Tree Model Research Articles

Prediction of Summer Precipitation via Machine Learning with Key Climate Variables：A Case Study in Xinjiang, China

Study region: Xinjiang is located in the mid-latitude region of Eurasia in northwestern China. Precipitation is predominantly concentrated in northern Xinjiang, while southern Xinjiang remains comparatively arid. Summer precipitation accounts for 54.4% of the annual total. Study focus: This study aims to develop a machine learning model to predict summer precipitation (June–August) in XJ and explore the key variables contributing to summer precipitation in this region. The SHapley Additive exPlanations method was integrated with an extreme tree model to quantify the contributions of variables towards precipitation. Artificial neural networks, support vector machines, and extreme gradient boosting were considered to predict summer precipitation. To train the ML model, we used precipitation data from 1961 to 2012, whilst the forecast results from 2013 to 2017 were used for validation.New hydrological insights for the regions: The results demonstrated that the ANN model achieved robust performance during both the training and validation periods. For Northern and Southern XJ, the Mean Absolute Error and Root Mean Square Error of the ANN model were 15.34 (20.40) and 23.21 (30.01), respectively. The SHAP analysis showed that in the context of Northern Xinjiang, the Niño B Sea Surface Temperature Anomaly, Western Pacific Subtropical High Intensity, Pacific Subtropical High Intensity, and Multivariate ENSO Index play crucial roles in the prediction of summer precipitation. In Southern Xinjiang, the South China Sea Subtropical High Intensity, South China Sea Subtropical High Area, Western Pacific Warm Pool Strength, and Atlantic multidecadal oscillation have emerged as key variables affecting summer precipitation forecasting.

Surface temperature change amplifies social inequality in heat exposure? Evidence from Shanghai, China

Heat-related environmental justice has gained increasing attention, with many studies focusing on social inequalities in exposure to extreme surface temperatures. However, changes in land surface temperature (LST) also contribute to residents’ thermal discomfort, underscoring the need to explore inequalities in exposure to these changes. This study addresses this gap by analyzing spatial and social disparities in exposure to both LST changes and summer LST in Shanghai. We employed a boosted regression tree model to quantify LST changes, offering better predictive performance than traditional linear regression, and used geographically weighted regression with data from China's sixth population census to assess the city-wide and local scale inequality differences in exposure to LST changes and summer LST. Our findings reveal that 57.83% of Shanghai's subdistricts experienced above-average LST changes and summer LST. Specific groups—males (22.17%), children (22.61%), the elderly (13.04%), and residents in older housing (60.87%)—were disproportionately exposed to local-scale LST changes. In contrast, those employed in agriculture, forestry, and fisheries faced consistently lower exposure. Furthermore, exposure to LST changes was 3.47% higher among the elderly and 28.26% higher for residents in older housing compared to exposure based on summer LST alone. To mitigate these inequalities, we recommend increasing water coverage and green roofs, enhancing green infrastructure in aging neighborhoods, and providing financial subsidies for outdoor workers. These findings emphasize the importance of addressing social inequalities in exposure to temperature changes to enhance urban resilience and promote sustainable urban planning in the face of climate change.

A novel controllability method on temporal networks based on tree model

A novel controllability method on temporal networks based on tree model

The relative importance of herbicide use for conservation tillage adoption by U.S. corn and soybean producers

Herbicide use is widespread in agricultural production to control weeds prior to and after planting and to “burndown” weeds in the spring for conservation tillage. Whether conservation tillage adoption leads to higher herbicide usage has been a question of policy relevance for decades in the United States. Older U.S. studies using standard statistical and economic techniques have not consistently demonstrated higher herbicide usage levels among producers practicing conservation tillage, but these studies did not fully account for other practices, economic, or agronomic factors. To provide a more timely and comprehensive understanding of the importance of herbicides to conservation tillage, this study achieves two objectives with the most recent, nationally representative data from the US Department of Agriculture. First, it describes trends and compares conservation tillage and herbicide usage among field corn and soybean producers, similar to previous studies using standard economic techniques. Second, a Classification and Regression Tree (CART) model is employed—a novel methodology relative to previous studies that offers distinct advantages over traditional regression modeling—to evaluate the importance of herbicide use for conservation tillage adoption while accounting for other factors. Pairwise mean comparisons for field corn and soybeans indicated that herbicide usage pre-emergence was significantly higher with conservation tillage, but there was no consistent, significant differences in herbicide usage post-emergence. The CART analysis (with prediction accuracy ranging from 68–72%) also showed that pre-emergent use of glyphosate was the strongest predictor (with predicted probabilities from 0.83–0.86) of conservation tillage for field corn in 2016 and soybeans in 2018. Other factors such as the use of crop rotations, highly erodible land, region, and farm size were also strong predictors of conservation tillage. These findings highlight the importance and complexity of herbicide use in the adoption of conservation tillage for U.S. field corn and soybeans.

