Relative Importance Of Features Research Articles

The spatial structure of the tumor immune microenvironment can explain and predict patient response in high-grade serous carcinoma.

Ovarian cancer is the deadliest gynecological malignancy, and therapeutic options and mortality rates over the last three decades have largely not changed. Recent studies indicate that the composition of the tumor immune microenvironment (TIME) influences patient outcomes. To improve spatial understanding of the TIME, we performed multiplexed ion beam imaging on 83 human high-grade serous carcinoma tumor samples, identifying about 160,000 cells across 23 cell types. For 77 of these samples meeting inclusion criteria, we generated composition features based on cell type proportions, spatial features based on the distances between cell types, and spatial network features representing cell interactions and cell clustering patterns, which we linked to traditional clinical and immunohistochemical variables and patient overall survival (OS) and progression-free survival (PFS) outcomes. Among these features, we found several significant univariate correlations, including B-cell contact with M1 macrophages (OS hazard ratio HR=0.696, p=0.011, PFS HR=0.734, p=0.039). We then used high-dimensional random forest models to evaluate out-of-sample predictive performance for OS and PFS outcomes and to derive relative feature importance scores for each feature. The top model for predicting low or high PFS used TIME composition and spatial features and achieved an average AUC (area under the receiver-operating characteristic curve) score of 0.71. The results demonstrate the importance of spatial structure in understanding how the TIME contributes to treatment outcomes. Furthermore, the present study provides a generalizable roadmap for spatial analyses of the TIME in ovarian cancer research.

Comparative analysis of RUSLE and SWPT for sub-watershed conservation prioritization in the Ayu watershed, Abay basin, Ethiopia

Ethiopia is currently facing a major environmental problem caused by soil erosion. In order to tackle this problem, it is essential to implement a comprehensive watershed management approach and give priority to conservation efforts depending on the level of severity. Therefore, the objective of this research is to evaluate the mean annual soil erosion and rank the sub-watersheds for conservations in the Ayu watershed, utilizing the Revised Universal Soil Loss Equation (RUSLE) model and the Sub-Watershed Prioritization Tool (SWPT). RUSLE was utilized to predict the annual average soil erosion rate, while SWPT was applied to conduct Weighted Sum Analysis (WSA) for ranking sub-watersheds. Support Vector Machine (SVM) was employed for classifying land use and land cover. The Relative importance of morphometric and topo-hydrologic features in the SWPT was analyzed using a Random Forest model. The Bland-Altman plot and Wilcoxon Signed Rank Test were employed to assess the agreement in prioritizing watersheds between RUSLE results and the SWPT. Furthermore, field observations were conducted to validate the land use classification by collecting ground data. In addition, the study was enhanced with local viewpoints by conducting focus group discussions with agricultural experts and farmers to obtain qualitative insights and validation of resuts. The findings showed that soil loss varied from 0 to 110 t/ha/yr, with an average of 8.95 t/ha/yr, resulting in a total loss of 384365.3 tons annually. The comparison of RUSLE and SWPT showed a moderate positive relationship (r = 0.59). The results of the Bland-Altman plot indicate a consistent agreement between the two methods. However, there is inconsistency among the five sub watersheds. This study enhances the knowledge of soil erosion patterns and offers useful guidance for watershed conservation techniques. It can be also used as a beneficial framework for managing watersheds, with possible uses outside of the Ayu watershed.

Artificial Intelligence-based Electrocardiogram prediction for duration of Atrial Fibrillation

