Important Predictor Variables Research Articles

ASSESSMENT OF ADHERENCE TO ANTIHYPERTENSIVE MEDICATION IN A POPULATION OF ARGENTINA DURING THE COVID-19 PANDEMIC

Objective: The objective was to assess the adherence to antihypertensive treatment and its determinants in patients during the pandemic by COVID-19 Design and method: A multicenter, prospective, observational cohort study included outpatients from the Cuyo region of Argentina from March to July 2021 that met the following inclusion criteria: 1- patients over 16 years old under antihypertensive treatment; 2- informed consent signature 3- complete 35-dimensional questionnaire. Results: From 512 enrolled patients, 468 met the inclusion criteria. The average age was 50.1 ± 0.1 years, 56.8% were women. At least 39% had 1 CV risk factor. COVID-19 was documented in 35% and 3.6% presented a severe form. The most widely used vaccine was Sputnik V and 70% had at least 1 dose of vaccination for COVID-19. Average adherence was 69%. The average of systolic blood pressure was 139.3 ± 1.0 mmHg and diastolic blood pressure was 85.9 ± 0.6 mmHg. The average number of antihypertensive drugs was 1.26. Table 1 shows the results of multivariate analysis. Conclusions: In a population with intermediate cardiovascular risk, adherence was low, and blood pressure control was suboptimal. The most important predictive variables of low adherence were age, the number of antihypertensive drug tablets, level of education, and smoking.

The psychological contracts of university professional staff: expectations, obligations and benefits

ABSTRACT The psychological contract shapes people’s perceptions and behaviours in the workplace through how people perceive and react to feedback from their environment. Little research has been carried out on the psychological contracts of university professional staff and this oversight is particularly problematic due to the impact that the psychological contract has on employee wellbeing and performance. An online survey instrument was utilised incorporating previously validated measures of psychological contract breach, satisfaction, and positive and negative affect. 226 responses to the survey were analysed through a hierarchical linear regression analysis. This research finds that the psychological contract and psychological contract breach can be conceptualised as either contemporary or traditional, incorporating elements from those career orientations. This article concludes that psychological contract expectations of contemporary career factors are one of the most important predictor variables of psychological contract breach alongside satisfaction.

Predicting Major Adverse Cardiovascular Events in Acute Coronary Syndrome: A Scoping Review of Machine Learning Approaches.

Acute coronary syndrome is the topmost cause of death worldwide; therefore, it is necessary to predict major adverse cardiovascular events and cardiovascular deaths in patients with acute coronary syndrome to make correct and timely clinical decisions. The current review aimed to highlight algorithms and important predictor variables through examining those studies which used machine learning algorithms for predicting major adverse cardiovascular events in patients with acute coronary syndrome. To predict major adverse cardiovascular events in patients with acute coronary syndrome, the preferred reporting items for scoping reviews guidelines were used. In doing so, PubMed, Embase, Web of Science, Scopus, Springer, and IEEE Xplore databases were searched for articles published between 2005 and 2021. The checklist "Quality assessment of machine learning studies" was used to assess the quality of eligible studies. The findings of the studies are presented in the form of a narrative synthesis of evidence. In total, among 2,558 retrieved articles, 22 studies were qualified for analysis. Major adverse cardiovascular events and mortality were predicted in 5 and 17 studies, respectively. According to the results, 14 (63.64%) studies did not perform external validation and only used registry data. The algorithms used in this study comprised, inter alia, Regression Logistic, Random Forest, Boosting Ensemble, Non-Boosting Ensemble, Decision Trees, and Naive Bayes. Multiple studies (N = 20) achieved a high area under the ROC curve between 0.8 and 0.99 in predicting mortality and major adverse cardiovascular events. The predictor variables used in these studies were divided into demographic, clinical, and therapeutic features. However, no study reported the integration of machine learning model into clinical practice. Machine learning algorithms rendered acceptable results to predict major adverse cardiovascular events and mortality outcomes in patients with acute coronary syndrome. However, these approaches have never been integrated into clinical practice. Further research is required to develop feasible and effective machine learning prediction models to measure their potentially important implications for optimizing the quality of care in patients with acute coronary syndrome.

