy = 0.91. The top features included social cognition, neuropsychiatric symptoms, executive functioning performance, and cognitive screening; with secondary contributions from age, educational attainment, and sex. Results demonstrate that data-driven techniques applied in archival clinical datasets could enhance diagnostic procedures in regions with limited resources. These results also suggest specific fine-grained cognitive and behavioural measures may aid in the diagnosis of AD and FTD in LAC. Moreover, our results highlight an opportunity for harmonisation of clinical tools for dementia diagnosis in the region. This work was supported by the Multi-Partner Consortium to Expand Dementia Research in Latin America (ReDLat), funded by NIA/NIH (R01AG057234), Alzheimer's Association (SG-20-725707-ReDLat), Rainwater Foundation, Takeda (CW2680521), Global Brain Health Institute; as well as CONICET; FONCYT-PICT (2017-1818, 2017-1820); PIIECC, Facultad de Humanidades, Usach; Sistema General de Regalías de Colombia (BPIN2018000100059), Universidad del Valle (CI 5316); ANID/FONDECYT Regular (1210195, 1210176, 1210176); ANID/FONDAP (15150012); ANID/PIA/ANILLOS ACT210096; and Alzheimer's Association GBHI ALZ UK-22-865742.

Spatial Interpolation of Gravimetric Soil Moisture Using EM38-mk Induction and Ensemble Machine Learning (Case Study from Dry Steppe Zone in Volgograd Region).

The implementation of the sustainable management of the interaction between agriculture and the environment requires an increasingly deep understanding and numerical description of the soil genesis and properties of soils. One of the areas of application of relevant knowledge is digital irrigated agriculture. During the development of such technologies, the traditional methods of soil research can be quite expensive and time consuming. Proximal soil sensing in combination with predictive soil mapping can significantly reduce the complexity of the work. In this study, we used topographic variables and data from the Electromagnetic Induction Meter (EM38-mk) in combination with soil surface hydrological variables to produce cartographic models of the gravimetric soil moisture for a number of depth intervals. For this purpose, in dry steppe zone conditions, a test site was organized. It was located at the border of the parcel containing the irrigated soybean crop, where 50 soil samples were taken at different points alongside electrical conductivity data (ECa) measured in situ in the field. The modeling of the gravimetric soil moisture was carried out with the stepwise inclusion of independent variables, using methods of ensemble machine learning and spatial cross-validation. The obtained cartographic models showed satisfactory results with the best performance R2cv 0.59–0.64. The best combination of predictors that provided the best results of the model characteristics for predicting gravimetric soil moisture were geographical variables (buffer zone distances) in combination with the initial variables converted into the principal components. The cartographic models of the gravimetric soil moisture variability obtained this way can be used to solve the problems of managed irrigated agriculture, applying fertilizers at variable rates, thereby optimizing the use of resources by crop producers, which can ultimately contribute to the sustainable management of natural resources.

A Prediction-Oriented Specification Search Algorithm for Generalized Structured Component Analysis

Generalized structured component analysis (GSCA) is used for specifying and testing the relationships between observed variables and components. GSCA can perform model selection by comparing theoretically established models. In practice, however, theories may not always completely and unambiguously specify the relationships between variables in the model. In such situations, a specification search strategy allows for exploring potential relationships between variables in a data-driven manner. A specification search based on prediction of unseen observations is attractive as it does not require the provision of theoretically plausible models. To date, GSCA has not been equipped with such a specification search strategy. Addressing this limitation, we propose a prediction-oriented specification search algorithm for GSCA, which reveals the best combination of predictors that minimizes each target variable’s prediction error. We conduct a simulation study to examine the new algorithm’s performance and apply it to real data to further investigate and demonstrate its practical usefulness.

