Important Predictor Variables Research Articles

Novel decision tree models predict the overall survival of patients with submandibular gland cancer.

While the accurate prediction of the overall survival (OS) in patients with submandibular gland cancer (SGC) is paramount for informed therapeutic planning, the development of reliable survival prediction models has been hindered by the rarity of SGC cases. The purpose of this study is to identify key prognostic factors for OS in SGC patients using a large database and construct decision tree models to aid the prediction of survival probabilities in 12, 24, 60 and 120 months. We performed a retrospective cohort study using the Surveillance, Epidemiology and End Result (SEER) program. Demographic and peri-operative predictor variables were identified. The outcome variables overall survival at 12-, 24-, 60, and 120 months. The C5.0 algorithm was utilized to establish the dichotomous decision tree models, with the depth of tree limited within 4 layers. To evaluate the performances of the novel models, the receiver operator characteristic (ROC) curves were generated, and the metrics such as accuracy rate, and area under ROC curve (AUC) were calculated. A total of 1,705, 1,666, 1,543, and 1,413 SGC patients with a follow up of 12, 24, 60 and 120 months and exact survival status were identified from the SEER database. Predictor variables of age, sex, surgery, radiation, chemotherapy, tumor histology, summary stage, metastasis to distant lymph node, and marital status exerted substantial influence on overall survival. Decision tree models were then developed, incorporating these vital prognostic indicators. Favorable consistency was presented between the predicted and actual survival statuses. For the training dataset, the accuracy rates for the 12-, 24-, 60- and 120-month survival models were 0.866, 0.767, 0.737 and 0.797. Correspondingly, the AUC values were 0.841, 0.756, 0.725, and 0.774 for the same time points. Based on the most important predictor variables identified using the large, SEER database, decision tree models were established that predict OS of SGC patients. The models offer a more exhaustive evaluation of mortality risk and may lead to more personalized treatment strategies.

Machine learning techniques in classifying satisfaction with the economy of Latin American Citizens

Applications of machine learning algorithms offer a new perspective for exploring data from opinion polls, aiming to better understand perceptions and population profiles on various topics, including satisfaction with the economy. In this context, this research aimed to apply machine learning algorithms to classify economic satisfaction from citizens in Latin American countries, evaluating their performance on different datasets and analyzing the variables contributing most to this theme. Four base algorithms were applied: Random Forest; Gradient Boosting; XGBoost; and Naïve Bayes, along with two ensemble methods: hard voting; and soft voting. Subsequently, variable importance was assessed using Gradient Boosting and Random Forest methods, showing the contribution of the predictors to the economic satisfaction. The results showed an accuracy of approximately 86% for three of the base classifiers and soft voting but all methods performed better in classifying dissatisfied citizens, struggling to recognize patterns of those satisfied with the economy. The most important predictor variables considered for classifying this satisfaction were "Satisfaction with democracy" and "Comparison with the economy 12 months before".

Machine learning approach for prediction of outcomes in anticoagulated patients with atrial fibrillation