Abstract Introduction Atrial fibrillation (AF) is the most common sustained arrhythmia and is associated with poor clinical outcomes, including stroke, acute coronary events, and heart failure. Recent studies have shown that early rhythm treatment of new-onset atrial fibrillation improves the patient's prognosis. However, atrial fibrillation is often asymptomatic, and it is difficult to determine its duration accurately. Recently, AI-bsed ECG has been studied for various cardiovascular diseases. Hypothesis We sought to develop and validate a predictive model of the ECG for the duration of atrial fibrillation. Methods All patients aged 18 years older with at least one ECG were included in the study. Only patients with sinus rhythm with ECG prior to atrial fibrillation with ECG were selected. An ECG within 1 year from AF was first documented in ECG was defined as new onset AF. After dividing our datasets into training (and test sets, we developed an end-to-end deep neural network to predict for the duration of AF. Performance evaluation was conducted using various metrics, including AUROC, AUPRC, sensitivity, specificity, F1 score. Results The dataset consisted of 83,525 ECGs from 16,193 patients from two hospitals. The AUROC for discriminating old AF and new-onset AF is 0.8186 (0.8181 - 0.8190) on internal validation set and 0.7967 (0.7966 - 0.7969) on external validation set. Sensitivity, Specificity, and F1 score are 0.7126((0.7118-0.7134), 0.7697 (0.7693-0.7701) and 0.5751 (0.5745-0.5757) on internal validation set and 0.7309 (0.7307-0.7311), 0.7225 (0.7224-0.7227) and 0.6354 (0.6352-0.6356)on external validation set. Conclusion Our deep learning model can be used to predict atrial fibrillation's duration. Additional studies are ongoing to understand the relative importance of ECG features.

Socio-demographic predictors of not having private dental insurance coverage: machine-learning algorithms may help identify the disadvantaged

BackgroundFor accessing dental care in Canada, approximately 62% of the population has employment-based insurance, 6% have some publicly funded coverage, and 32% have to pay out-of pocket. Those with no insurance or public coverage find dental care more unaffordable compared to those with private insurance. To support the development of more comprehensive publicly funded dental care programs, it is important to understand the socio-demographic attributes of all those, who find dental care unaffordable.MethodsThis study is a secondary analysis of the data collected from Ontarians during the latest available cycle of the Canadian Community Health Survey (2017-18), a cross-sectional survey that collects information on health status, health care utilization, and health determinants for the Canadian population. First, bivariate analysis was conducted to determine the characteristics of Ontarians who lack dental insurance. Afterwards, we employed machine learning (ML) to analyze data and identify risk indicators for not having private dental insurance. Specifically, we trained several supervised ML models and utilized Shapley additive explanations (SHAP) to determine the relative feature importance for not having private dental insurance from the best ML model [the gradient boosting (GBM)].ResultsApproximately one-third of Ontarians do not have private insurance coverage for dental care. Individuals with an income below $20,000, those unemployed or working part-time, seniors aged above 70, and those unable to afford to have their own housing are more at risk of not having private dental insurance, leading to financial barriers in accessing dental care.ConclusionIn the future, government-funded programs can incorporate these identified risk indicators when determining eligible populations for publicly funded dental programs. Understanding these attributes is critical for developing targeted and effective interventions, ensuring equitable access to dental care for Canadians.

Predicting suicidality with small sets of interpretable reward behavior and survey variables

The prediction of suicidal thought and behavior has met with mixed results. This study of 3,476 de-identified participants (4,019 before data exclusion) quantified the prediction of four suicidal thought and behavior (STB) variables using a short reward/aversion judgment task and a limited set of demographic and mental health surveys. The focus was to produce a simple, quick and objective framework for assessing STB that might be automatable, without the use of big data. A balanced random forest classifier performed better than a Gaussian mixture model and four standard machine learning classifiers for predicting passive suicide ideation, active suicide ideation, suicide planning and planning for safety. Accuracies ranged from 78% to 92% (optimal area under the curve between 0.80 and 0.95) without overfitting, and peak performance was observed for predicting suicide planning. The relative importance of features for prediction showed distinct weighting across judgment variables, contributing between 40% and 64% to prediction per Gini scores. Mediation/moderation analyses showed that depression, anxiety, loneliness and age variables moderated the judgment variables, indicating that the interaction of judgment with mental health and demographic indices is fundamental for the high-accuracy prediction of STB. These findings suggest the feasibility of an efficient and highly scalable system for suicide assessment, without requiring psychiatric records or neural measures. The findings suggest that STB might be understood within a cognitive framework for judgment with quantitative variables whose unique constellation separates passive and active suicidal thought (ideation) from suicide planning and planning for safety.