Correlation approach in predictor selection for groundwater level forecasting in areas threatened by water deficits

Abstract Reliable long-term groundwater level (GWL) prediction is essential to assess the availability of resources and the risk to drinking water supply in changing climatic and socio-economic conditions, especially in areas with water deficits. The modern approach in this area involves the use of machine learning methods. However, the greatest challenge in these methods lies in the optimization of input selection. The presented research concerns the selection of the best combination of predictors using the Hellwig method. It served as a preprocessing technique before GWL prediction using support vector regression (SVR) and multilayer perceptron (MLP) for three wells in the Greater Poland Province, where the largest water deficits occur, in the period 1975–2014. The results of this method were compared with those of the regression method, general regression model. For the case study under investigation, the Hellwig method provided the best set of predictors consisted of GWL at lags of −1 and −2 months, precipitation from the current month, and delayed from −1 to −6 months, and past temperature at −1, −3, −4 and −6 months. Such input led to a model accuracy of 0.003–0.022 for a mean squared error and r2 of &amp;gt;0.8. The results obtained with SVR were slightly better than those with MLP. Moreover, every well required an individual set of predictors, and additional meteorological inputs improved the models’ performance.

“Beyond liking” measures in food-related consumer research supplement hedonic responses and improve ability to predict consumption

Sensory and consumer science is concerned with measuring perceptual and affective responses to consumer products. Historically, hedonic responses (degree of liking or preference for a set of test products) have been the primary measure of product performance in food-related consumer research, but recent years have seen an increase in the uptake of perceptual measures that go “beyond liking”, with interest primarily focusing on product-elicited emotions, conceptualisations and situational appropriateness. Although the ultimate purpose of collecting such responses is that they are predictive of what consumers will like, choose and consume in their everyday life, such data are very rarely validated against actual consumer behaviour.Against this backdrop, the present research aimed to evaluate the ability of emotional, conceptual, and situational appropriateness responses to predict a behaviourally relevant measure of product performance – frequency of past consumption. Two (online) consumer studies were conducted with US adults, using salads (Study 1, n = 606) and non-alcoholic beverages (Study 2, n = 603) as product categories. In each study, the predictive ability of each set of measures was benchmarked against that of expected liking to identify the optimal (most predictive of consumption) combination of product-related measures.Both studies provided evidence that all included measures (liking, emotional, conceptual, and situational responses) were significantly correlated with frequency of past consumption, and importantly, that inclusion of “beyond liking” measures improved behavioural prediction over and above models based on hedonic responses only. These findings confirmed that liking in and of itself is insufficient as a predictor of consumption and supported calls for the purposeful combination of different response types using “global” or multi-response approaches. Differences between the two studies pertaining to the relative importance of liking and the best combination of predictors were uncovered, suggesting that the optimal combination of “beyond liking” measures in practical applications is likely to be study-specific.

Neural Approach in Short-Term Outdoor Temperature Prediction for Application in HVAC Systems

An accurate air-temperature prediction can provide the energy consumption and system load in advance, both of which are crucial in HVAC (heating, ventilation, air conditioning) system operation optimisation as a way of reducing energy losses, operating costs, as well as pollution and dust emissions while maintaining residents’ thermal comfort. This article presents the results of an outdoor air-temperature time-series prediction for a multifamily building with the use of artificial neural networks during the heating period (October–May). The aim of the research was to analyse in detail the created neural models with a view to select the best combination of predictors and the optimal number of neurons in a hidden layer. To meet that task, the Akaike information criterion was used. The most accurate results were obtained by MLP 3-3-1 (r = 0.986, AIC = 1300.098, SSE = 4467.109), with the ambient-air-temperature time series observed 1, 2, and 24 h before the prognostic temperature as predictors. The AIC proved to be a useful method for the optimum model selection in a machine-learning modelling. What is more, neural network models provide the most accurate prediction, when compared with LR and SVR. Additionally, the obtained temperature predictions were used in HVAC applications: entering-water temperature and indoor temperature modelling.

Predicting Upper Quadrant Musculoskeletal Injuries in the Military: A Cohort Study.