Abstract Background Despite the availability of different risk scores, the accuracy of actual prediction tools for outcomes in patients with atrial fibrillation (AF) remains modest [1]. Although Machine Learning (ML) has been used to predict outcomes in the AF population, evidences about outcome prediction in a population entirely on oral anticoagulation is lacking. The aim of this study is to use ML to predict outcomes in anticoagulated patients with atrial fibrillation, processing data from the Italian AF START-2 Register. Methods and aims Different ML models were applied to predict all-cause death, cardiovascular (CV) death, major bleeding and stroke in anticoagulated patients with nonvalvular AF. Overall population, Vitamin K Antagonists (VKA) and Direct Oral Anticoagulants (DOACs) populations were considered for the analyses. Results 11078 AF patients (male n=6029, 54.3%) were enrolled with a median follow-up period of 1.5 years [IQR 1.0-2.6]. Patients on VKA were 5135 (46.4%), while 5943 (53.6%) were on DOACs. During the follow-up, 785 patients died, of which 169 (21.6%) due to CV causes; 240 major bleeding events were recorded, and 50 strokes occurred. Using Multi-Gate Mixture of Experts (MmoE), a cross-validated AUC of 0.779 ± 0.016 and 0.745 ± 0.022 were obtained, respectively, for the prediction of all-cause death and CV death in the overall population. The best ML model outperformed CHA2DSVA2SC and HAS-BLED for all-cause death prediction (p<0.001 for both). When compared to HAS-BLED, Gradient Boosting improved major bleeding prediction in DOACs patients compared to HAS-BLED score (0.711 vs. 0.586, p<0.001). Body mass index, age, glomerular filtration rate, platelet count and hemoglobin levels resulted the most important variables for ML prediction. Conclusions In patients with AF, ML models showed good discriminative ability to predict all-cause death, regardless of the type of anticoagulation strategy, and major bleeding on DOAC therapy, outperforming CHA2DS2VASC and the HAS-BLED scores for risk prediction in these populations. Anemia, platelet count, and BMI emerged as new potential risk predictors in anticoagulated AF patients. The applications of ML prediction models in clinical practice could improve the risk assessment and subsequent management of anticoagulated patients with AF.Graphical AbstractFeature importance for Bleeding in DOACs

Comparison of traditional regression modeling vs. AI modeling for the prediction of dental caries: a secondary data analysis

IntroductionThere are substantial gaps in our understanding of dental caries in primary and permanent dentition and various predictors using newer modeling methods such as Machine Learning (ML) algorithms and Artificial Intelligence (AI). The objective of this study is to compare the accuracy, precision, and differences between the caries predictive capability of AI vs. traditional multivariable regression techniques.MethodsThe study was conducted using secondary data stored in the Temple University Kornberg School of Dentistry electronic health records system (axiUm) of pediatric patients aged 6–16 years who were patients on record at the Pediatric Dentistry Clinic. The outcome variables considered in the study were the decayed–missing–filled teeth (DMFT) and the decayed–extracted–filled teeth (deft) scores. The predictors included age, sex, insurance, fluoride exposure, having a dental home, consumption of sugary meals, family caries experience, having special needs, visible plaque, medications reducing salivary flow, and overall assessment questions.ResultsThe average DMFT score was 0.85 ± 2.15, while the average deft scores were 0.81 ± 2.15. For childhood dental caries, XGBoost was the best performing ML algorithm with accuracy, sensitivity. and Kappa as 81%, 84%, and 61%, respectively, followed by Support Vector Machine and Lasso Regression algorithms, both with 84% specificity. The most important variables for prediction found were age and visible plaque.ConclusionsThe machine learning model outperformed the traditional statistical model in the prediction of childhood dental caries. Data from a more diverse population will help improve the quality of caries prediction for permanent dentition where the traditional statistical method outperformed the machine learning model.

#460 Digital twin in hemodialysis model, with machine learning algorithms. Data from the Andalusian Hemodialysis Registry. SICATA

Abstract Background and Aims Orthodoxly, a digital twin is a computer simulation of an individual, “in silico”, and in real time. A digital twin can also be created with BIG DATA. The Objective It is the development of a survival digital twin model using Machine Learning algorithms using data from the Andalusian SICATA registry. Method The database was composed of 29,189 records at the time of analysis and contains variables in the areas of comorbidity, anemia, efficacy of renal replacement therapy (RRT), and renal bone disease. The algorithms used were: - XG Boost, AdaBoost, Cat boost. KNN, Random Forest, Decision Trees, Logistic Regression. A survival model was developed with each Machine Learning (ML) algorithm . The metrics of the models were taken into account: precision, calibration, accuracy. The software used was Orange based on Python and Rattle based on R. An explanation of the “black box” type models was carried out using SHAP (Shapley values) based on game theory. Results The characteristics that the digital twin that we have modeled with the greatest survival would have are: 1. Age equal to or less than 45 years, 2. Being included in the kidney transplant waiting list. 3. Longer duration of the hemodialysis session. 4. Low doses of Erythropoietin. 5. Normal or high serum albumin levels. 6. BMI > 21.The sample was stratified according to the Ultrafiltration (UF) ratio during the Hemodialysis session in ml/kg/hour. Group1: UF <4 ml/kg/hour. Group2: UF 5-9 ml/kg/hour. Group3: UF>9 ml/kg/hour. And a mortality study was carried out using multivariate Logistic Regression. We found that the OR for mortality in groups 2 and 3 was 17% and 24% in relation to group 1, which had the lowest mortality. Conclusion ML has revealed that the UF ratio (ml/kg/hour) is an important predictor variable of mortality. In our studio a UF ratio<4 ml/kg/hour is safe ML has found an answer to a question that we had not asked ourselves when beginning the study: What is the optimal UF rate in the hemodialysis session?