Comparative Analysis of ML Models with Selection Methods for Early Predictive Analytics of Sepsis in ICU

Objectives: This study aims to make early prediction of Sepsis using ML algorithms and provide comparative analysis of different feature selection techniques. Methods: In this study, the Physionet website dataset has been used. Sepsis categorization is done on the basis of Sepsis-3 criteria. The dataset used is highly imbalanced, with only 2% of the data belonging to Sepsis. The SmoteTomek technique is used to handle an imbalanced dataset. Various filter, embedded, and wrapper feature selection techniques, like tree-based feature selection technique, recursive feature elimination (RFE), information gain, Bhattacharya distance, lasso, etc., have been applied for the top-performing classification ML models. These selection techniques were applied to RandomForest, K Nearest Neighbors (KNN), and Decision Tree models. We compared the impact of these selection techniques on the aforesaid machine learning models. Findings: After applying the RFE technique, the Area Under the Receiver Operating Characteristic curve (AUROC) score of the RandomForest model has slightly increased from 0.996 to 0.9974. KNN model with a tree-based feature selection technique showed the highest sensitivity of 0.934. which is slightly higher than the sensitivity of 0.922, which was without applying any feature selection technique. Along with the AUROC score, the highest performance, in terms of specificity (0.9976), accuracy (0.9959), and f-measure score (0.9062), is achieved when RFE is applied to the RandomForest model. The best selection algorithm for decision trees and KNN is the tree-based selection technique. RFE is the best selection technique for RandomForest. Novelty: In this research, the AUROC score is slightly increased to 0.9974, which has not been achieved yet. Instead of 40, the number of features chosen is 20. This research also provides a comparison of different feature selection techniques like tree-based feature selection, information gain, Bhattacharya, and RFE. It also analyses their impact on the performance of models, which has not been done yet with the same set of selection techniques. Keywords: Sepsis, Machine Learning, Feature selection, Early prediction, Predictive Analytics

Evaluation and Acceptance of Open-Source 3D-Printed Tracheobronchial Tree Models for Improving Pediatric Lung Isolation Techniques Training

Evaluation and Acceptance of Open-Source 3D-Printed Tracheobronchial Tree Models for Improving Pediatric Lung Isolation Techniques Training

Comparative Analysis of Machine Learning Models for Predicting Rice Yield: Insights from Agricultural Inputs and Practices in Rwanda

Food security is a global challenge, especially in developing countries like Rwanda. With a growing population and limited agricultural land, Rwanda struggles to meet increasing food demands. Rice is a staple food crop in Rwanda, playing a crucial role in the country’s food security. However, factors like climate variability, soil nutrient management, and limited access to high-quality inputs hinder rice yield optimization. This paper investigates the most effective machine learning model for predicting rice crop yield in Rwanda, using agricultural inputs and practices. The study used secondary datasets from the National Institute of Statistics of Rwanda (NISR) for rice yield prediction. Eight supervised machine learning algorithms were used, including Linear Regression, Random Forest, Gradient Boosting, Support Vector Machine, Artificial Neural Network, eXtreme Gradient Boosting Tree, and AdaBoost. The models were evaluated based on their accuracy in predicting rice yields, with RMSE, MAE, and Relative Error as primary metrics. The feature importance analysis was also conducted to identify significant factors influencing yield predictions. The study’s findings revealed that the Adaptive Boosting Tree model outperformed the other machine learning models in predicting rice yield. This model achieved RMSE, MAE and Relative Error of 0.69, 0.46 and 12.4%, respectively, indicating a high level of predictive accuracy. The feature importance analysis further highlighted the key factors that contributed to rice yield predictions, with the quantity of inorganic fertilizer, degree of erosion, season, and seed type emerging as the most influential variables. The study demonstrates the effectiveness of machine learning models, particularly the Adaptive Boosting Tree, in improving rice yield predictions and highlighting the crucial role of agricultural inputs like fertilizer and seed type, in influencing crop yields. The output from this study will help the farmers and stakeholders to make data-driven decisions about resource use and crop management.