This study aimed to identify characteristics and movement-based tests that predict upper quadrant musculoskeletal injury (UQI) in military personnel over a 12-month follow-up. A prospective observational cohort study of military members (n = 494; 91.9% male) was conducted. Baseline predictors associated with UQI were gathered through surveys and movement-based tests. Survey data included demographic information, injury history, and biosocial factors. Movement-based tests include the following: Y Balance Tests (YBT), Functional Movement Screen, Selective Functional Movement Assessment lumbar multisegmental mobility, modified-modified Schober, side bridge, ankle mobility, modified Sorensen, and passive lumbar extension. Self-reported UQI was collected through monthly online surveys, and 87% completed the follow-up. Univariate associations were determined between potential predictors and UQI. A forward, stepwise logistic regression model was used to identify the best combination of predictors for UQI. Twenty-seven had UQI. Univariate associations existed with three demographic (smoking, >1 previous UQI, baseline upper quadrant function ≤90%), three pain-related (Selective Functional Movement Assessment rotation, side bridge, hurdle step), and six movement-based variables (YBT upper quarter (UQ) superolateral worst score ≤57.75 cm, YBT-UQ composite worst score ≤81.1%, failed shoulder clearance, Sorenson <72.14 s, in-line lunge total score <15, and in-line lunge asymmetry >1). Smoking, baseline upper quadrant function ≤90%, and YBT-UQ composite score ≤81.1% predicted UQI in the logistic regression while controlling for age and sex. Presenting two or more predictors resulted in good specificity (85.6%; odds ratio, 4.8; 95% confidence interval, 2.2-10.8), and at least one predictor resulted in 81.5% sensitivity (odds ratio, 3.2; 95% confidence interval, 1.2-8.7). A modifiable movement-based test (YBT-UQ), perceived upper limb function, and smoking predicted UQI. A specific (two or more) and sensitive (at least one predictor) model could identify persons at higher risk.

Development and Validation of a Predictive Model of Hypovitaminosis D in General Adult Population: SCOPYD Study.

The worldwide global increase in serum 25-hydroxyvitamin D (25(OH)D) measurements has led some countries to restrict reimbursement for certain clinical situations only. Another approach could consist in providing physicians with screening tools in order to better target blood test prescription. The objective of the SCOPYD study was to identify the best combination of predictors of serum VitD concentration among adults aged 18–70 years. Potential risk factors for VitD deficiency were collected using a comprehensive self-administered questionnaire. A multivariable linear regression was used to build a predictive model of serum 25(OH)D concentration. Among 2488 participants, 1080 (43.4%) had VitD deficiency (<50 nmol/L) and 195 (7.8%) had severe deficiency (<25 nmol/L). The final model included sunlight exposure in the preceding week and during the last holidays, month of blood sampling, age, sex, body mass index, skin phototype, employment, smoking, sport practice, latitude, and VitD supplementation in preceding year. The area under the curve was 0.82 (95% CI (0.78; 0.85)) for severe deficiency. The model predicted severe deficiency with a sensitivity of 77.9% (95% CI (69.1; 85.7)) and a specificity of 68.3% (95% CI (64.8; 71.9)). We identified a set of predictors of severe VitD deficiency that are easy to collect in routine that may help to better target patients for serum 25(OH)D concentration determination.

Prospectivity modelling of Canadian magmatic Ni (±Cu ± Co ± PGE) sulphide mineral systems