A machine learning approach to evaluate coastal risks related to extreme weather events in the Veneto region (Italy)

A significant and unprecedented increase of temperature has been recorded worldwide during the last decades, leading to the occurrence of numerous extreme events. Coastal areas, with their high population density, interconnected economic activities, fragile ecosystems, are particularly vulnerable to climate change impacts. These impacts can be intensified by the interactions of multiple hazards which operate at different spatio-temporal scales and affect exposure and vulnerability patterns. An integrated approach is here proposed to assess the relationship between risk factors and to evaluate the multiplicity of impacts that may affect the coastline. A new path to tackle these multi-risk events is offered by ML algorithms to effectively handle vast amounts of heterogenous data, and model complex non-linear relationships between multiple factors and feedback mechanisms. To assess impacts caused by extreme events (storm surges, extreme precipitation, wind events) in the Veneto coastal municipalities, a ML approach was developed to understand connections between atmospheric and marine hazards and impacts recorded by the Veneto region emergency archive during the 2009–2019 timeframe, identifying the most influencing factors triggering multiple risks. Additionally, the coastal municipalities were clustered considering the intrinsic relationships between impact occurrences, exposure and vulnerability features. Several algorithms were compared to estimate daily risk of impacts to occur providing hazards, exposure and vulnerability information. The MLP algorithm showed satisfactory performances (weighted-F1-score of 0.94) to estimate the relative importance of input features. The proposed algorithm was designed as support tool to increase the understanding of impacts’ occurrence in coastal areas, thus helping the adaptation planning process.

Abstract PO4-08-06: Causal Relationships and Familial Aggregation of Mammogram Risk Scores: Insights from Breast Cancer in Twins and Families

Abstract Background: Mammogram risk scores based on texture and density, determined by brightness thresholds, are associated with breast cancer risk differently and could reveal distinct information about breast cancer risk. This study aimed to investigate the causal relationships between these intercorrelated risk scores and the familial aggregation of a texture-based mammogram measure, shedding light on the relevance to breast cancer aetiology. Methods: We analysed data from the Australian Mammographic Density Twins and Sisters Study, including 3195 breast-cancer-free women from 1519 families. The dataset comprised 527 pairs of monozygotic (MZ) twins, 271 pairs of dizygotic (DZ) twins, and 1599 sisters. The average age at mammography for sister pairs ranged from 40 to 70 years. Mammogram risk scores based on texture (named Cirrus) and three density-defined areas (light, bright, and brightest) that were spatially independent were generated. The Inference about Causation from Examination of Familial Confounding (ICE FALCON) method was employed for causal inference. Familial aggregation and the variance of Cirrus were assessed as a function of age using a multivariate normal model for pedigree analysis. The classic twin model was employed but allowing for varying shared environmental effects among different sister pairs. The single-nucleotide polymorphism (SNP)-based heritability was estimated using genetic variants from both closely and distantly related individuals. Model fits were compared using likelihood ratio tests and the Akaike Information Criterion. Results: Causal analysis using ICE FALCON revealed significant positive causal effects, with Cirrus, light areas, and bright areas influencing the brightest areas (accounting for 34%, 55%, and 85% of the associations, respectively). Similarly, light areas and bright areas demonstrated causal effects on Cirrus (accounting for 37% and 28% of the associations, respectively). No age-related differences in familial correlations or Cirrus variance were found for women aged 40 to 70 years. Familial correlations were estimated at 0.51 (0.03) for MZ pairs and 0.16 (0.03) for combined DZ and non-twin sister pairs, with no significant difference between DZ and non-twin sister pairs (P=0.3). Additive genetic effects accounted for up to 32% (5%) of the variance, consistent with SNP-based heritability estimates of 36% (12%). MZ-specific environmental effects contributed to at least 20% (3%), while shared environmental effects for DZ and non-twin sibling pairs accounted for less than 40% of that for MZ pairs. Conclusion: In a mammogram, the lighter (less dense) areas causally influence the brightest (highly dense) areas, including through textural features. These causal relationships provide insights into the relative importance of different mammogram features in breast cancer aetiology. For example, the brightest areas are more aetiologically important for screen-detected breast cancer, while the light areas are more aetiologically important for interval breast cancer. Additionally, specific textural features capture aetiologically independent breast cancer risk information from dense areas. We also confirm the partial heritability of Cirrus, the texture-based mammogram measure, substantially influenced by environmental factors (familial and non-familial). As well as genetic factors, Cirrus could be determined by factors operating at times of life when MZ pairs share their environments to a greater extent than do DZ and non-twin sisters, starting in utero and prior to adulthood, and remaining constant thereafter. These insights from twin and family data enhance our understanding of mammogram risk scores and their implications for breast cancer risk assessment. Citation Format: Zhoufeng Ye, Shuai Li, Gill Dite, Tuong Nguyen, Robert MacInnis, John Hopper. Causal Relationships and Familial Aggregation of Mammogram Risk Scores: Insights from Breast Cancer in Twins and Families [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO4-08-06.