PD55-04 PREDICTING THE NEED FOR INTERVENTION IN RENAL COLIC DUE TO A URETERAL STONE <5 MM: A MACHINE LEARNING MODEL

You have accessJournal of UrologyCME1 May 2022PD55-04 PREDICTING THE NEED FOR INTERVENTION IN RENAL COLIC DUE TO A URETERAL STONE <5 MM: A MACHINE LEARNING MODEL Rennen Haramaty, Nir Kleinmann, Miki Haifler, and Dorit E. Zilberman Rennen HaramatyRennen Haramaty More articles by this author , Nir KleinmannNir Kleinmann More articles by this author , Miki HaiflerMiki Haifler More articles by this author , and Dorit E. ZilbermanDorit E. Zilberman More articles by this author View All Author Informationhttps://doi.org/10.1097/JU.0000000000002635.04AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: Renal colic due to ureteral stone is among the most common reasons for acute admissions to a hospital urology department. A high expulsion rate is reported for ureteric stones ≤5 mm. We aim to fit a machine learning model (ML) for surgical intervention prediction in cases of intractable pain caused by <5 mm ureteral stones. METHODS: We retrospectively reviewed all patients with renal colic caused by ureteral stone <5 mm at our institute between 2016-2021. Data on age, sex, body mass index, the presence of associated hydronephrosis, fat stranding, stone side and location, medical history, serum white blood and neutrophil counts, creatinine, C-reactive protein, and vital signs were obtained upon admission. Continuous and categorical variables were compared with the Wilcoxon rank-sum test and Fisher exact test. Missing data were imputed with multiple imputations. We fit an extreme gradient boosting ML model (XGboost) to predict the need for intervention based on clinical parameters. Variable importance was assessed with Shapley values additive explanation. The performance of the XGboost was determined by the area under the ROC curve metric (AUC) and decision curve analysis. RESULTS: 471 patients (median age 49 [IQR 40.5 ,60], 83% males) were included. The median stone diameter was 3.5 mm (3-4.1) and 74% of the stones were in the distal ureter. 160 (34%) patients who experienced intractable pain underwent surgical intervention. The time to stone expulsion and the time to surgical intervention were not significantly different. The model’s AUC according to the training and test data AUC were 0.8 and 0.78, respectively (p=0.68, Figure 1). Stone location and size had the highest variable importance compared to other variables. CONCLUSIONS: The decision to surgically remove ureteral stones <5 mm is difficult and can be facilitated by the use of our accurate prediction ML model. Our prediction ML model was both accurate and generalizable. The most important variables for intervention prediction were stone size and location. The presented model may be implemented in the treatment protocols, helping the decision-making process. Source of Funding: None © 2022 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 207Issue Supplement 5May 2022Page: e948 Advertisement Copyright & Permissions© 2022 by American Urological Association Education and Research, Inc.MetricsAuthor Information Rennen Haramaty More articles by this author Nir Kleinmann More articles by this author Miki Haifler More articles by this author Dorit E. Zilberman More articles by this author Expand All Advertisement PDF DownloadLoading ...

National‐scale predictions of plant assemblages via community distribution models: Leveraging published data to guide future surveys