New mineral deposit discoveries are required to meet the forecasted demand for some critical raw materials. Governments are responding to that challenge by investing in mineral systems research and by by making government geoscience datasets freely available to the public and explorers. However, translating conceptual mineral system models to mappable geological, geochemical, and geophysical proxies is difficult with incomplete data of variable quality from modern and legacy surveys. Herein we address those knowledge gaps and propose a new open source workflow in R for prospectivity modelling using public geoscience datasets. We focus on the largest footprints of magmatic Ni (±Cu) sulphide mineral systems and their critical raw materials (±Co ± PGE). Multiple prospectivity models are presented, including data-driven methods (e.g., weights of evidence, gradient boosting machines) that use the features of known mineral occurrences as a training set and a hybrid method that also incorporates conceptual mineral system criteria. All models are validated using data from northern Canada (i.e., north of 60° latitude) as a test set. Statistical analysis of the prospectivity results suggests that rock types and geological ages are two of the most important predictive datasets, which correspond to the sources and drivers within the mineral system framework, respectively. Variable importance plots further suggest that geological boundaries (e.g., horizontal gradient magnitude of the gravity data and multi–scale edges) and the close spatial association between areas of high mineral potential and the edges of thick continental crust represent prospective ore-forming pathways. Model performance and the best combination of predictors and hyperparameters for each model are based on the receiver operating characteristics (ROC) plots, which yield a range of area under the curve (AUC) from 0.846 to 0.923 for the spatially independent test set. Most Canadian geological provinces, possibly with the exception of the Grenville orogen for the hybrid and weights of evidence methods (AUC = 0.716–0.726), yield comparable model performance, suggesting that the heterogeneous spatial distribution of different mineral system sub-types (e.g., komatiite-associated, rift-related, Alaskan-type, and hydrothermal awaruite) have a relatively minor impact on the prospectivity results. Monte Carlo-type simulations further suggest that the expert weightings used in the hybrid method (AUC = 0.843) are only slightly better than an average model constructed from random combinations of weightings (AUC = 0.809). The general agreement between different methods and multiple iterations of the same model demonstrate that public geoscience datasets can effectively reduce the search space to support mineral exploration targeting (i.e., less than 8% of map pixels contain more than 80% of the known Ni mineralization). However, vast segments of the Canadian landmass have not undergone systematic geological surveying or data acquisition. Prospectivity modelling can thus also be used by governments and academia to prioritize areas for future targeted geoscience research.

Retrospective Study of Investigation of Possible Predictors for Total Fertility Rate in India

Total fertility rate (TFR) is the most acceptable and widely used measure of current fertility. Since TFR is based on age-specific fertility rate which required the total number of births in different age groups as well as age of female. When the population is illiterate or older then the information on age may have some recall bias, misreporting digit preference etc., thus in this situation TFR may departed from the actual. Therefore, need some indirect methodology which enables us to have an idea about the estimation of TFR. In this study an attempt has been made to identify some predictors that the explain TFR and try to suggest the best combination of predictors to get estimate of TFR. The methodology used in this study is essentially based on the regression technique. The identification and acceptance of possible predictors are based on the coefficient of determination. The data for the major states of India from National Family Health Survey (NFHS 4) is used for the analysis.

Organic Micropollutants in New York Lakes: A Statewide Citizen Science Occurrence Study.

The widespread occurrence of organic micropollutants (OMPs) is a challenge for aquatic ecosystem management, and closing the gaps in risk assessment of OMPs requires a data-driven approach. One promising tool for increasing the spatiotemporal coverage of OMP data sets is through the active involvement of citizen volunteers to expand the scale of OMP monitoring. Working collaboratively with volunteers from the Citizens Statewide Lake Assessment Program (CSLAP), we conducted the first statewide study on OMP occurrence in surface waters of New York lakes. Samples collected by CSLAP volunteers were analyzed for OMPs by a suspect screening method based on mixed-mode solid-phase extraction and liquid chromatography-high resolution mass spectrometry. Sixty-five OMPs were confirmed and quantified in samples from 111 lakes across New York. Hierarchical clustering of OMP occurrence data revealed the relevance of 11 most frequently detected OMPs for classifying the contamination status of lakes. Partial least squares regression and multiple linear regression analyses prioritized three water quality parameters linked to agricultural and developed land uses (i.e., total dissolved nitrogen, specific conductance, and a wastewater-derived fluorescent organic matter component) as the best combination of predictors that partly explained the interlake variability in OMP occurrence. Lastly, the exposure-activity ratio approach identified the potential for biological effects associated with detected OMPs that warrant further biomonitoring studies. Overall, this work demonstrated the feasibility of incorporating citizen science approaches into the regional impact assessment of OMPs.