Global drivers of the conservation-invasion paradox.

The conservation-invasion paradox (CIP) refers to a long-term phenomenon wherein species threatened in their native range can sustain viable populations when introduced to other regions. Understanding the drivers of CIP is helpful for conserving threatened species and managing invasive species, which is unfortunately still lacking. We compiled a global data set of 1071 introduction events, including 960 CIP events (successful establishment of threatened species outside its native range) and 111 non-CIP events (unsuccessful establishment of threatened species outside its native range after introduction), involving 174 terrestrial vertebrates. We then tested the relative importance of various predictors at the location, event, and species levels with generalized linear mixed models and model averaging. Successful CIP events occurred across taxonomic groups and biogeographic realms, especially for the mammal group in the Palearctic and Australia. Locations of successful CIP events had fewer native threat factors, especially less climate warming in invaded regions. The probability of a successful CIP event was highest when species introduction efforts were great and there were more local congeners and fewer natural enemies. These results can inform threatened species ex situ conservation and non-native invasive species mitigation.

Predictors of social risk for post-ischemic stroke reintegration

After stroke rehabilitation, patients need to reintegrate back into their daily life, workplace and society. Reintegration involves complex processes depending on age, sex, stroke severity, cognitive, physical, as well as socioeconomic factors that impact long-term outcomes post-stroke. Moreover, post-stroke quality of life can be impacted by social risks of inadequate family, social, economic, housing and other supports needed by the patients. Social risks and barriers to successful reintegration are poorly understood yet critical for informing clinical or social interventions. Therefore, the aim of this work is to predict social risk at rehabilitation discharge using sociodemographic and clinical variables at rehabilitation admission and identify factors that contribute to this risk. A Gradient Boosting modelling methodology based on decision trees was applied to a Catalan 217-patient cohort of mostly young (mean age 52.7), male (66.4%), ischemic stroke survivors. The modelling task was to predict an individual’s social risk upon discharge from rehabilitation based on 16 different demographic, diagnostic and social risk variables (family support, social support, economic status, cohabitation and home accessibility at admission). To correct for imbalance in patient sample numbers with high and low-risk levels (prediction target), five different datasets were prepared by varying the data subsampling methodology. For each of the five datasets a prediction model was trained and the analysis involves a comparison across these models. The training and validation results indicated that the models corrected for prediction target imbalance have similarly good performance (AUC 0.831–0.843) and validation (AUC 0.881 - 0.909). Furthermore, predictor variable importance ranked social support and economic status as the most important variables with the greatest contribution to social risk prediction, however, sex and age had a lesser, but still important, contribution. Due to the complex and multifactorial nature of social risk, factors in combination, including social support and economic status, drive social risk for individuals.

Vertical structure of reef fish assemblages and light penetration reveal new boundaries of mesophotic ecosystems in the subtropical Southwestern Atlantic

In the Southwest Atlantic, fisheries and in situ observations suggest that mesophotic reefs occur further south than has been reported in the literature, and a description of these subtropical regions is still lacking. We used Baited Remote Underwater stereo-Videos to explore unrevealed patterns in the vertical structure of fish assemblages on subtropical Atlantic reefs, contrasting shallow and mesophotic habitats. Our data on species turnover and light penetration reveal that in the subtropical Atlantic, the boundaries between shallow and mesophotic habitats occur at depths of 18 m, which is shallower than most previous studies have shown. Generalized additive mixed models identified different environmental and management factors as important predictor variables that explain distribution of fish assemblages’ attributes. Besides adding new evidence about mesophotic limits, we described variations in the vertical structure of fish assemblages, providing important information for the description and conservation of mesophotic environments in subtropical regions.