Data-driven prediction of natural period for existing RC high-rise buildings using probabilistic machine learning methods

The natural period of existing reinforced concrete (RC) high-rise buildings is a fundamental structural parameter for dynamic analysis and condition assessment, while the estimation of such structural characteristic is an important task for life-cycle maintenance. A commonly adopted approach for predicting natural periods involves the use of design-oriented empirical formulas. However, as shown by real-world observations in both this study and others, these formulas with limited building features may not fully capture the complexity and uncertainty of the natural period for existing buildings. Therefore, it is desired to develop an alternative method that is both feature-based and data-driven, to predict natural periods accurately. This study proposes a feature-based probabilistic machine learning method to determine the natural periods and quantify the uncertainty intervals of the predictions for existing RC high-rise buildings. Specifically, the proposed method comprises a random-forest method using quantile regression. Through in situ measurements and an extensive literature review, a database comprising over 500 actual records was constructed, including the building features and their dynamic properties. The results show that the proposed method achieves a coefficient of determination of 0.96 and an uncertainty interval confined within 20 % for the test set. These findings indicate that the proposed method may be both more accurate and reliable. Furthermore, an in-depth investigation into the model interpretability was conducted to reveal the relative importance of different building features. The analyses indicate that the building height emerges as the most crucial feature, while the building width and its lateral resistant system may also demonstrate noticeable influence on predicting natural periods.

Fractal-Based Multi-Criteria Feature Selection to Enhance Predictive Capability of AI-Driven Mineral Prospectivity Mapping

AI-driven mineral prospectivity mapping (MPM) is a valid and increasingly accepted tool for delineating the targets of mineral exploration, but it suffers from noisy and unrepresentative input features. In this study, a set of fractal and multifractal methods, including box-counting calculation, concentration–area fractal modeling, and multifractal analyses, were employed to excavate the underlying nonlinear mineralization-related information from geological features. Based on these methods, multiple feature selection criteria, namely prediction–area plot, K-means clustering, information gain, chi-square, and the Pearson correlation coefficient, were jointly applied to rank the relative importance of ore-related features and their fractal representations, so as to choose the optimal input feature dataset readily used for training predictive AI models. The results indicate that fault density, the multifractal spectrum width (∆α) of the Yanshanian intrusions, information dimension (D1) of magnetic anomalies, correlation dimension (D2) of iron-oxide alteration, and the D2 of argillic alteration serve as the most effective predictor features representative of the corresponding ore-controlling elements. The comparative results of the model assessment suggest that all the AI models trained by the fractal datasets outperform their counterparts trained by raw datasets, demonstrating a significant improvement in the predictive capability of fractal-trained AI models in terms of both classification accuracy and predictive efficiency. A Shapley additive explanation was employed to trace the contributions of these features and to explain the modeling results, which imply that fractal representations provide more discriminative and definitive feature values that enhance the cognitive capability of AI models trained by these data, thereby improving their predictive performance, especially for those indirect predictor features that show subtle correlations with mineralization in the raw dataset. In addition, fractal-trained models can benefit practical mineral exploration by outputting low-risk exploration targets that achieve higher capturing efficiency and by providing new mineralization clues extracted from remote sensing data. This study demonstrates that the fractal representations of geological features filtered by multi-criteria feature selection can provide a feasible and promising means of improving the predictive capability of AI-driven MPM.