A Nomogram for Predicting Severe Exacerbations in Stable COPD Patients.

ObjectiveTo develop a practicable nomogram aimed at predicting the risk of severe exacerbations in COPD patients at three and five years.MethodsCOPD patients with prospective follow-up data were extracted from Subpopulations and Intermediate Outcome Measures in COPD Study (SPIROMICS) obtained from National Heart, Lung and Blood Institute (NHLBI) Biologic Specimen and Data Repository Information Coordinating Center. We comprehensively considered the demographic characteristics, clinical data and inflammation marker of disease severity. Cox proportional hazard regression was performed to identify the best combination of predictors on the basis of the smallest Akaike Information Criterion. A nomogram was developed and evaluated on discrimination, calibration, and clinical efficacy by the concordance index (C-index), calibration plot and decision curve analysis, respectively. Internal validation of the nomogram was assessed by the calibration plot with 1000 bootstrapped resamples.ResultsAmong 1711 COPD patients, 523 (30.6%) suffered from at least one severe exacerbation during follow-up. After stepwise regression analysis, six variables were determined including BMI, severe exacerbations in the prior year, comorbidity index, post-bronchodilator FEV1% predicted, and white blood cells. Nomogram to estimate patients’ likelihood of severe exacerbations at three and five years was established. The C-index of the nomogram was 0.74 (95%CI: 0.71–0.76), outperforming ADO, BODE and DOSE risk score. Besides, the calibration plot of three and five years showed great agreement between nomogram predicted possibility and actual risk. Decision curve analysis indicated that implementation of the nomogram in clinical practice would be beneficial and better than aforementioned risk scores.ConclusionOur new nomogram was a useful tool to assess the probability of severe exacerbations at three and five years for COPD patients and could facilitate clinicians in stratifying patients and providing optimal therapies.

Prediction of difficult laryngoscopy in school-aged patients with microtia.

Children with microtia may experience difficult laryngoscopy because the ear and the mandible develop from the first and second bronchial arches and clefts. In this prospective observational study, we enrolled 166 patients (6-14 years old) with microtia scheduled for auricular reconstruction with autologous costal cartilage framework. Airway of the subjects was assessed preoperatively with the Modified Mallampati classification, Thyromental distance, Interincisor gap, Forward protrusion of the mandible and Horizontal length of the mandible. Anesthesiologist performed the direct laryngoscopy. Difficult laryngoscopy was classified as grade III or IV based on the Cormack-Lehane classification. A total of 166 patients completed the study, including 158 (95%) isolated microtia patients and 8 (5%) hemifacial microsomia patients. The incidence of difficult laryngoscopy was 34/166 (20.5%) in all patients, 3/8 (37.5%) in hemifacial microsomia patients, 31/158 (19.6%) in isolated microtia patients (P=0.35). Multivariate logistic regression shows that Thyromental distance, Interincisor gap, Forward protrusion of the mandible are all independent predictors of difficult laryngoscopy. The sensitivity and specificity of each predictor were: Thyromental distance (82.35%, 89.39%), Interincisor gap (58.82%, 86.36%), and Forward protrusion (17.65%, 97.73%), respectively. The cutoff values of Thyromental distance and Interincisor gap for prediction of difficult laryngoscopy were 4.0 cm and 3.3 cm. The best combination of predictors was Thyromental distance/Interincisor gap/Forward protrusion of the mandible with a sensitivity of 94.12% and specificity of 86.36%. The combination of Thyromental distance, Interincisor gap, and Forward protrusion of the mandible is the optimal assessment to predict difficult laryngoscopy in school-aged patients with microtia.