Machine learning approach for prediction of outcomes in anticoagulated patients with atrial fibrillation

BackgroundThe accuracy of available prediction tools for clinical outcomes in patients with atrial fibrillation (AF) remains modest. Machine Learning (ML) has been used to predict outcomes in the AF population, but not in a population entirely on anticoagulant therapy. Methods and aimsDifferent supervised ML models were applied to predict all-cause death, cardiovascular (CV) death, major bleeding and stroke in anticoagulated patients with AF, processing data from the multicenter START-2 Register. Results11078 AF patients (male n = 6029, 54.3%) were enrolled with a median follow-up period of 1.5 years [IQR 1.0–2.6]. Patients on Vitamin K Antagonists (VKA) were 5135 (46.4%) and 5943 (53.6%) were on Direct Oral Anticoagulants (DOAC). Using Multi-Gate Mixture of Experts, a cross-validated AUC of 0.779 ± 0.016 and 0.745 ± 0.022 were obtained, respectively, for the prediction of all-cause death and CV-death in the overall population. The best ML model outperformed CHA2DSVA2SC and HAS-BLED for all-cause death prediction (p < 0.001 for both). When compared to HAS-BLED, Gradient Boosting improved major bleeding prediction in DOACs patients (0.711 vs. 0.586, p < 0.001). A very low number of events during follow-up (52) resulted in a suboptimal ischemic stroke prediction (best AUC of 0.606 ± 0.117 in overall population). Body mass index, age, renal function, platelet count and hemoglobin levels resulted the most important variables for ML prediction. ConclusionsIn AF patients, ML models showed good discriminative ability to predict all-cause death, regardless of the type of anticoagulation strategy, and major bleeding on DOAC therapy, outperforming CHA2DS2VASC and the HAS-BLED scores for risk prediction in these populations.

Synergetic Use of Sentinel-1 and Sentinel-2 Data for Wheat-Crop Height Monitoring Using Machine Learning

Monitoring crop height during different growth stages provides farmers with valuable information important for managing and improving expected yields. The use of synthetic aperture radar Sentinel-1 (S-1) and Optical Sentinel-2 (S-2) satellites provides useful datasets that can assist in monitoring crop development. However, studies exploring synergetic use of SAR S-1 and optical S-2 satellite data for monitoring crop biophysical parameters are limited. We utilized a time-series of monthly S-1 satellite data independently and then used S-1 and S-2 satellite data synergistically to model wheat-crop height in this study. The polarization backscatter bands, S-1 polarization indices, and S-2 spectral indices were computed from the datasets. Optimized Random Forest Regression (RFR), Support Vector Machine Regression (SVMR), Decision Tree Regression (DTR), and Neural Network Regression (NNR) machine-learning algorithms were applied. The findings show that RFR (R2 = 0.56, RMSE = 21.01 cm) and SVM (R2 = 0.58, RMSE = 20.41 cm) produce a low modeling accuracy for crop height estimation with S-1 SAR data. The S-1 and S-2 satellite data fusion experiment had an improvement in accuracy with the RFR (R2 = 0.93 and RMSE = 8.53 cm) model outperforming the SVM (R2 = 0.91 and RMSE = 9.20 cm) and other models. Normalized polarization (Pol) and the radar vegetation index (RVI_S1) were important predictor variables for crop height retrieval compared to other variables with S-1 and S-2 data fusion as input features. The SAR ratio index (SAR RI 2) had a strong positive and significant correlation (r = 0.94; p &lt; 0.05) with crop height amongst the predictor variables. The spatial distribution maps generated in this study show the viability of data fusion to produce accurate crop height variability maps with machine-learning algorithms. These results demonstrate that both RFR and SVM can be used to quantify crop height during the growing stages. Furthermore, findings show that data fusion improves model performance significantly. The framework from this study can be used as a tool to retrieve other wheat biophysical variables and support decision making for different crops.