A New Permutation-Based Method for Ranking and Selecting Group Features in Multiclass Classification

The selection of group features is a critical aspect in reducing model complexity by choosing the most essential group features, while eliminating the less significant ones. The existing group feature selection methods select a set of important group features, without providing the relative importance of all group features. Moreover, few methods consider the relative importance of group features in the selection process. This study introduces a permutation-based group feature selection approach specifically designed for high-dimensional multiclass datasets. Initially, the least absolute shrinkage and selection operator (lasso) method was applied to eliminate irrelevant individual features within each group feature. Subsequently, the relative importance of the group features was computed using a random-forest-based permutation method. Accordingly, the process selected the highly significant group features. The performance of the proposed method was evaluated using machine learning algorithms and compared with the performance of other approaches, such as group lasso. We used real-world, high-dimensional, multiclass microarray datasets to demonstrate its effectiveness. The results highlighted the capability of the proposed method, which not only selected significant group features but also provided the relative importance and ranking of all group features. Furthermore, the proposed method outperformed the existing method in terms of accuracy and F1 score.

Understanding of facial features in face perception: insights from deep convolutional neural networks.

Face recognition has been a longstanding subject of interest in the fields of cognitive neuroscience and computer vision research. One key focus has been to understand the relative importance of different facial features in identifying individuals. Previous studies in humans have demonstrated the crucial role of eyebrows in face recognition, potentially even surpassing the importance of the eyes. However, eyebrows are not only vital for face recognition but also play a significant role in recognizing facial expressions and intentions, which might occur simultaneously and influence the face recognition process. To address these challenges, our current study aimed to leverage the power of deep convolutional neural networks (DCNNs), an artificial face recognition system, which can be specifically tailored for face recognition tasks. In this study, we investigated the relative importance of various facial features in face recognition by selectively blocking feature information from the input to the DCNN. Additionally, we conducted experiments in which we systematically blurred the information related to eyebrows to varying degrees. Our findings aligned with previous human research, revealing that eyebrows are the most critical feature for face recognition, followed by eyes, mouth, and nose, in that order. The results demonstrated that the presence of eyebrows was more crucial than their specific high-frequency details, such as edges and textures, compared to other facial features, where the details also played a significant role. Furthermore, our results revealed that, unlike other facial features, the activation map indicated that the significance of eyebrows areas could not be readily adjusted to compensate for the absence of eyebrow information. This finding explains why masking eyebrows led to more significant deficits in face recognition performance. Additionally, we observed a synergistic relationship among facial features, providing evidence for holistic processing of faces within the DCNN. Overall, our study sheds light on the underlying mechanisms of face recognition and underscores the potential of using DCNNs as valuable tools for further exploration in this field.

Transformer-Based Nowcasting of Radar Composites from Satellite Images for Severe Weather

Abstract Weather radar data are critical for nowcasting and an integral component of numerical weather prediction models. While weather radar data provide valuable information at high resolution, their ground-based nature limits their availability, which impedes large-scale applications. In contrast, meteorological satellites cover larger domains but with coarser resolution. However, with the rapid advancements in data-driven methodologies and modern sensors aboard geostationary satellites, new opportunities are emerging to bridge the gap between ground- and space-based observations, ultimately leading to more skillful weather prediction with high accuracy. Here, we present a transformer-based model for nowcasting ground-based radar image sequences using satellite data up to 2-h lead time. Trained on a dataset reflecting severe weather conditions, the model predicts radar fields occurring under different weather phenomena and shows robustness against rapidly growing/decaying fields and complex field structures. Model interpretation reveals that the infrared channel centered at 10.3 μm (C13) contains skillful information for all weather conditions, while lightning data have the highest relative feature importance in severe weather conditions, particularly in shorter lead times. The model can support precipitation nowcasting across large domains without an explicit need for radar towers, enhance numerical weather prediction and hydrological models, and provide radar proxy for data-scarce regions. Moreover, the open-source framework facilitates progress toward operational data-driven nowcasting. Significance Statement Ground-based weather radar data are essential for nowcasting, but data availability limitations hamper usage of radar data across large domains. We present a machine learning model, rooted in transformer architecture, that performs nowcasting of radar data using high-resolution geostationary satellite retrievals, for lead times of up to 2 h. Our model captures the spatiotemporal dynamics of radar fields from satellite data and offers accurate forecasts. Analysis indicates that the infrared channel centered at 10.3 μm provides useful information for nowcasting radar fields under various weather conditions. However, lightning activity exhibits the highest forecasting skill for severe weather at short lead times. Our findings show the potential of transformer-based models for nowcasting severe weather.

Predicting early return to the operating room in early-onset scoliosis patients using machine learning techniques.

