scholarly journals Variant pathogenic prediction models VSRFM and VSRFM-s, the importance of splicing and allele frequency

2018 ◽  
Author(s):  
JL Cabrera-Alarcon ◽  
J Garcia-Martinez
Keyword(s):  
Random Forest ◽  
Allele Frequency ◽  
Prediction Models ◽  
Specific Model ◽  
Random Forest Model ◽  
Independent Data ◽  
Data Set ◽  
New Model ◽  
Forest Model

ABSTRACTCurrently, there are available several tools to predict the effect of variants, with the aim of classify variants in neutral or pathogenic. In this study, we propose a new model trained over ensemble scores with two particularities, first we consider minor frequency allele from gnomAD and second, we split variants based on their splicing for training each specific model. Variants Stacked Random Forest Model (VSRFM) was constructed for variants not involved in splicing and Variants Stacked Random Forest Model for splicing (VSRFM-s) was trained for variants affected by splicing. Comparing these scores with their constituent scores used as features, our models showed the best outcomes. These results were confirmed using an independent data set from Clinvar database, with similar results.

scholarly journals Clinical trial registries as Scientometric data: A novel solution for linking and deduplicating clinical trials from multiple registries

2021 ◽  
Author(s):  
Christian Thiele ◽  
Gerrit Hirschfeld ◽  
Ruth von Brachel
Keyword(s):  
Clinical Trials ◽  
Random Forest ◽  
Random Forest Model ◽  
Scientometric Analysis ◽  
Data Set ◽  
The Public ◽  
Forest Model ◽  
Clinical Trial Registries ◽  
Multiple Primary ◽  
Clinical Trials Registry

AbstractRegistries of clinical trials are a potential source for scientometric analysis of medical research and serve important functions for the research community and the public at large. Clinical trials that recruit patients in Germany are usually registered in the German Clinical Trials Register (DRKS) or in international registries such as ClinicalTrials.gov. Furthermore, the International Clinical Trials Registry Platform (ICTRP) aggregates trials from multiple primary registries. We queried the DRKS, ClinicalTrials.gov, and the ICTRP for trials with a recruiting location in Germany. Trials that were registered in multiple registries were linked using the primary and secondary identifiers and a Random Forest model based on various similarity metrics. We identified 35,912 trials that were conducted in Germany. The majority of the trials was registered in multiple databases. 32,106 trials were linked using primary IDs, 26 were linked using a Random Forest model, and 10,537 internal duplicates on ICTRP were identified using the Random Forest model after finding pairs with matching primary or secondary IDs. In cross-validation, the Random Forest increased the F1-score from 96.4% to 97.1% compared to a linkage based solely on secondary IDs on a manually labelled data set. 28% of all trials were registered in the German DRKS. 54% of the trials on ClinicalTrials.gov, 43% of the trials on the DRKS and 56% of the trials on the ICTRP were pre-registered. The ratio of pre-registered studies and the ratio of studies that are registered in the DRKS increased over time.

scholarly journals Machine Learning Approach Using Routine Immediate Postoperative Laboratory Values for Predicting Postoperative Mortality

2021 ◽  
Vol 11 (12) ◽  
pp. 1271
Author(s):  
Jaehyeong Cho ◽  
Jimyung Park ◽  
Eugene Jeong ◽  
Jihye Shin ◽  
Sangjeong Ahn ◽  
...  
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Prediction Models ◽  
External Validation ◽  
Model Development ◽  
Postoperative Mortality ◽  
Random Forest Model ◽  
Forest Model ◽  
Laboratory Values ◽  
Increased Risk