Evaluation of performance of leading algorithms for variant pathogenicity predictions and designing a combinatory predictor method: application to Rett syndrome variants.

BackgroundGenomics diagnostic tests are done for a wide spectrum of complex genetics conditions such as autism and cancer. The growth of technology has not only aided in successfully decoding the genetic variants that causes or trigger these disorders. However, interpretation of these variants is not a trivial task even at a level of distinguish pathogenic vs benign variants.MethodsWe used the clinically significant variants from ClinVar database to evaluate the performance of 14 most popular in-silico predictors using supervised learning methods. We implemented a feature selection and random forest classification algorithm to identify the best combination of predictors to evaluate the pathogenicity of a variant. Finally, we have also utilized this combination of predictors to reclassify the variants of unknown significance in MeCP2 gene that are associated with the Rett syndrome.ResultsThe results from analysis shows an optimized selection of prediction algorithm and developed a combinatory predictor method. Our combinatory approach of using both best performing independent and ensemble predictors reduces any algorithm biases in variant characterization. The reclassification of variants (such as VUS) in MECP2 gene associated with RETT syndrome suggest that the combinatory in-silico predictor approach had a higher success rate in categorizing their pathogenicity.

How to Predict Intraocular Pressure Reduction after Cataract Surgery? A Prospective Study

ABSTRACTPurpose/Aim: To evaluate the best formula to predict intraocular pressure (IOP) reduction after cataract surgery.Materials and methods: In this prospective longitudinal study, we included consecutive patients with or without glaucoma, either with open or with narrow angles but without peripheral anterior synechiae, who underwent phacoemulsification. Clinical factors and anterior segment parameters measured with optical biometry and optical coherence tomography were evaluated as preoperative predictors for IOP reduction at 6 months postoperatively. To find the best combinations of predictors, model selection was conducted using least absolute selection and shrinkage operator regression with cross-validation.Results: A total of 156 eyes from 109 patients were enrolled. The mean age of the patients was 74.89 (±8.54) years and the average preoperative IOP was 15.6 (±3.68) mmHg with 0.7 (range 0–4) glaucoma medications. The mean IOP reduction after phaco was 3.1 (±2.49) mmHg at postoperative month 6. After multivariate analysis, preoperative IOP (β = 0.49 [0.4–0.59] P < .0001), gonioscopy score (β = −0.14 [−0.23 to −0.06] P = .0001), anterior chamber depth (β = −0.85 [−1.64 to −0.07] P = 0.033), lens thickness (LT) (β = 0.87 [0.12–1.62] P = .024), and angle open distance 750 (β = −2.2 [−3.96 to −0.44] P = 0.014) were associated with IOP reduction. The best formula to correlate with IOP reduction was −4.76 + (0.46 × preoperative IOP) – (0.42 × LT) – (0.1 × gonioscopy score) – (0.66 × glaucoma status).Conclusion: Different formulas were developed and were able to improve the predictive value of the preoperative IOP. When used in combination, these formulas may help the clinician to know how much IOP reduction to expect after cataract surgery at 6 months postoperatively.

Individual Differences Related to College Students’ Course Performance in Calculus II

In this study, we explore student achievement in a semester-long flipped Calculus II course, combining various predictor measures related to student attitudes (math anxiety, math confidence, math interest, math importance) and cognitive skills (spatial skills, approximate number system), as well as student engagement with the online system (discussion forum interaction, time to submission of workshop assignments, quiz attempts), in predicting final grades. Data from 85 students enrolled in a flipped Calculus II course was used in dominance analysis to determine which predictors emerged as the most important for predicting final grades. Results indicated that feelings of math importance, approximate number system (ANS) ability, total amount of discussion forum posting, and time grading peer workshop submissions was the best combination of predictors of final grade, accounting for 17% of variance in a student’s final grade. The point of this work was to determine which predictors are the most important in predicting student grade, with the end goal of building a recommendation system that could be implemented to help students in this traditionally difficult class. The methods used here could be used for any class.