Abstract Species distribution models (SDMs) have been widely used to create maps of expected species incidence, often using citizen science (CS) occurrence data as inputs. Environmental policy is informed by knowledge of community distributions, but there have been fewer attempts to utilise the potential of community distribution models (CDMs) to predict these. Many countries have vegetation community classification systems which include phytosociological information on individual species. Within Great Britain, the National Vegetation Classification (NVC) is the primary standard for vegetation communities, and while maps have been produced at regional scales, cost‐effective techniques are required for national scales. Published NVC occurrence records of 22 upland NVC communities in England and Wales were used as observed occurrences (presence‐only data). Predictors for the CDMs were enhanced vegetation index (EVI), elevation, slope, aspect, temperature and rainfall. Five modelling methods were investigated: generalised linear models (GLMs), support vector machines (SVMs), random forests (RFs), maximum entropy (MaxEnt) and maximum likelihood (MaxLike). Model quality was assessed via bootstrapping via area under the curve (AUC), true skill statistic (TSS) and Kappa index. There were only small differences in the accuracy of the models (median TSS model accuracy 0.742; range 0.280 to 0.873) with RF models the best overall CDM method. Across all NVC communities, summer and winter maximum temperatures and annual rainfall were the most important predictor variables. NVCs with spatially disjunct distributions in both lowlands and uplands, or that responded to localised management or environmental conditions, were poorly predicted. Synthesis and applications. Vegetation communities can be reliably predicted at large spatial scales using CDMs from extant datasets. Management practitioners can use community‐level predictions to design targeted field surveys for individual species typically associated with specific communities. Most existing CS survey schemes focus on species rather than communities. Hence future development of new CS schemes similar to the National Plant Monitoring Scheme (NPMS), that aligns with the NVC, will enable CS data to generate up‐to‐date maps of both communities and species.

Prediction and Evaluation of Machine Learning Algorithm for Prediction of Blood Transfusion during Cesarean Section and Analysis of Risk Factors of Hypothermia during Anesthesia Recovery.

Prediction and Evaluation of Machine Learning Algorithm for Prediction of Blood Transfusion during Cesarean Section and Analysis of Risk Factors of Hypothermia during Anesthesia Recovery.

Effect of Different Culture Conditions on Gene Expression Associated With Cyst Production in Populations of Artemia franciscana.

Artemia franciscana inhabits hypersaline environments in the Americas and has a well-adapted reproductive system that allows it to survive in these extreme conditions, represented by the production of diapause cysts (oviparous reproduction). This reproduction mode is controlled by numerous genes that are expressed in response to different environmental stressors, enabling this species to avoid population extinction. However, to date, the expression of these genes has not been sufficiently studied to clarify their levels in response to a combination of different environmental factors under controlled conditions. We analyzed the expression of eight genes related to oviparous reproduction (SGEG, Arp-CBP, artemin, BRCA1, p8, ArHsp21, ArHsp22, and p26) to determine their association with cyst production in two populations of A. franciscana with contrasting phenotypes, one with high (Barro Negro, BNE, Chile) and one with low (San Francisco Bay, SFB, United States) cyst production. Populations were cultured under controlled conditions of salinity (SAL, 35 and 75 ppt), photoperiod (PHO, 12L:12D and 24L:00D), iron concentration (IC, 0[Fe] and 5[Fe]), and microalgae diet (DIE; Dunaliella tertiolecta (DUN) and Tetraselmis suecica (TETRA)). Sixteen treatments were performed by combining the two conditions of each of the four factors. Data on nine reproductive parameters per female were recorded, including the percent of offspring encysted (%) (POE). The gene expression levels were analyzed by semiquantitative RT-PCR. The mean POE was significantly greater in BNE than in SFB (32.40 versus 12.74%, Mann–Whitney’s test, p < 0.05). Significantly upregulated expression of seven genes in BNE (more than twofold, p < 0.05) was observed in 38.28% of the treatments (e.g., DUN-75ppt-12L:12D-5[Fe] and TETRA-35ppt-12L:12D-5[Fe]). In SFB, seven genes showed significant differential expression, but most were downregulated in 29.69% of the treatments (e.g., DUN-75ppt-12L:12D-0[Fe] and DUN-75ppt-24L:00D-0[Fe]). Multiple regression analyses indicated that in BNE, five genes (SGEG, artemin, Arp-CBP, p8, and BRCA1) and three environmental factors (DIE, SAL, and IC) were important predictor variables for the POE response variable given that all of them were included in the highest-ranking models. In SFB, only two genes (ArHsp21 and artemin) and one environmental factor (SAL) were important explanatory variables in the highest-ranking models. It was concluded that the BNE population presented a characteristic gene expression pattern that differed from that of the SFB population. This pattern might be related to the marked oviparous reproduction of the BNE population. This gene expression pattern could be useful for monitoring the reproductive mode leading to diapause in Artemia and to assist with intensive cyst production in pond systems.