Abstract P274: Beyond MVPA: Machine Learning Insights Into Activity Patterns and Cardiometabolic Health in the National Health and Nutrition Examination Survey (NHANES) 2003-2004

Physical activity (PA) and sedentary behavior (SB) volumes and patterns may be useful in identifying individuals who are at elevated cardiometabolic risk. Identifying individuals at risk will facilitate early interventions for prevention of cardiovascular diseases. The objective of this project is to identify important free-living physical activity patterns to classify cardiometabolic markers including insulin, plasma glucose, and triglyceride. Methods: We used cross-sectional NHANES 2003-2004 accelerometer, blood exam and demographic data. We included adults age ≥18 years who wore a hip-worn ActiGraph 7164 during waking hours for at least 4 days of 10 hours/day of wear time. We included PA and SB predictors including minutes per day (min/d) in sedentary time (0-99 counts per minute, cpm), light intensity PA (100-759 cpm), Lifestyle PA (760-2019 cpm), moderate to vigorous PA (MVPA, ≥2019 cpm), number of breaks in SB, and number of 30-min and 60-min SB, 10-min MVPA bouts etc. The cut points for cardiovascular disease risk were defined based on literature as: fasting serum insulin ≤ 5 μU/mL, plasma glucose ≤ 100 mg/dL and triglyceride &lt; 150 mg/dL. Three random forest (RF) classification models were developed for each cardiometabolic marker using age, body-mass-index, general health, and PA variables as predictors. Models were trained and tested using a 60% training - 40% testing split. We tuned the hyperparameters (final selection: number of trees = 500 and variables = 3) to find the model with the highest testing accuracy based on the out-of-bag error. The importance of each predictor was measured and ranked by the mean decrease in GINI index. Results: The three datasets include insulin (n = 2810, age=34 ±23, 50% females), glucose (n = 1852, age = 49±20, 51% females) and triglyceride (n = 3180, age=36 ±24, 50% females). For insulin model, the PA variables with largest mean decrease in GINI index were SB time, light intensity PA, MVPA, and SB bouts of 30-min. Similarly, time spent in Lifestyle PA, light intensity PA, number of SB breaks and SB bouts of 30-min were the most important variables for abnormal fasting glucose prediction. The number of sedentary breaks, light intensity PA, SB time and SB bouts of 30-min were the most important PA predictors for risk of high triglyceride. Both BMI and age were important demographic variables for prediction of increased risk in each model. The prediction accuracy and precision are as following: Insulin(73%; 76%), Plasma Glucose(68%; 58%), and Triglyceride(64%; 46%). Conclusion: Cardiometabolic markers using free-living PA has the potential to provide insights for classifying the risk for cardiovascular disease based on given physiological marker cut-off points. The time spent lower intensity PA patterns and the number of sedentary breaks, and 30-minutes bouts are important predictors for identifying individuals who may have increased cardiometabolic risk.

Abstract 43: Development and Internal Validation of Risk Scores to Predict Survival to Discharge Following Pediatric In-Hospital Cardiac Arrest

Introduction: In-hospital cardiac arrest (IHCA) in the pediatric population is associated with poor survival and neurological outcomes. We aimed to develop and internally validate a risk score to predict survival to discharge in pediatric IHCA patients. Methods: For model development and validation, we included pediatric IHCA patients captured in the Get With The Guidelines® registry (provided by the American Heart Association) between 2005 and 2021. We used logistic regression (LR), classification and regression trees (CART), and artificial neural networks (ANN) to develop models using 70% of the data and validate them using the remaining 30% of the data. The discrimination of the models was compared based on the area under the receiver operating characteristic curve (AUC), calibration based on the ratio of observed to expected (O: E), and predictive accuracy based on percent survival in each risk group. Results: We included 6141 pediatric patients (mean age 4.82 years, 41.3% infants, 54.7% male). Most patients survived to hospital discharge (60.9%). We developed separate models for infants vs. older children using LR, CART, and ANN. The least absolute shrinkage and selection operator (LASSO) identified age, illness category, acyanotic cardiac malformation, hepatic insufficiency, hypotension/hypoperfusion, metabolic/electrolyte abnormality, metastatic/hematologic malignancy, renal insufficiency, septicemia, and pediatric cerebral performance score on admission as important predictor variables for analysis. Table 1 shows a comparison of model parameters. Logistic regression models were found to be better at classifying survivors into risk categories with higher specificity and discrimination. Conclusion: Pediatric patients experiencing IHCA can be classified into low, moderate, and high-risk categories using a simple risk score and easily identified pre-arrest variables. These risk scores can support clinical decisions, facilitate research, and help monitor the quality of medical services.