Cost-effectiveness of high-dose vitamin D supplementation to reduce the occurrence of repeat episodes of pneumonia in children.

Despite the growing evidence on efficacy, little is known regarding the efficiency of Vitamin D supplementation (VDS) to reduce the incidence of repeat episodes of pneumonia in children. This study aimed to determine the cost-utility of VDS to reduce the incidence rate of repeat episodes of pneumonia in children. A decision tree model was used to estimate the cost and quality-adjusted life-years (QALYs) of VDS in a patient aged 2 to 60 months with a clinical diagnosis of community-acquired pneumonia requiring in-hospital management. Multiple sensitivity analyses were conducted to evaluate the robustness of the model. Cost-effectiveness was evaluated at a willingness-to-pay (WTP) value of US$5180. The QALYs per person were 0,93 with VDS and 0,92 without VDS. The total costs per person were US$ 1148 for VDS and US$ 1284 without VDS. This position of absolute dominance of VDS makes it unnecessary to estimate the incremental cost-effectiveness ratio. The ICER was sensitive to changes in the relative risk of Vitamin D supplementation. In conclusion, our study shows that VDS is a cost-effective to reduce the incidence rate of repeat episodes of pneumonia in children, from a societal perspective. However, the sensitivity of ICER to relative risk makes it necessary to take the results of this economic evaluation with caution.

Prediction of acute respiratory infections using machine learning techniques in Amhara Region, Ethiopia.

Many studies have shown that infectious diseases are responsible for the majority of deaths in children under five. Among these children, Acute Respiratory Infections is the most prevalent illness and cause of death worldwide. Acute respiratory infections continue to be the leading cause of death in developing countries, including Ethiopia. In order to predict the main factors contributing to acute respiratory infections in the Amhara regional state of Ethiopia, a machine learning technique was employed. This study utilized data from the 2016 Ethiopian Demographic and Health Survey. Seven machine learning models, including logistic regression, random forests, decision trees, Gradient Boosting, support vector machines, Naïve Bayes, and K-nearest neighbors, were employed to forecast the factors influencing acute respiratory infections. The accuracy of each model was assessed using receiver operating characteristic curves and various metrics. Among the seven models used, the Random Forest algorithm demonstrated the highest accuracy in predicting acute respiratory infections, with an accuracy rate of 90.35% and Area under the Curve of 94.80%. This was followed by the Decision Tree model with an accuracy rate of 88.69%, K-nearest neighbors with 86.35%, and Gradient Boosting with 82.69%. The Random Forest algorithm also exhibited positive and negative predictive values of 92.22% and 88.83%, respectively. Several factors were identified as significantly associated with ARI among children under five in the Amhara regional state, Ethiopia. These factors, included families with a poorer wealth status (log odds of 0.18) compared to their counterparts, families with four to six children (log odds of 0.1) compared to families with fewer than three living children, children without a history of diarrhea (log odds of -0.08), mothers who had occupation(log odds of 0.06) compared mothers who didn't have occupation, children under six months of age (log odds of -0.05) compared to children older than six months, mothers with no education (log odds of 0.04) compared to mothers with primary education or higher, rural residents (log odds of 0.03) compared to non-rural residents, families using wood as a cooking material (log odds of 0.03) compared to those using electricity. Through Shapley Additive exPlanations value analysis on the Random Forest algorithm, we have identified significant risk factors for acute respiratory infections among children in the Amhara regional state of Ethiopia. The study found that the family's wealth index, the number of children in the household, the mother's occupation, the mother's educational level, the type of residence, and the fuel type used for cooking were all associated with acute respiratory infections. Additionally, the research emphasized the importance of children being free from diarrhea and living in households with fewer children as essential factors for improving children's health outcomes in the Amhara regional state, Ethiopia.