The Impact of Variable Omission on Variable Importance Measures of Cart, Random Forest, and Boosting Algorithms

When researchers use statistical models to gain insights into various phenomena, they make a tacit assumption that most (if not all) of the important predictor variables with respect to the outcome are included in the analysis. However, in practice this may not always be possible, whether because some important variables could not be measured, or because a researcher was not aware of all such important predictors. Prior research has shown that when important variables are omitted from both linear and nonlinear regression models, the model coefficients can be biased, with greater levels of bias being associated with larger correlations between the missing and retained variables. However, very little work has examined how such omissions impact the performance of variable importance measures used with popular machine learning algorithms. Therefore, the purpose of this simulation study was to address this gap in the literature and thereby provide insights into the impact of such omissions on variable importance measures for classification and regression trees, random forests, and boosting algorithms. Results showed that when an important variable is omitted from an analysis, other predictors that are correlated with and/or involved in an interaction with it will have inflated variables importance measures themselves. An empirical example and implications of these results are discussed. Journal of Statistical Research 2021, Vol. 55, No. 2, pp. 335-358

Utility of machine learning in developing a predictive model for early-age-onset colorectal neoplasia using electronic health records.

The incidence of colorectal cancer (CRC) is increasing in adults younger than 50, and early screening remains challenging due to cost and under-utilization. To identify individuals aged 35-50 years who may benefit from early screening, we developed a prediction model using machine learning and electronic health record (EHR)-derived factors. We enrolled 3,116 adults aged 35-50 at average-risk for CRC and underwent colonoscopy between 2017-2020 at a single center. Prediction outcomes were (1) CRC and (2) CRC or high-risk polyps. We derived our predictors from EHRs (e.g., demographics, obesity, laboratory values, medications, and zip code-derived factors). We constructed four machine learning-based models using a training set (random sample of 70% of participants): regularized discriminant analysis, random forest, neural network, and gradient boosting decision tree. In the testing set (remaining 30% of participants), we measured predictive performance by comparing C-statistics to a reference model (logistic regression). The study sample was 55.1% female, 32.8% non-white, and included 16 (0.05%) CRC cases and 478 (15.3%) cases of CRC or high-risk polyps. All machine learning models predicted CRC with higher discriminative ability compared to the reference model [e.g., C-statistics (95%CI); neural network: 0.75 (0.48-1.00) vs. reference: 0.43 (0.18-0.67); P = 0.07] Furthermore, all machine learning approaches, except for gradient boosting, predicted CRC or high-risk polyps significantly better than the reference model [e.g., C-statistics (95%CI); regularized discriminant analysis: 0.64 (0.59-0.69) vs. reference: 0.55 (0.50-0.59); P<0.0015]. The most important predictive variables in the regularized discriminant analysis model for CRC or high-risk polyps were income per zip code, the colonoscopy indication, and body mass index quartiles. Machine learning can predict CRC risk in adults aged 35-50 using EHR with improved discrimination. Further development of our model is needed, followed by validation in a primary-care setting, before clinical application.

Accounting for the State Fixed Effect for Municipal Cash Reserves: The Role of Financial and Institutional Variables

One of the most important predictor variables in multistate studies of American municipal cash reserves is the state in which a municipality is located, and to date no research has explored why. In this paper, we show that two broad categories of variables can account for a large fraction of this puzzle. Average differences in financial variables and state-to-state institutional differences combine to absorb over 80% of the importance of state fixed effects, with financial variables mostly accounting for why cities in some states save less than the national average, and institutional variables doing a better job of accounting for why cities in some states save more than the national average.