Development and validation of algorithms to predict left ventricular ejection fraction class from healthcare claims data.

The use of large medical or healthcare claims databases is very useful for population-based studies on the burden of heart failure (HF). Clinical characteristics and management of HF patients differ according to categories of left ventricular ejection fraction (LVEF), but this information is often missing in such databases. We aimed to develop and validate algorithms to identify LVEF in healthcare databases where the information is lacking. Algorithms were built by machine learning with a random forest approach. Algorithms were trained and reinforced using the French national claims database [Système National des Données de Santé (SNDS)] and a French HF registry. Variables were age, gender, and comorbidities, which could be identified by medico-administrative code-based proxies, Anatomical Therapeutic Chemical codes for drug delivery, International Classification of Diseases (Tenth Revision) coding for hospitalizations, and administrative codes for any other type of reimbursed care. The algorithms were validated by cross-validation and against a subset of the SNDS that includes LVEF information. The areas under the receiver operating characteristic curve were 0.84 for the algorithm identifying LVEF≤40% and 0.79 for the algorithms identifying LVEF<50% and ≥50%. For LVEF≤40%, the reinforced algorithm identified 50% of patients in the validation dataset with a positive predictive value of 0.88 and a specificity of 0.96. The most important predictive variables were delivery of HF medication, sex, age, hospitalization, and testing for natriuretic peptides with different orders of positive or negative importance according to the LVEF category. The algorithms identify reduced or preserved LVEF in HF patients within a nationwide healthcare claims database with high positive predictive value and low rates of false positives.

Tourism SME default: A note on predictors

This note addresses the lack of progress on small and medium enterprises (SMEs) default prediction in the tourism sector. Using the case of Croatian tourism SMEs, this note applies Altman's recently introduced Omega Score model to (i) predict SME default, (ii) explore and contrast important variables for default prediction in tourism vs. other sectors, and (iii) adapt the original Omega Score to tourism sector specifics. Financial ratios have historically been prominent among the predictors in scholarly investigations. However, our findings underscore the significance of supplementing these ratios with other predictors of tourism SME default, specifically with information on (i) creditor payment behaviour and (ii) employee characteristics and relationships with them. Furthermore, our research demonstrates that a refined selection of six variables, accompanied by adjusted weightings, can significantly enhance default prediction accuracy. The results provide valuable lessons for the strategic management of tourism firms.

Mapping stony rise landforms using a novel remote sensing, geophysical, and machine learning approach

A basalt lava plain covers much of central and western Victoria in Australia. Many of the younger flows (<1 Ma) have hummocky surfaces with abundant tumuli and lava rises, known locally as stony rises due to the protruding basalt boulders. Stony rises are noteworthy geologically but have ecological importance and are culturally significant to Aboriginal Australians. Ecologically, stony rises host many native plant species. Culturally, stony rises provide evidence of Aboriginal land use and occupation through the presence of artefact deposits. Currently, mapping of stony rises is carried out by conducting field surveys on a study-by-study basis. This could be performed more rapidly using remote sensing datasets such as LiDAR, satellite and aerial imagery, and geophysical data. This research mapped stony rises in an area that is experiencing rapid urban development using a combination of remotely sensed datasets that characterise the morphology of stony rises through a Machine Learning approach that utilises Object Based Image Analysis (OBIA) segmentation and Random Forests (RF) classification techniques. The research successfully identified stony rises in the landscape to an accuracy of 89.97 %, with 314 potential new stony rise sites identified. The research identified the most important predictor variables were slope, local elevation, the concentrations of thorium and uranium (obtained from airborne radiometric data), aeromagnetic data (total magnetic intensity), the Normalised Difference Water Index (NDWI), and the Clay Mineral Ratio. The results from this study lay a foundation for standardising the definition of a stony rise within Australia. Furthermore, by utilising various predictor variables, this study highlights the success of a multi-faceted approach into detecting a specific landform at a landscape level.