Application of Machine Learning to Predict the Capacity of Fractured Horizontal Wells in Shale Reservoirs

Shale oil wells typically have numerous volume fracturing segments in their horizontal sections, resulting in significant variability in productivity across these segments. Conventional productivity prediction and fracturing effect evaluation methods are challenging to apply effectively. Establishing a stable and efficient intelligent productivity prediction method using machine learning is a promising approach for the effective development of shale oil reservoirs. This study is based on geological data, fracturing records, and a production database of 91 production wells in a shale oil reservoir in a specific area. Fourteen key parameters affecting productivity were selected from geological and engineering perspectives, and the recursive feature elimination method based on support vector machines identified five optimal main controlling factors. Three machine learning methods—decision tree, random forest, and gradient boosting decision tree (GBDT)—were used to model productivity prediction, with root mean square error (RMSE) employed to evaluate model performance. The study results indicate that formation coefficient, cluster spacing, treatment volume, sand volume, and fracturing segment length are the main controlling factors influencing productivity in fractured horizontal wells. Among the models, the random forest algorithm with bootstrap sampling produced the most stable prediction results, achieving a prediction accuracy of 94% and an RMSE of 0.934 on the test set, outperforming the decision tree and GBDT models in terms of minimum RMSE on the test set.

Analyzing Autonomous Vehicle Collision Types to Support Sustainable Transportation Systems: A Machine Learning and Association Rules Approach

The increasing presence of autonomous vehicles (AVs) in transportation, driven by advances in AI and robotics, requires a strong focus on safety in mixed-traffic environments to promote sustainable transportation systems. This study analyzes AV crashes in California using advanced machine learning to identify patterns among various crash factors. The main objective is to explore AV crash mechanisms by extracting association rules and developing a decision tree model to understand interactions between pre-crash conditions, driving states, crash types, severity, locations, and other variables. A multi-faceted approach, including statistical analysis, data mining, and machine learning, was used to model crash types. The SMOTE method addressed data imbalance, with models like CART, Apriori, RF, XGB, SHAP, and Pearson’s test applied for analysis. Findings reveal that rear-end crashes are the most common, making up over 50% of incidents. Side crashes at night are also frequent, while angular and head-on crashes tend to be more severe. The study identifies high-risk locations, such as complex unsignalized intersections, and highlights the need for improved AV sensor technology, AV–infrastructure coordination, and driver training. Technological advancements like V2V and V2I communication are suggested to significantly reduce the number and severity of specific types of crashes, thereby enhancing the overall safety and sustainability of transportation systems.

Public health and economic impact of COVID-19 vaccination with the Pfizer-BioNTech COVID-19 Vaccine, KP.2, among adults in 2024/2025 in the United States

ABSTRACT Objectives To assess the public health and economic impact of vaccination with the Pfizer-BioNTech COVID-19 vaccine, KP.2, in 2024/2025 in the United States. Methods A combined cohort Markov-decision tree model was used to estimate the cost-effectiveness and budget impact of vaccination versus no vaccination in adults aged ≥18 years. The adult population was further stratified into 18-64 years and ≥65 years age groups. Public health surveillance data informed the annual proportion of individuals infected with and vaccinated against SARS-CoV-2 and the proportion of those infected who were hospitalized, stratified by age. Other age-stratified clinical, cost, and vaccine effectiveness parameters were informed by literature. The budget impact analysis was based on a hypothetical 1-million-member plan and used a payer perspective. Parameter uncertainty was tested in sensitivity analyses. Results Without vaccination, the model projected 21.4 million new symptomatic cases, 15,793 deaths, 629,098 hospitalizations, $115.5 billion in total costs, and 1 million QALYs lost among adults aged ≥18 years, with the greatest health burden observed among older adults aged ≥65 years (72% of hospitalizations and 86% of deaths) and the greatest economic burden in adults aged 18 to 64 years (62% of total costs). Vaccination was projected to prevent 915,501 cases, 1,057 deaths, and 37,489 hospitalizations, resulting in cost savings of $513 million, 9,173 LYs gained, 56,482 QALYs gained, and a dominant ICER. In the budget impact analysis, vaccination was estimated to result in total incremental cost savings for the population aged ≥ 65 years and a modest budget increase for the population aged 18 to 64 years. Conclusions Vaccination with the Pfizer BioNTech COVID-19 vaccine, KP.2, is a cost-effective measure from the societal perspective at a willingness-to-pay threshold of $50,000 that could reduce the health and economic burden of COVID-19.