Using Logistic Regression to Predict the Probability That Individual ‘Honeycrisp’ Apples Will Develop Bitter Pit

‘Honeycrisp’ is a popular apple cultivar, but it is prone to several disorders, especially bitter pit. Previously reported models for predicting bitter pit are biased, indicating that the models are missing one or more important predictor variables. To identify additional variables that may improve bitter pit prediction, a study was undertaken to investigate the influence of canopy position, spur characteristics, and individual fruit characteristics on bitter pit development. ‘Honeycrisp’ trees from two orchards over 2 years provided four combinations of orchards and years. Fruits were sampled from spurs at different canopy positions and with varying bourse shoot lengths and numbers of fruits and leaves. Following cold storage, bitter pit was assessed in three ways: 1) bitter pit severity was recorded as the number of pits per fruit, 2) bitter pit was recorded as a binomial response (yes, no) for each fruit, and 3) the incidence of bitter pit was recorded as the proportion of fruit developing bitter pit. As a result of the high fruit-to-fruit variation, bitter pit severity was associated with canopy position or spur characteristics to a lesser extent than bitter pit incidence. Bitter pit incidence was generally greater for fruits developing on spurs with only one fruit and spurs from the lower canopy. Binomial data were analyzed with a generalized linear mixed model. Fruit harvested from trees with heavy crop loads, and those developing on spurs with multiple fruit and spurs with long bourse shoots had the lowest probability of developing bitter pit. Regardless of how bitter pit was assessed, bitter pit related positively to fruit weight (FW), but the relationship usually depended on other variables such as canopy position, fruit per spur, and leaves per spur. The advantages of fitting binomial data with logistic regression models are discussed.

Predicting the necessity of oxygen therapy in the early stage of COVID-19 using machine learning.

Medical oxygen is a critical element in the treatment process of COVID-19 patients which its shortage impacts the treatment process adversely. This study aims to apply machine learning (ML) to predict the requirement for oxygen-based treatment for hospitalized COVID-19 patients. In the first phase, demographic information, symptoms, and patient’s background were extracted from the databases of two local hospitals in Iran, and preprocessing actions were applied. In the second step, the related features were selected. Lastly, five ML models including logistic regression (LR), random forest (RF), XGBoost, C5.0, and neural networks (NNs) were implemented and compared based on their accuracy and capability. Among the variables related to the patient’s background, consuming opium due to the high rate of opium users in Iran was considered in the models. Of the 398 patients included in the study, 112 (28.14%) received oxygen-based treatment. Shortness of breath (71.42%), fever (62.5%), and cough (59.82%) had the highest frequency in patients with oxygen requirements. The most important variables for prediction were shortness of breath, cough, age, and fever. For opioid-addicted patients, in addition to the high mortality rate (23.07%), the rate of oxygen-based treatment was twice as high as non-addicted patients. XGBoost and LR obtained the highest area under the curve with values of 88.7% and 88.3%, respectively. For accuracy, LR and NNs achieved the best and same accuracy (86.42%). This approach provides a tool that accurately predicts the need for oxygen in the treatment process of COVID-19 patients and helps hospital resource management.Graphical abstract

Personalized Surgical Transfusion Risk Prediction Using Machine Learning to Guide Preoperative Type and Screen Orders.

Accurate estimation of surgical transfusion risk is essential for efficient allocation of blood bank resources and for other aspects of anesthetic planning. This study hypothesized that a machine learning model incorporating both surgery- and patient-specific variables would outperform the traditional approach that uses only procedure-specific information, allowing for more efficient allocation of preoperative type and screen orders. The American College of Surgeons National Surgical Quality Improvement Program Participant Use File was used to train four machine learning models to predict the likelihood of red cell transfusion using surgery-specific and patient-specific variables. A baseline model using only procedure-specific information was created for comparison. The models were trained on surgical encounters that occurred at 722 hospitals in 2016 through 2018. The models were internally validated on surgical cases that occurred at 719 hospitals in 2019. Generalizability of the best-performing model was assessed by external validation on surgical cases occurring at a single institution in 2020. Transfusion prevalence was 2.4% (73,313 of 3,049,617), 2.2% (23,205 of 1,076,441), and 6.7% (1,104 of 16,053) across the training, internal validation, and external validation cohorts, respectively. The gradient boosting machine outperformed the baseline model and was the best- performing model. At a fixed 96% sensitivity, this model had a positive predictive value of 0.06 and 0.21 and recommended type and screens for 36% and 30% of the patients in internal and external validation, respectively. By comparison, the baseline model at the same sensitivity had a positive predictive value of 0.04 and 0.144 and recommended type and screens for 57% and 45% of the patients in internal and external validation, respectively. The most important predictor variables were overall procedure-specific transfusion rate and preoperative hematocrit. A personalized transfusion risk prediction model was created using both surgery- and patient-specific variables to guide preoperative type and screen orders and showed better performance compared to the traditional procedure-centric approach.