Application of the Boruta algorithm to assess the multidimensional determinants of malnutrition among children under five years living in southern Punjab, Pakistan

BackgroundMalnutrition causes nutrient deficiencies that have both physical and clinical consequences in severe acute malnutrition children. Globally, there were 47 million wasted children under the age of five in 2019. One in four were located in sub-Saharan Africa, with half being in South Asia. This study aims to apply the Boruta algorithm to identify the determinants of undernutrition among children under five living in Dera Ghazi Khan, one of the marginalized districts of densely populated Punjab Province in Pakistan.MethodsA multicenter cross-sectional study design was used to collect data from 185 children with severe acute malnutrition aged under five years visiting the OTPs centers located in Dera Ghazi Khan, Punjab, Pakistan. A purposive sampling technique was used to collect data using a pretested structured questionnaire from parents/caregivers regarding family sociodemographic characteristics, child nutrition, and biological and healthcare characteristics. Anthropometric measurements, including height, weight, and mid-upper arm circumference, were collected. The Boruta models were used to incorporate the children’s anthropometric, nutritional, and household factors to determine the important predictive variables for undernutrition using the Boruta package in R studio.ResultsThis study included 185 children, with a mean age of 15.36 ± 10.23 months and an MUAC of 10.19 ± 0.96 cm. The Boruta analysis identifies age, mid-upper arm circumference, weaning practices, and immunization status as important predictors of undernutrition. Income per month, exclusive breastfeeding, and immunization status were found to be key factors of undernutrition in children under the age of five.ConclusionThis study highlights age, mid-upper arm circumference, weaning practices, and immunization status as key determinants of weight-for-height and weight-for-age in children under five years. It also suggests that economic context may influence undernutrition. The findings can guide targeted strategies for combating undernutrition.

Effects of climate on the distribution and conservation of commonly observed European earthworms.

Belowground biodiversity distribution does not necessarily reflect aboveground biodiversity patterns, but maps of soil biodiversity remain scarce because of limited data availability. Earthworms belong to the most thoroughly studied soil organisms and-in their role as ecosystem engineers-have a significant impact on ecosystem functioning. We used species distribution modeling (SDMs) and available data sets to map the spatial distribution of commonly observed (i.e., frequently recorded) earthworm species (Annelida, Oligochaeta) across Europe under current and future climate conditions. First, we predicted potential species distributions with commonly used models (i.e., MaxEnt and Biomod) and estimated total species richness (i.e., number of species in a 5 × 5km grid cell). Second, we determined how much the different types of protected areas covered predicted earthworm richness and species ranges (i.e., distributions) by estimating the respective proportion of the range area. Earthworm species richness was high in central western Europe and low in northeastern Europe. This pattern was mainly associated with annual mean temperature and precipitation seasonality, but the importance of predictor variables to species occurrences varied among species. The geographical ranges of the majority of the earthworm species were predicted to shift to eastern Europe and partly decrease under future climate scenarios. Predicted current and future ranges were only poorly covered by protected areas, such as national parks. More than 80% of future earthworm ranges were on average not protected at all (mean [SD] = 82.6% [0.04]). Overall, our results emphasize the urgency of considering especially vulnerable earthworm species, as well as other soil organisms, in the design of nature conservation measures.