Surgical treatment of early-onset scoliosis (EOS) is associated with high rates of complications, often requiring unplanned return to the operating room (UPROR). The aim of this study was to create and validate a machine learning model to predict which EOS patients will go on to require an UPROR during their treatment course. A retrospective review was performed of all surgical EOS patients with at least 2 years follow-up. Patients were stratified based on whether they had experienced an UPROR. Ten machine learning algorithms were trained using tenfold cross-validation on an independent training set of patients. Model performance was evaluated on a separate testing set via their area under the receiver operating characteristic curve (AUC). Relative feature importance was calculated for the top-performing model. 257 patients were included in the study. 146 patients experienced at least one UPROR (57%). Five factors were identified as significant and included in model training: age at initial surgery, EOS etiology, initial construct type, and weight and height at initial surgery. The Gaussian naïve Bayes model demonstrated the best performance on the testing set (AUC: 0.79). Significant protective factors against experiencing an UPROR were weight at initial surgery, idiopathic etiology, initial definitive fusion construct, and height at initial surgery. The Gaussian naïve Bayes machine learning algorithm demonstrated the best performance for predicting UPROR in EOS patients. Heavier, taller, idiopathic patients with initial definitive fusion constructs experienced UPROR less frequently. This model can be used to better quantify risk, optimize patient factors, and choose surgical constructs. Prognostic: III.

Effects of cultural landscape service features in national forest parks on visitors’ sentiments: A nationwide social media-based analysis in China

National Forest Parks (NFPs) represent the highest level of the forest park system in China and have long been considered to cultivate positive experiences among visitors through their rich cultural services. However, there is limited knowledge of the relationship between the cultural dimension of landscape services (CLSs) in NFPs and visitors’ positive experiences. This study represents the first effort to investigate the effects of NFP CLSs on visitors’ expressed sentiments and aims to further identify the relative importance of various CLS features. A total of 267 NFPs across 31 provinces in China were selected as case studies, and approximately 300,000 visitors’ online comments from a widely used travel platform (CTrip) were obtained for sentiment analysis using natural language processing. A novel systematic framework for assessing CLSs was proposed, encompassing five principal features and 19 indicator subcategories. Ordinary least-squares and spatial regression models were used to reveal associations between NFP CLSs and expressed sentiments, whereas the XGBoost model and Shapley Additive exPlanations (SHAP) values were employed to identify the relative importance of NFP CLS features. Three major findings were observed: First, the feature scoring of CLSs in NFPs was unevenly distributed across the country, with scores along the eastern seaboard relatively higher than those inland in the northwest. Second, beneficial associations between the five principal CLS features and visitors’ expressed sentiments were suggested. Third, the influence of landscape aesthetics was particularly prominent in promoting visitors’ positive sentiments, followed by education, physical activities and health, recreation and tourism, and cultural heritage and spiritual practices. These findings provide valuable insights for health-oriented NFP management, policies, and planning.

Using machine learning techniques to predict the risk of osteoporosis based on nationwide chronic disease data

Osteoporosis is a major public health concern that significantly increases the risk of fractures. The aim of this study was to develop a Machine Learning based predictive model to screen individuals at high risk of osteoporosis based on chronic disease data, thus facilitating early detection and personalized management. A total of 10,000 complete patient records of primary healthcare data in the German Disease Analyzer database (IMS HEALTH) were included, of which 1293 diagnosed with osteoporosis and 8707 without the condition. The demographic characteristics and chronic disease data, including age, gender, lipid disorder, cancer, COPD, hypertension, heart failure, CHD, diabetes, chronic kidney disease, and stroke were collected from electronic health records. Ten different machine learning algorithms were employed to construct the predictive mode. The performance of the model was further validated and the relative importance of features in the model was analyzed. Out of the ten machine learning algorithms, the Stacker model based on Logistic Regression, AdaBoost Classifier, and Gradient Boosting Classifier demonstrated superior performance. The Stacker model demonstrated excellent performance through ten-fold cross-validation on the training set and ROC curve analysis on the test set. The confusion matrix, lift curve and calibration curves indicated that the Stacker model had optimal clinical utility. Further analysis on feature importance highlighted age, gender, lipid metabolism disorders, cancer, and COPD as the top five influential variables. In this study, a predictive model for osteoporosis based on chronic disease data was developed using machine learning. The model shows great potential in early detection and risk stratification of osteoporosis, ultimately facilitating personalized prevention and management strategies.