Background: Several prediction models have been proposed for preoperative risk stratification for mortality. However, few studies have investigated postoperative risk factors, which have a significant influence on survival after surgery. This study aimed to develop prediction models using routine immediate postoperative laboratory values for predicting postoperative mortality. Methods: Two tertiary hospital databases were used in this research: one for model development and another for external validation of the resulting models. The following algorithms were utilized for model development: LASSO logistic regression, random forest, deep neural network, and XGBoost. We built the models on the lab values from immediate postoperative blood tests and compared them with the SASA scoring system to demonstrate their efficacy. Results: There were 3817 patients who had immediate postoperative blood test values. All models trained on immediate postoperative lab values outperformed the SASA model. Furthermore, the developed random forest model had the best AUROC of 0.82 and AUPRC of 0.13, and the phosphorus level contributed the most to the random forest model. Conclusions: Machine learning models trained on routine immediate postoperative laboratory values outperformed previously published approaches in predicting 30-day postoperative mortality, indicating that they may be beneficial in identifying patients at increased risk of postoperative death.

scholarly journals Weighted Random Forests to Improve Arrhythmia Classification

Electronics ◽  
2020 ◽  
Vol 9 (1) ◽  
pp. 99 ◽  
Author(s):  
Krzysztof Gajowniczek ◽  
Iga Grzegorczyk ◽  
Tomasz Ząbkowski ◽  
Chandrajit Bajaj
Keyword(s):  
Random Forest ◽  
Optimal Parameter ◽  
Parameter Tuning ◽  
Classification Problem ◽  
Random Forest Model ◽  
Data Set ◽  
Forest Model ◽  
Intensive Care Patients ◽  
Comprehensive Examination ◽  
Straightforward Approach

Construction of an ensemble model is a process of combining many diverse base predictive learners. It arises questions of how to weight each model and how to tune the parameters of the weighting process. The most straightforward approach is simply to average the base models. However, numerous studies have shown that a weighted ensemble can provide superior prediction results to a simple average of models. The main goals of this article are to propose a new weighting algorithm applicable for each tree in the Random Forest model and the comprehensive examination of the optimal parameter tuning. Importantly, the approach is motivated by its flexibility, good performance, stability, and resistance to overfitting. The proposed scheme is examined and evaluated on the Physionet/Computing in Cardiology Challenge 2015 data set. It consists of signals (electrocardiograms and pulsatory waveforms) from intensive care patients which triggered an alarm for five cardiac arrhythmia types (Asystole, Bradycardia, Tachycardia, Ventricular Tachycardia, and Ventricular Fultter/Fibrillation). The classification problem regards whether the alarm should or should not have been generated. It was proved that the proposed weighting approach improved classification accuracy for the three most challenging out of the five investigated arrhythmias comparing to the standard Random Forest model.

scholarly journals Application of Data Mining Technology in Risk Prediction of Metabolic Syndrome in Oil Workers

2020 ◽  
Author(s):  
Jie Wang ◽  
Chao Li ◽  
Jing Li ◽  
Sheng Qin ◽  
Chunlei Liu ◽  
...  
Keyword(s):  
Metabolic Syndrome ◽  
Random Forest ◽  
Risk Prediction ◽  
Roc Curve ◽  
Prediction Models ◽  
Prediction Performance ◽  
Random Forest Model ◽  
Forest Model ◽  
The Metabolic Syndrome ◽  
Oil Workers

Abstract Background. The prevalence of metabolic syndrome continues to rise sharply worldwide, seriously threatening people's health.In this paper, three kinds of risk prediction models applicable to the metabolic syndrome of oil workers were established, and the optimal models were found through comparison. The optimal model can be used to identify people at high risk of metabolic syndrome as early as possible, to predict their risk, and to persuade them to change their adverse lifestyle so as to slow down and reduce the incidence of metabolic syndrome.Methods. A total of 1,468 workers from an oil company who participated in occupational health physical examination from April 2017 to October 2018 were included in this study. We established the Logistic regression model, the random forest model and the convolutional neural network model, and compared the prediction performance of the models according to the F1 score, sensitivity, accuracy and other indicators of the three models.Results. The results showed that the accuracy of the three models in the training set was 83.45%, 94.21% and 86.34%, the sensitivity was 78.47%, 94.62% and 81.30%, the F1 score was 0.79, 0.93 and 0.83, and the area under the ROC curve was 0.894, 0.987 and 0.935, respectively. In the test set, the accuracy was 76.72%, 80.66% and 78.69%, the sensitivity was 70.00%, 77.50% and 68.33%, the F1 score was 0.70, 0.76 and 0.71, and the area under the ROC curve was 0.797, 0.861 and 0.855, respectively.Conclusions. The study showed that the prediction performance of random forest model is better than other models, and the model has higher application value, which can better predict the risk of metabolic syndrome in oil workers, and provide corresponding theoretical basis for the health management of oil workers.