Using machine learning to identify indicators of rare earth element enrichment in sedimentary strata with applications for metal prospectivity

Rare earth elements (REE), classified as critical minerals which are crucial for clean energy technologies, face soaring demand. While economic deposits are found in limited geologic environments including carbonatites and ion-adsorption clays, unconventional, secondary sources such as those from sedimentary basins could hold potential to meet this increased demand. Coal and its associated combustion by-products, phosphorites, oil sands tailings, and formation waters have all garnered interest for REE recovery, yet they remain significantly underexplored. Accordingly, new tools for data analysis and optimization such as machine learning can assist in mineral prospectivity, with these tools being subject to rapid proliferation in the Earth sciences.This work leverages compositional data analysis principles and machine learning to probe geochemical relationships and predict REE abundances in sedimentary lithologies using unsupervised (correlation, principal component, and cluster analysis) and supervised (regression, support vector machine, random forest, and boosting) machine learning models. These three unsupervised models display similar results, with REE typically being associated with incompatible elements (e.g., Th, Nb, and Hf). Gradient boosting, Adaboost, and Random Forest had the highest performance for predicting REE concentrations, with Th and P commonly being the most important predictor variables. Identifying geochemical indicators of REE enrichment that may be used to assist in discovering potentially exploitable REE resources based on existing data, as well as increasing the understanding of metal behaviour in sedimentary systems, is a step forward in understanding novel secondary and unconventional REE sources. Although REE concentrations from these sources are generally lower than primary ore deposits, the amount of available feedstock, potentially simpler, cheaper, and less environmentally taxing extraction processes, and the added benefit of remediating waste streams and contributing to the circular economy make these sources alluring.

Estimating Lithium Concentrations in Groundwater Used as Drinking Water for the Conterminous United States.

Lithium (Li) concentrations in drinking-water supplies are not regulated in the United States; however, Li is included in the 2022 U.S. Environmental Protection Agency list of unregulated contaminants for monitoring by public water systems. Li is used pharmaceutically to treat bipolar disorder, and studies have linked its occurrence in drinking water to human-health outcomes. An extreme gradient boosting model was developed to estimate geogenic Li in drinking-water supply wells throughout the conterminous United States. The model was trained using Li measurements from ∼13,500 wells and predictor variables related to its natural occurrence in groundwater. The model predicts the probability of Li in four concentration classifications, ≤4 μg/L, >4 to ≤10 μg/L, >10 to ≤30 μg/L, and >30 μg/L. Model predictions were evaluated using wells held out from model training and with new data and have an accuracy of 47-65%. Important predictor variables include average annual precipitation, well depth, and soil geochemistry. Model predictions were mapped at a spatial resolution of 1 km2 and represent well depths associated with public- and private-supply wells. This model was developed by hydrologists and public-health researchers to estimate Li exposure from drinking water and compare to national-scale human-health data for a better understanding of dose-response to low (<30 μg/L) concentrations of Li.

Artificial intelligence-based prediction models for acute myeloid leukemia using real-life data: A DATAML registry study

We designed artificial intelligence-based prediction models (AIPM) using 52 diagnostic variables from 3687 patients included in the DATAML registry treated with intensive chemotherapy (IC, N = 3030) or azacitidine (AZA, N = 657) for an acute myeloid leukemia (AML). A neural network called multilayer perceptron (MLP) achieved a prediction accuracy for overall survival (OS) of 68.5% and 62.1% in the IC and AZA cohorts, respectively. The Boruta algorithm could select the most important variables for prediction without decreasing accuracy. Thirteen features were retained with this algorithm in the IC cohort: age, cytogenetic risk, white blood cells count, LDH, platelet count, albumin, MPO expression, mean corpuscular volume, CD117 expression, NPM1 mutation, AML status (de novo or secondary), multilineage dysplasia and ASXL1 mutation; and 7 variables in the AZA cohort: blood blasts, serum ferritin, CD56, LDH, hemoglobin, CD13 and disseminated intravascular coagulation (DIC). We believe that AIPM could help hematologists to deal with the huge amount of data available at diagnosis, enabling them to have an OS estimation and guide their treatment choice. Our registry-based AIPM could offer a large real-life dataset with original and exhaustive features and select a low number of diagnostic features with an equivalent accuracy of prediction, more appropriate to routine practice.