Integrating landmark modeling framework and machine learning algorithms for dynamic prediction of tuberculosis treatment outcomes.

This study aims to establish an informative dynamic prediction model of treatment outcomes using follow-up records of tuberculosis (TB) patients, which can timely detect cases when the current treatment plan may not be effective. We used 122267 follow-up records from 17958 new cases of pulmonary TB in the Republic of Moldova. A dynamic prediction framework integrating landmark modeling and machine learning algorithms was designed to predict patient outcomes during the course of treatment. Sensitivity and positive predictive value (PPV) were calculated to evaluate performance of the model at critical time points. New measures were defined to determine when follow-up laboratory tests should be conducted to obtain most informative results. The random-forest algorithm performed better than support vector machine and penalized multinomial logistic regression models for predicting TB treatment outcomes. For all 3 outcome classes (ie, cured, not cured, and died after 24 months following treatment initiation), sensitivity and PPV of prediction models improved as more follow-up information was collected. Specifically, sensitivity and PPV increased from 0.55 to 0.84 and from 0.32 to 0.88, respectively, for the not cured class. The dynamic prediction framework utilizes longitudinal laboratory test results to predict patient outcomes at various landmarks. Sputum culture and smear results are among the important variables for prediction; however, the most recent sputum result is not always the most informative one. This framework can potentially facilitate a more effective treatment monitoring program and provide insights for policymakers toward improved guidelines on follow-up tests.

Development of machine learning prediction models to explore nutrients predictive of cardiovascular disease using Canadian linked population-based data.

Machine learning may improve use of observational data to understand the nutritional epidemiology of cardiovascular disease (CVD) through better modelling of non-linearity, non-additivity, and dietary complexity. Our objective was to develop machine learning prediction models for exploring how nutrients are related to CVD risk and to evaluate their predictive performance. We established a population-based cohort from the Canadian Community Health Survey and measured CVD incidence and mortality from 2004 to 2018 using administrative databases of national hospital discharges and deaths. Predictors included 61 nutrition variables and fourteen socioeconomic, demographic, psychological, and behavioural variables. Conditional inference forest models were interpreted and evaluated by permutation feature importance, accumulated local effects, and predictive discrimination and calibration. A total of 12 130 individuals were included in the study. Use of supplements, caffeine, and alcohol were the most important nutrition variables for prediction of CVD. Supplement use was associated with decreased risk, caffeine was associated with increasing risk, and alcohol had a u-shaped association with risk. The model had an out-of-sample c-statistic of 0.821 (95% confidence interval = 0.801-0.842). Exploratory findings included both known and novel associations and predictive performance was competitive, suggesting that further application of machine learning to nutritional epidemiology may help elucidate risks and improve predictive models. Novelty: Machine learning prediction models were developed for CVD using dietary data. Models were interpreted with interpretable machine learning techniques, revealing diverse associations between diet and CVD. Models achieved comparable or superior predictive performance to existing CVD risk prediction models.