scholarly journals Full-coverage 1 km daily ambient PM<sub>2.5</sub> and O<sub>3</sub> concentrations of China in 2005–2017 based on multi-variable random forest model

2021 ◽  
Author(s):  
Runmei Ma ◽  
Jie Ban ◽  
Qing Wang ◽  
Yayi Zhang ◽  
Yang Yang ◽  
...  
Keyword(s):  
Random Forest ◽  
Transport Model ◽  
Average Concentration ◽  
Random Forest Model ◽  
Meteorological Variables ◽  
Spatial Characteristic ◽  
Daily Maximum ◽  
Chemical Transport Model ◽  
Data Set ◽  
Forest Model

Abstract. The health risks of fine particulate matter (PM2.5) and ambient ozone (O3) have been widely recognized in recent years. An accurate estimate of PM2.5 and O3 exposures is important for supporting health risk analysis and environmental policy-making. The aim of our study was to construct random forest models with high-performance, and estimate daily average PM2.5 concentration and O3 daily maximum 8 h average concentration (O3-8hmax) of China in 2005–2017 at a spatial resolution of 1 km×1 km. The model variables included meteorological variables, satellite data, chemical transport model output, geographic variables and socioeconomic variables. Random forest model based on ten-fold cross validation was established, and spatial and temporal validations were performed to evaluate the model performance. According to our sample-based division method, the daily, monthly and yearly simulations of PM2.5 gave average model fitting R2 values of 0.85, 0.88 and 0.90, respectively; these R2 values were 0.77, 0.77, and 0.69 for O3-8hmax, respectively. The meteorological variables and their lagged values can significantly affect both PM2.5 and O3-8hmax simulations. During 2005–2017, PM2.5 exhibited an overall downward trend, while ambient O3 experienced an upward trend. Whilst the spatial patterns of PM2.5 and O3-8hmax barely changed between 2005 and 2017, the temporal trend had spatial characteristic. The dataset is accessible to the public at https://doi.org/10.5281/zenodo.4009308, and the shared data set of Chinese Environmental Public Health Tracking: CEPHT (https://cepht.niehs.cn:8282/developSDS3.html).

scholarly journals Predicting the required pre-surgery blood volume in surgical patients based on machine learning

2019 ◽  
Author(s):  
Ruilin Li ◽  
Xinyin Han ◽  
Liping Sun ◽  
Yannan Feng ◽  
Xiaolin Sun ◽  
...  
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Blood Transfusion ◽  
Blood Volume ◽  
Prediction Models ◽  
Machine Learning Algorithms ◽  
Random Forest Model ◽  
Surgical Patients ◽  
Forest Model ◽  
Formidable Challenge

AbstractPrecisely predicting the required pre-surgery blood volume (PBV) in surgical patients is a formidable challenge in China. Inaccurate estimation is associate with excessive costs, postponed surgeries and adverse outcome after surgery due to in sufficient supply or inventory. This study aimed to predict required PBV based on machine learning techniques. 181,027 medical documents over 6 years were cleaned and finally obtained 92,057 blood transfusion records. The blood transfusion and surgery related factors of perioperative patients, surgeons experience volumes and the actual volumes of transfused RBCs were extracted. 6 machine learning algorithms were used to build prediction models. The surgery patients received allogenic RBCs or without transfusion, had total volume less than 10 units, or had the latest laboratory examinations of pre-surgery within 7 days were included, providing 118,823 data points. 39 predictive factors related to the RBCs transfusion were identified. Random forest model was selected to predict the required PBV of RBCs with 72.9% accuracy and strikingly improved the accuracy by 30.4% compared with surgeons experience, where 90% of data was used for training. We tested and demonstrated that both the data-driven models and the random forest model achieved higher accuracy than surgeons experience. Furthermore, we developed a computational tool, PTRBC, to precisely estimate the required PBV in surgical patients and we believe this tool will find more applications in assisting clinician decisions, not only confined to making accurate pre-surgery blood requirement predicting.