A perceptual approach to address complex water management issues in lowland permeable catchments

Water quality management is a pressing global concern, and an increasingly complex issue due to climate and land-use change, legacy pollution, and the persistent release of well-known and emerging contaminants from diffuse and point sources. The increasing availability of high-frequency monitoring data is leading existing, often heuristic approaches, to be judged inadequate. Water managers frequently rely on simple qualitative and/or quantitative approaches for decision-making, but a lack of tangible improvement in freshwater quality outcomes is demanding new and innovative approaches that rely more on physical process understanding, rather than precedent. In this study, we drew upon local geological, hydrogeological, and hydrological data to infer a high-level perceptual model of surface/groundwater interactions in a chalk stream in Dorset, UK. We used the perceptual model to interrogate spatial and temporal trends in historical water quality data and to construct reach-scale nutrient mass balances. Through novel representation with Sankey diagrams, the perceptual model highlighted the relative importance of different hydrological features. Surface/groundwater interactions were found to occur predominantly by spring flow. We demonstrate that river flow accretion was dominated by the Chalk aquifer despite only occupying ca. 15 % of the surface bedrock area, and that spring sources, whilst vital to dilute treated sewage inputs in baseflow conditions, were also major sources of legacy nitrate. Nutrient mass balances showed that sewage treatment works contributed ca. 13 % to soluble reactive phosphorus load, with groundwater accounting for ca. 48 %. Thus, a determinand often associated with point-source pollution was shown to be diffuse dominated in this river. The study demonstrated how a multi-disciplinary approach to water management, based on a comprehensive perceptual modelling approach, could identify hitherto unknown sources and relative contributors to freshwater pollution and allow flow and load apportionment studies to provide useful decision-support to manage nutrient pollution. The novel application of perceptual modelling tools, such as the Sankey diagram, allows different source attributions to be presented in an accessible manner, and can be readily transferred to other study areas.

Designing outdoor fitness areas for older adults: a conjoint analysis study

ABSTRACT This study aimed to examine the relative importance of micro-level design features of outdoor fitness areas preferred by older adults and whether preferences varied according to gender, park accompaniment, and mobility status. A series of Adaptive Choice-Based Conjoint (ACBC) tasks were completed by older adults (n = 372, 65–93 years, 57% female) in an online survey. Ten different micro-level design features of outdoor fitness areas were examined including surface type, location of fitness equipment, equipment designed for older adults, equipment distribution, shade sails, light fixtures, shady trees, benches, drinking water, and water body near fitness area. Hierarchical Bayes analyses within Sawtooth software were used to identify the relative importance scores and part-worth utilities of the design features. For the overall sample, the three most important features were as follows: surface type (21.5%, 95% CI 20.0, 22.9); equipment specifically designed for older adults (18.7%, 95% CI 17.3, 20.2); and shady trees around the fitness area (14.0%, 95% CI 12.9, 15.0). A few significant differences in the relative ranking of design features were observed by gender and mobility status. This study will help stakeholders in understanding which design features to prioritise when (re)designing outdoor fitness areas for older adults.

Machine Learning-Based Discrimination of Cardiovascular Outcomes in Patients With Hypertrophic Cardiomyopathy

BackgroundCurrent risk stratification strategies for patients with hypertrophic cardiomyopathy (HCM) are limited to traditional methodologies. ObjectivesThe authors aimed to establish machine learning (ML)-based models to discriminate major cardiovascular events in patients with HCM. MethodsWe enrolled consecutive HCM patients from 2 tertiary referral centers and used 25 clinical and echocardiographic features to discriminate major adverse cardiovascular events (MACE), including all-cause death, admission for heart failure (HF-adm), and stroke. The best model was selected for each outcome using the area under the receiver operating characteristic curve (AUROC) with 20-fold cross-validation. After testing in the external validation cohort, the relative importance of features in discriminating each outcome was determined using the SHapley Additive exPlanations (SHAP) method. ResultsIn total, 2,111 patients with HCM (age 61.4 ± 13.6 years; 67.6% men) were analyzed. During the median 4.0 years of follow-up, MACE occurred in 341 patients (16.2%). Among the 4 ML models, the logistic regression model achieved the best AUROC of 0.800 (95% CI: 0.760-0.841) for MACE, 0.789 (95% CI: 0.736-0.841) for all-cause death, 0.798 (95% CI: 0.736-0.860) for HF-adm, and 0.807 (95% CI: 0.754-0.859) for stroke. The discriminant ability of the logistic regression model remained excellent when applied to the external validation cohort for MACE (AUROC = 0.768), all-cause death (AUROC = 0.750), and HF-adm (AUROC = 0.806). The SHAP analysis identified left atrial diameter and hypertension as important variables for all outcomes of interest. ConclusionsThe proposed ML models incorporating various phenotypes from patients with HCM accurately discriminated adverse cardiovascular events and provided variables with high importance for each outcome.