A pretest model of obstructive coronary artery disease based on machine learning: from the C-Strat study

Objective: To develop a pretest probability model of obstructive coronary artery disease with machine learning based on multi-site Chinese population data. Methods: Chinese regiStry in early deTection and Risk strAtificaTion of coronary plaques (C-Strat) study is a prospective multi-center cohort study, in which consecutive patients with suspected obstructive coronary artery disease and ≥64 detector row coronary computed tomography angioplasty (CCTA) evaluation were included. Data from the patients were randomly split into a training set (70%) and a test set (30%). More than 50% of coronary artery stenosis by CCTA was defined as positive outcome. A boosted ensemble algorithm (XGBoost), 10-fold cross-validation and Bayesian optimization were used to establish a new prediction model-CARDIACS(pretest probability model from Chinese registry in eARly Detection and rIsk stratificAtion of Coronary plaques Study), and a logistic regression was used to establish a model-LOGISTIC in training set. The test set was used for validation and comparison among CARDIACS, LOGISTIC, UDFM (updated Diamond-Forrester Model) and DFCASS(Diamond-Forrester and CASS). Results: The study population included 29 455 patients with age of (57.0±9.7) years and 44.8% women, of whom 19.1% (5 622/29 455) had obstructive coronary artery disease. For CARDIACS, the age, the reason for visit and the body mass index (BMI) were the most important predictive variables. In the independent test set, the area under the curve (AUC) of CARDIACS was 0.72 (95%CI 0.70-0.73), which was significantly superior to that of LOGISTIC (AUC 0.69, 95%CI 0.68-0.71, P=0.015), UDFM (AUC 0.64, 95%CI 0.62-0.65, P<0.001) and DFCASS (AUC 0.66, 95%CI 0.64-0.67, P<0.001), respectively. Conclusion: Based on Chinese population, the study developed a new pretest probability model--CARDIACS, which was superior to the traditional models. CARDIACS is expected to assist in the clinical decision-making for patients with stable chest pain.

Machine learning based rate decline prediction in unconventional reservoirs

The main objective of this paper is to develop a novel machine learning based model that can accurately predict the decline curves and EUR (Estimated Ultimate Recovery) for new wells based on the data collected from nearby wells. This is because decline curves are easier and faster alternative to complex reservoir simulators which perform computationally expensive operations. In contrast to this, decline curves require only a few parameters in the equation which can be easily collected from the existing data of the wells. In this study, first we collected the well data corresponding to well parameters such as initial monthly oil flow rate ( q i ), well completion parameters (i.e., no. of fracturing stages, completed length, amount of proppant used, amount of fracturing fluid used), well location parameters (TVD Heel-Toe Difference), reservoir fluid properties (Oil API Gravity, initial 24 h period Gas-Oil Ratio (GOR), initial 24 h period Gas Produced, initial 24 h period Oil Produced), flowing tubing pressure, casing pressure tubing size,choke size from publicly available databases of the Eagle Ford Shale formation Texas RRC (Railroad Commission of Texas). Wells were selected randomly and only those wells were finally included for the study whose data of the all the required parameters were available. The model parameters were estimated by fitting the production data to the decline curve models. Artificial Neural Network (ANN) was employed to build Machine learning models as a function of the above well parameters for the corresponding model parameters. The decline curves for new or existing wells were rapidly predicted using these models. In order to estimate the predictive accuracy of these models when applied to new or test wells cross validation technique such as k-fold cross validation was employed. These models were also used to predict EUR for the test wells. Additionally, feature selection was also done using algorithms such as Chi Square Test (χ2) and Minimum Redundancy Maximum Relevance (MRMR) Algorithm to determine the relative importance of predictor variables in predicting EUR. The predictor variables were successfully linked to SEDM (Stretched Exponential Decline Model) decline curve parameters (n and τ) in a random set of oil field well data. The relative influences of various well parameters were also examined to determine the hidden relationship between them. The novelty in this study lies in the algorithm and dataset that we used for the rate decline prediction in Eagle Ford data set. Although, this paper has referenced some previous papers where machine learning has been used to make prediction, but this paper presents use of new algorithm as well as a new dataset. As more data gets available, there is definitely extra room for further data analysis and improved results using machine learning methods. In future, the number of wells can be increased and the updated results can again be submitted after investing more time. It should be noted here that data downloading and preparing takes most of the time for such study especially when dealing with oil and gas data.