scholarly journals Comparison of Models Used to Predict Flight Delays at Jomo Kenyatta International Airport

2019 ◽  
pp. 1-8
Author(s):  
P. K. Gachoki ◽  
M. M. Muraya
Keyword(s):  
Logistic Regression ◽  
Random Forest ◽  
Support Vector Machine Model ◽  
Prediction Models ◽  
Random Forest Model ◽  
Aviation Industry ◽  
Support Vector ◽  
Flight Delays ◽  
Machine Model ◽  
Forest Model

Delays in flights have negative socio-economics effects on passengers, airlines and airports, resulting to huge economic loses. Therefore, their prediction is crucial during the decision-making process for all players of aviation industry for proper management. The development of accurate prediction models for flight delays depend on the complexity of air transport system and airport infrastructure, hence may be country specific. However, there exists no prediction models tailored to Kenyan aviation industry. Hence there is need to develop prediction models amenable to Kenya aviation conditions. The objective of this study was to compare the prediction power of the developed models. Secondary data from Jomo Kenya International Airport (JKIA) was used in this study. The data collected included the day of the flight (Monday to Sunday), the month (January to December), the airline, the flight class (domestic or international), season (summer or winter), capacity of the aircraft, flight ID (tail number) and whether the flight had flown at night or during the day. The analysis of the data was done using R- software. Three models, Logistic model, Support Vector Machine model and Random Forest model, were fitted. The strength and utility of the models was determined using bias-variance learning curves. The study revealed that the models predicted delays with different accuracies. The Random Forest model had a prediction accuracy of 68.99% while the Support Vector Machine model (SVM) had an accuracy of 68.62% and the Logistic Regression model had an accuracy of 66.18%. The Random Forest model outperformed the SVM and Logistic Regression with accuracies of 0.37% and 2.71% respectively. The SVM and Random Forest do not assume probability distribution of the response under investigation, probably indicating why they performed better than the logistic regression. The study recommends application of Random Forest model to predict flight delays at JKIA.

scholarly journals Heatwave Damage Prediction Using Random Forest Model in Korea

2020 ◽  
Vol 10 (22) ◽  
pp. 8237
Author(s):  
Minsoo Park ◽  
Daekyo Jung ◽  
Seungsoo Lee ◽  
Seunghee Park
Keyword(s):  
Random Forest ◽  
Prediction Models ◽  
Mean Squared Error ◽  
Random Forest Model ◽  
Floating Population ◽  
Coefficient Of Determination ◽  
Positioning Systems ◽  
Forest Model ◽  
Population Variable ◽  
Proposed Model

Climate change increases the frequency and intensity of heatwaves, causing significant human and material losses every year. Big data, whose volumes are rapidly increasing, are expected to be used for preemptive responses. However, human cognitive abilities are limited, which can lead to ineffective decision making during disaster responses when artificial intelligence-based analysis models are not employed. Existing prediction models have limitations with regard to their validation, and most models focus only on heat-associated deaths. In this study, a random forest model was developed for the weekly prediction of heat-related damages on the basis of four years (2015–2018) of statistical, meteorological, and floating population data from South Korea. The model was evaluated through comparisons with other traditional regression models in terms of mean absolute error, root mean squared error, root mean squared logarithmic error, and coefficient of determination (R2). In a comparative analysis with observed values, the proposed model showed an R2 value of 0.804. The results show that the proposed model outperforms existing models. They also show that the floating population variable collected from mobile global positioning systems contributes more to predictions than the aggregate population variable.

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