Cost-effectiveness of diagnostic technologies for mycobacterium tuberculosis infection in India and Brazil.

The economic value of new skin-based tests and blood-based interferon-γ release assays (IGRAs) for tuberculosis (TB) infection is not yet well-established. This study evaluates the cost and cost-effectiveness in two high-burden countries by comparing:(a) new skin-based tests(Diaskintest and Cy-Tb) with the purified protein derivative (PPD)-tuberculin test (TST);(b) IGRAs (Standard E TB-Feron ELISA (TBF))with approved IGRAs (QuantiFERON-TB Gold Plus (QFT-GP)and TSPOT.TB); and (c) the best performing skin-based test with the best performing IGRA) based on cost effectiveness. In this paper, we developed a decision tree model for India and Brazil from a health system perspective. To quantify the effect of parameter variability and uncertainty, we performed both univariate and probabilistic sensitivity analysis. The study findings reveal that among skin-based tests, the Diaskintest is more cost-effective compared to TST-PPD at 22.6 USD and 41.0 USD per correctly diagnosed case of TB infection for Brazil and India, respectively. For blood-based assays, TSPOT.TB outperforms QFT-GP and TBF due to its lower cost and higher effectiveness. When compared with Diaskintest, TSPOT.TB has an incremental cost of approximately 8 USD and 6 USD for India and Brazil respectively but is more effective. The incremental cost-effectiveness ratio (ICER) was 74 USD and 55 USD for India and Brazil, respectively. In summary, while Diaskintest is potentially cost-saving when compared to TSPOT.TB in these two high-burden TB countries but the TSPOT.TB demonstrates higher effectiveness.

Abstract Su804: Use of Machine Learning Models to Predict Neurologically Intact Survival for Advanced Age Adults Following Out-of-hospital Cardiac Arrest

Background: We recently published a multivariable logistic regression model that successfully predicted neurologic outcomes in advanced age adults (≥ 65 years old) who achieve return of spontaneous circulation following out-of-hospital cardiac arrest (OHCA). Prior to externally validating this model, we sought to compare the predictive performance of our logistic regression model against common machine learning (ML) algorithms using the same dataset. Methods: We performed a retrospective observational analysis of the Cardiac Arrest Registry to Enhance Survival database from 2013-2021. All non-traumatic OHCA occurring in adults (≥65 years) who survived to hospital admission were included. The primary outcome measure was neurologically intact survival defined as a cerebral performance category (CPC) score of 1 or 2 at hospital discharge. Our original logistic regression model was compared to Boosted Trees and Decision Tree ML algorithms and assessed using accuracy, Area Under the Receiver Operating Characteristic Curve (AUC-ROC) and Area Under the Precision-Recall Curve (AUC-PR). The model variables included age, gender, race/ethnicity, location, witnessed status, who initiated cardiopulmonary resuscitation, whether an automated external defibrillator was applied prior to emergency medical services arrival, and first monitored rhythm. Results: A total of 83,561 OHCA were included. Median age was 75 years (IQR 69-82), 58.9% were male, 53% were White, 67% experienced an OHCA at home, 52% were witnessed by a bystander, 44% received bystander CPR, and 34% were found in shockable rhythm. Neurologically intact survival (CPC 1 or 2) occurred in 23% of patients. The logistic regression model had an accuracy of 79.6%, AUC-PR 0.542, and AUC-ROC of 0.774. The Boosted Trees model had an accuracy of 79.2%, AUC-PR 0.555, and AUC-ROC of 0.774. The Decision Tree Model had an accuracy of 79.3%, AUC-PR 0.490, and AUC-ROC of 0.717. Conclusion: Boosted Trees and Decision Tree ML models performed comparably to the logistic regression model in predicting favorable versus unfavorable neurological outcomes following successful resuscitation from OHCA in advanced-age adults. Given their similar performance, logistic regression may be preferred for its simplicity and interpretability over alternative ML models. Future efforts to improve and ultimately externally validate the model are warranted.