Neuroimaging data repositories and AI-driven healthcare-Global aspirations vs. ethical considerations in machine learning models of neurological disease.

Neuroimaging data repositories are data-rich resources comprising brain imaging with clinical and biomarker data. The potential for such repositories to transform healthcare is tremendous, especially in their capacity to support machine learning (ML) and artificial intelligence (AI) tools. Current discussions about the generalizability of such tools in healthcare provoke concerns of risk of bias-ML models underperform in women and ethnic and racial minorities. The use of ML may exacerbate existing healthcare disparities or cause post-deployment harms. Do neuroimaging data repositories and their capacity to support ML/AI-driven clinical discoveries, have both the potential to accelerate innovative medicine and harden the gaps of social inequities in neuroscience-related healthcare? In this paper, we examined the ethical concerns of ML-driven modeling of global community neuroscience needs arising from the use of data amassed within neuroimaging data repositories. We explored this in two parts; firstly, in a theoretical experiment, we argued for a South East Asian-based repository to redress global imbalances. Within this context, we then considered the ethical framework toward the inclusion vs. exclusion of the migrant worker population, a group subject to healthcare inequities. Secondly, we created a model simulating the impact of global variations in the presentation of anosmia risks in COVID-19 toward altering brain structural findings; we then performed a mini AI ethics experiment. In this experiment, we interrogated an actual pilot dataset (n = 17; 8 non-anosmic (47%) vs. 9 anosmic (53%) using an ML clustering model. To create the COVID-19 simulation model, we bootstrapped to resample and amplify the dataset. This resulted in three hypothetical datasets: (i) matched (n = 68; 47% anosmic), (ii) predominant non-anosmic (n = 66; 73% disproportionate), and (iii) predominant anosmic (n = 66; 76% disproportionate). We found that the differing proportions of the same cohorts represented in each hypothetical dataset altered not only the relative importance of key features distinguishing between them but even the presence or absence of such features. The main objective of our mini experiment was to understand if ML/AI methodologies could be utilized toward modelling disproportionate datasets, in a manner we term "AI ethics." Further work is required to expand the approach proposed here into a reproducible strategy.

Improving Academic Advising in Engineering Education with Machine Learning Using a Real-World Dataset

The role of academic advising has been conducted by faculty-student advisors, who often have many students to advise quickly, making the process ineffective. The selection of the incorrect qualification increases the risk of dropping out, changing qualifications, or not finishing the qualification enrolled in the minimum time. This study harnesses a real-world dataset comprising student records across four engineering disciplines from the 2016 and 2017 academic years at a public South African university. The study examines the relative importance of features in models for predicting student performance and determining whether students are better suited for extended or mainstream programmes. The study employs a three-step methodology, encompassing data pre-processing, feature importance selection, and model training with evaluation, to predict student performance by addressing issues such as dataset imbalance, biases, and ethical considerations. By relying exclusively on high school performance data, predictions are based solely on students’ abilities, fostering fairness and minimising biases in predictive tasks. The results show that removing demographic features like ethnicity or nationality reduces bias. The study’s findings also highlight the significance of the following features: mathematics, physical sciences, and admission point scores when predicting student performance. The models are evaluated, demonstrating their ability to provide accurate predictions. The study’s results highlight varying performance among models and their key contributions, underscoring the potential to transform academic advising and enhance student decision-making. These models can be incorporated into the academic advising recommender system, thereby improving the quality of academic guidance.