Predicting medical usage rate at mass gathering events in Belgium: development and validation of a nonlinear multivariable regression model

BackgroundEvery year, volunteers of the Belgian Red Cross provide onsite medical care at more than 8000 mass gathering events and other manifestations. Today standardized planning tools for optimal preventive medical resource use during these events are lacking. This study aimed to develop and validate a prediction model of patient presentation rate (PPR) and transfer to hospital rate (TTHR) at mass gatherings in Belgium.MethodsMore than 200,000 medical interventions from 2006 to 2018 were pooled in a database. We used a subset of 28 different mass gatherings (194 unique events) to develop a nonlinear prediction model. Using regression trees, we identified potential predictors for PPR and TTHR at these mass gatherings. The additional effect of ambient temperature was studied by linear regression analysis. Finally, we validated the prediction models using two other subsets of the database.ResultsThe regression tree for PPR consisted of 7 splits, with mass gathering category as the most important predictor variable. Other predictor variables were attendance, number of days, and age class. Ambient temperature was positively associated with PPR at outdoor events in summer. Calibration of the model revealed an R2 of 0.68 (95% confidence interval 0.60–0.75). For TTHR, the most determining predictor variables were mass gathering category and predicted PPR (R2 = 0.48). External validation indicated limited predictive value for other events (R2 = 0.02 for PPR; R2 = 0.03 for TTHR).ConclusionsOur nonlinear model performed well in predicting PPR at the events used to build the model on, but had poor predictive value for other mass gatherings. The mass gathering categories “outdoor music” and “sports event” warrant further splitting in subcategories, and variables such as attendance, temperature and resource deployment need to be better recorded in the future to optimize prediction of medical usage rates, and hence, of resources needed for onsite emergency medical care.

Population median imputation was noninferior to complex approaches for imputing missing values in cardiovascular prediction models in clinical practice

ObjectivesTo compare the validity and robustness of five methods for handling missing characteristics when using cardiovascular disease risk prediction models for individual patients in a real-world clinical setting. Study design and settingThe performance of the missing data methods was assessed using data from the Swedish National Diabetes Registry (n = 419,533) with external validation using the Scottish Care Information ˗ diabetes database (n = 226,953). Five methods for handling missing data were compared. Two methods using submodels for each combination of available data, two imputation methods: conditional imputation and median imputation, and one alternative modeling method, called the naïve approach, based on hazard ratios and populations statistics of known risk factors only. The validity was compared using calibration plots and c-statistics. ResultsC-statistics were similar across methods in both development and validation data sets, that is, 0.82 (95% CI 0.82–0.83) in the Swedish National Diabetes Registry and 0.74 (95% CI 0.74–0.75) in Scottish Care Information-diabetes database. Differences were only observed after random introduction of missing data in the most important predictor variable (i.e., age). ConclusionValidity and robustness of median imputation was not dissimilar to more complex methods for handling missing values, provided that the most important predictor variables, such as age, are not missing.

Informative predictors of pregnancy after first IVF cycle using eIVF practice highway electronic health records

The aim of this study is to determine the most informative pre- and in-cycle variables for predicting success for a first autologous oocyte in-vitro fertilization (IVF) cycle. This is a retrospective study using 22,413 first autologous oocyte IVF cycles from 2001 to 2018. Models were developed to predict pregnancy following an IVF cycle with a fresh embryo transfer. The importance of each variable was determined by its coefficient in a logistic regression model and the prediction accuracy based on different variable sets was reported. The area under the receiver operating characteristic curve (AUC) on a validation patient cohort was the metric for prediction accuracy. Three factors were found to be of importance when predicting IVF success: age in three groups (38–40, 41–42, and above 42 years old), number of transferred embryos, and number of cryopreserved embryos. For predicting first-cycle IVF pregnancy using all available variables, the predictive model achieved an AUC of 68% + /− 0.01%. A parsimonious predictive model utilizing age (38–40, 41–42, and above 42 years old), number of transferred embryos, and number of cryopreserved embryos achieved an AUC of 65% + /− 0.01%. The proposed models accurately predict a single IVF cycle pregnancy outcome and identify important predictive variables associated with the outcome. These models are limited to predicting pregnancy immediately after the IVF cycle and not live birth. These models do not include indicators of multiple gestation and are not intended for clinical application.