Analysing crash severity on expressways in India using statistical and ML approaches

The high injury severity of traffic crashes on Indian expressways is a significant concern for road safety experts, although studies dedicated to this critical issue are limited. The present study investigates the factors influencing crash severity using multinomial logit (MNL), decision tree (DT) and random forest (RF) models on a dataset of 2,747 crashes on three selected expressways. The dependent variable, crash severity, had four injury severity categories: fatal, severe, minor, and property damage only (PDO). Various explanatory variables, included traffic and speed characteristics, temporal and geometric characteristics, primary contributing factors, crash type, and the vehicles involved. Synthetic minority oversampling (SMOTE) and randomise class balancing (RCB) techniques were also employed to tackle the class imbalance issue in the dataset. The predictive performance of models was evaluated using classification accuracy and Kappa value. The RF model showed the highest predictive accuracy on the RCB dataset. The key findings highlight the critical need for enforcement of speed limits and entry restrictions, lane discipline, and improvement of design deficiencies such as provisions of truck laybys and bus bays. These measures can help in policy-making and engineering improvements to enhance road safety on expressways in India.

The established of a machine learning model for predicting the efficacy of adjuvant interferon alpha1b in patients with advanced melanoma

BackgroundInterferon-alpha1b (IFN-α1b) has shown remarkable therapeutic potential as adjuvant therapy for melanoma. This study aimed to develop five machine learning models to evaluate the efficacy of postoperative IFN-α1b in patients with advanced melanoma.MethodsWe retrospectively analyzed 113 patients with the American Joint Committee on Cancer (AJCC) stage III-IV melanoma who received postoperative IFN-α1b therapy between July 2009 and February 2024. Recurrence-free survival (RFS) and overall survival (OS) were assessed using Kaplan-Meier analysis. Five machine learning models (Decision Tree, Cox Proportional Hazards, Random Forest, Support Vector Machine, and LASSO regression) were developed and compared for their capacity to predict the outcomes of patients. Model performance was evaluated using concordance index (C-index), time-dependent receiver operating characteristic (ROC) curves, and decision curve analysis.ResultsThe 1-year, 2-year, and 3-year RFS rates were 71.10%, 43.10%, and 31.10%, respectively. For OS, the 1-year, 2-year, and 3-year OS rates were 99.10%, 82.30%, and 75.00%, respectively. The Decision Tree (DT) model demonstrated superior predictive performance with the highest C-index of 0.792. Time-dependent ROC analysis for predicting 1-, 2-, and 3-year RFS based on the DT model is 0.77, 0.79 and 0.76, respectively. Serum albumin emerged as the important predictor of RFS.ConclusionsOur study demonstrates the considerable efficacy DT model for predicting the efficacy of adjuvant IFN-α1b in patients with advanced melanoma. Serum albumin was identified as a key predictive factor of the treatment efficacy.

Site-specific effects of fertilizer on hay and grain yields of oats: evidence from large-scale field experiments.

Oat (Avena sativa L.) is a valuable crop due to its strong adaptability to marginal environments, making it an important component of agricultural systems in regions where other cereals may not thrive. The application of chemical fertilizer can influence oat hay and grain yield significantly. However, large-scale meta-analytical studies of the size and variability of oat hay and grain yields in response to fertilizer addition are still lacking. Based on 83 studies worldwide, this meta-analysis quantifies the impact of the addition of fertilizer on oat hay and grain yields under varying environmental conditions (e.g., soil nutrient levels, texture, and climate). The results confirmed that the fertilizer application increased oat hay yield by 48.9% and grain yield by 36.2%. This study demonstrated that balanced fertilization with nitrogen, phosphorus, and potassium generally enhances oat hay and grain yield despite large temporal and spatial variations. Boosted regression tree (BRT) models suggest that changes in hay and grain yield were primarily dominated by soil pH and nitrogen fertilizer. The response ratio (the natural logarithm of the mean values of hay yield or grain yield with and without fertilization, respectively) of hay yield declined linearly with soil pH. Elevation was the second most important factor affecting the change in response ratio of hay yield and the third most important factor affecting the change in response ratio of grain yield but climatic conditions were not the dominant factors affecting changes in oat hay or grain yield. Overall, these results will benefit producers considering site-specific fertilization management of oat. They could increase yields and save investment in fertilizer, and help to facilitate the genetic breeding of oat varieties with high nutrient use efficiency. © 2024 The Author(s). Journal of the Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

Clinical Benefits of Sustained Oral Nirmatrelvir/Ritonavir Use for the Outpatient Treatment of COVID-19: Findings from the Taiwanese Health Authority Perspective Using a Decision Tree Modeling Approach

Despite the observed clinical benefits of nirmatrelvir/ritonavir (NMV/r), it is uncertain whether Taiwan will continue covering NMV/r for high-risk individuals with mild-to-moderate coronavirus disease 2019 (COVID-19). This analysis assessed the impact of sustained utilization of NMV/r on COVID-19-associated healthcare resource utilization (HCRU) and mortality from the Taiwanese health authority perspective (THAP). A decision tree model estimated the incremental number of clinical events associated with NMV/r utilization over a 30-day period. Model results compared (1) a base case using current rates of NMV/r from the THAP, and (2) a hypothetical scenario assuming the current supply of NMV/r is not extended in Taiwan. NMV/r utilization rates included 80% and 0% in the base case and hypothetical scenario, respectively. Outcomes included the number of hospitalizations involving a general ward (GW) stay, intensive care unit (ICU) stay, and mechanical ventilation (MV) use, as well as the number of bed days, symptom days, and hospitalization deaths. Based on epidemiologic data, 150,255 patients with COVID-19 were eligible for treatment from the THAP. In the hypothetical scenario, HCRU increased by 175% compared to the base case, including increases in hospitalizations involving GW, ICU, and MV use (differences: 2067; 623; 591, respectively), bed days (difference: 51,521), symptom days (difference: 51,714), and deaths (difference: 480). Findings indicate that sustained utilization of NMV/r from the THAP reduces the clinical burden of mild-to-moderate COVID-19 through the reduced incidence of COVID-19-related HCRU and deaths.

Using a Stopping Rule to Optimize Cost-Quality Tradeoffs in a Large, Mixed-Mode Survey: A Simulation Study

Rising nonresponse and the increasing costs of conducting surveys are creating pressure on survey organizations to efficiently allocate resources. The challenge is to produce the highest quality data possible within a fixed budget. We use a stopping rule designed to minimize a function of cost and errors. The rule is based on the product of predicted costs and the predicted mean squared error of a survey estimate. We simulate the impact of implementing the stopping rule on the 2019 US Survey of Doctorate Recipients (SDR), which is a longitudinal survey conducted every two years in the US using web, mail, and CATI. We vary the types of models used to generate the input predictions (parametric regression vs. nonparametric tree models) and the timing of the implementation of the rule. We found that the modeling approach made less difference, while the timing of the implementation of the stopping rule made a large difference in outcomes. The rule is multivariate and optimizes outcomes for two variables. It performed better for one variable, leading to reduced costs and only small increases in errors. The other variable had larger error increases.

Advancing user classification models: A comparative analysis of machine learning approaches to enhance faculty password policies at the University of Buraimi

In this paper, we assess the results of experiment with different machine learning algorithms for the data classification on the basis of accuracy, precision, recall and F1-Score metrics. We collected metrics like Accuracy, F1-Score, Precision, and Recall: From the Neural Network model, it produced the highest Accuracy of 0.129526 also highest F1-Score of 0.118785, showing that it has the correct balance of precision and recall ratio that can pick up important patterns from the dataset. Random Forest was not much behind with an accuracy of 0.128119 and highest precision score of 0.118553 knit a great ability for handling relations in large dataset but with slightly lower recall in comparison with Neural Network. This ranked the Decision Tree model at number three with a 0.111792, Accuracy Score while its Recall score showed it can predict true positives better than Support Vector Machine (SVM), although it predicts more of the positives than it actually is a majority of the times. SVM ranked fourth, with accuracy of 0.095465 and F1-Score of 0.067861, the figure showing difficulty in classification of associated classes. Finally, the K-Neighbors model took the 6th place, with the predetermined accuracy of 0.065531 and the unsatisfactory results with the precision and recall indicating the problems of this algorithm in classification. We found out that Neural Networks and Random Forests are the best algorithms for this classification task, while K-Neighbors is far much inferior than the other classifiers.