Additive Trees Research Articles

Modelling the potential range of Agrilus planipennis in Europe according to current and future climate conditions

Agrilus planipennis, the emerald ash borer, is a species native to East Asia that was accidentally introduced to North America and Eastern Europe. In North America, it is responsible for tremendous damage. In Europe, its range has quickly expanded from the east where it was introduced in 2003, and it threatens the species of the genus Fraxinus. We developed an ensemble modelling approach to model the potential range of A. planipennis according to current climate conditions and four scenarios of climate change: SSP1–2.6, SSP2–4.5, SSP3–7.0 SSP5–8.5 in the period 2041–2060. We used three algorithms; random forest, boosted regression trees and Bayesian additive regression trees with occurrence data from both native and invaded ranges. The results indicate that most of the European continent is climatically suitable for A. planipennis. In Western Europe, the northern limit of the range is located in the British Isles and southern Scandinavia. The projection of the models according to estimates of future climate conditions shows that climate suitability would mostly remain unchanged in 2041–2060. During that period, the potential range is expected to slightly shrink in the south, around the Mediterranean Basin, and expand at its northern limit. Our results confirm that A. planipennis is, and will remain, a major threat to forest and ornamental ash tree health across Europe.

Partially fixed bayesian additive regression trees

Bayesian Additive Regression Trees (BART) is a widely popular nonparametric regression model known for its accurate prediction capabilities. In certain situations, there is knowledge suggesting the existence of certain dominant variables. However, the BART model fails to fully utilize the knowledge. To tackle this problem, the paper introduces a modification to BART known as the Partially Fixed BART model. By fixing a portion of the trees' structure, this model enables more efficient utilization of prior knowledge, resulting in enhanced estimation accuracy. Moreover, the Partially Fixed BART model can offer more precise estimates and valuable insights for future analysis even when such prior knowledge is absent. Empirical results substantiate the enhancement of the proposed model in comparison to the original BART.

The drug loading capacity prediction and cytotoxicity analysis of metal–organic frameworks using stacking algorithms of machine learning

Metal–organic frameworks (MOFs) have shown excellent performance in the field of drug delivery. Despite the synthesis of a vast array of MOFs exceeding 100,000 varieties, certain formulations have exhibited suboptimal performance characteristics. Therefore, there is a pressing need to enhance their efficacy by identifying MOFs with superior drug loading capacities and minimal cytotoxicity, which can be achieved through machine learning (ML). In this study, a stacking regression model was developed to predict drug loading capacity and cytotoxicity of MOFs using datasets compiled from various literature sources. The model exhibited exceptional predictive capabilities, achieving R2 values of 0.907 for drug loading capacity and 0.856 for cytotoxicity. Furthermore, various model interpretation methods including partial dependence plots, individual conditional expectation, Shapley additive explanation, decision tree, random forest, CatBoost Regressor, and light gradient-boosting machine were employed for feature importance analysis. The results revealed that specific metal atoms such as Zn, Cr, Fe, Zr, and Cu significantly influenced the drug loading capacity and cytotoxicity of MOFs. Through model validation encompassing experimental validation and computational verification, the reliability of the model was thoroughly established. In general, it is a good practice to use ML methods for predicting drug loading capacity and cytotoxicity analysis of MOFs, guiding the development of future property prediction methods for MOFs.

Demonstrating the utility of Instrumented Gait Analysis in the treatment of children with cerebral palsy.

Instrumented gait analysis (IGA) has been around for a long time but has never been shown to be useful for improving patient outcomes. In this study we demonstrate the potential utility of IGA by showing that machine learning models are better able to estimate treatment outcomes when they include both IGA and clinical (CLI) features compared to when they include CLI features alone. We carried out a retrospective analysis of data from ambulatory children diagnosed with cerebral palsy who were seen at least twice at our gait analysis center. Individuals underwent a variety of treatments (including no treatment) between sequential gait analyses. We fit Bayesian Additive Regression Tree (BART) models that estimated outcomes for mean stance foot progression to demonstrate the approach. We built two models: one using CLI features only, and one using CLI and IGA features. We then compared the models' performance in detail. We performed similar, but less detailed, analyses for a number of other outcomes. All results were based on independent test data from a 70%/30% training/testing split. The IGA model was more accurate than the CLI model for mean stance-phase foot progression outcomes (RMSEIGA = 11∘, RMSECLI = 13∘) and explained more than 1.5 × as much of the variance (R2IGA = .45, R2CLI = .28). The IGA model outperformed the CLI model for every level of treatment complexity, as measured by number of simultaneous surgeries. The IGA model also exhibited superior performance for estimating outcomes of mean stance-phase knee flexion, mean stance-phase ankle dorsiflexion, maximum swing-phase knee flexion, gait deviation index (GDI), and dimensionless speed. The results show that IGA has the potential to be useful in the treatment planning process for ambulatory children diagnosed with cerebral palsy. We propose that the results of machine learning outcome estimators-including estimates of uncertainty-become the primary IGA tool utilized in the clinical process, complementing the standard medical practice of conducting a through patient history and physical exam, eliciting patient goals, reviewing relevant imaging data, and so on.

Predicting post-treatment symptom severity for adults receiving psychological therapy in routine care for generalised anxiety disorder: a machine learning approach

Approximately half of generalised anxiety disorder (GAD) patients do not recover from first-line treatments, and no validated prediction models exist to inform individuals or clinicians of potential treatment benefits. This study aimed to develop and validate an accurate and explainable prediction model of post-treatment GAD symptom severity. Data from adults receiving treatment for GAD in eight Improving Access to Psychological Therapies (IAPT) services (n=15,859) were separated into training, validation and holdout datasets. Thirteen machine learning algorithms were compared using 10-fold cross-validation, against two simple clinically relevant comparison models. The best-performing model was tested on the holdout dataset and model-specific explainability measures identified the most important predictors. A Bayesian Additive Regression Trees model out-performed all comparison models (MSE=16.54 [95 % CI=15.58; 17.51]; MAE=3.19; R²=0.33, including a single predictor linear regression model: MSE=20.70 [95 % CI=19.58; 21.82]; MAE=3.94; R²=0.14). The five most important predictors were: PHQ-9 anhedonia, GAD-7 annoyance/irritability, restlessness and fear items, then the referral-assessment waiting time. The best-performing model accurately predicted post-treatment GAD symptom severity using only pre-treatment data, outperforming comparison models that approximated clinical judgement and remaining within the GAD-7 error of measurement and minimal clinically important differences. This model could inform treatment decision-making and provide desired information to clinicians and patients receiving treatment for GAD.

Identification of mycoplasma pneumonia in children based on fusion of multi-modal clinical free-text description and structured test data.

Mycoplasma pneumonia may lead to hospitalizations and pose life-threatening risks in children. The automated identification of mycoplasma pneumonia from electronic medical records holds significant potential for improving the efficiency of hospital resource allocation. In this study, we proposed a novel method for identifying mycoplasma pneumonia by integrating multi-modal features derived from both free-text descriptions and structured test data in electronic medical records. Our approach begins with the extraction of free-text and structured data from clinical records through a systematic preprocessing pipeline. Subsequently, we employ a pre-trained transformer language model to extract features from the free-text, while multiple additive regression trees are used to transform features from the structured data. An attention-based fusion mechanism is then applied to integrate these multi-modal features for effective classification. We validated our method using clinic records of 7157 patients, retrospectively collected for training and testing purposes. The experimental results demonstrate that our proposed multi-modal fusion approach achieves significant improvements over other methods across four key performance metrics.

Development of a hard cider flavor wheel using free word sorting and multivariate statistical techniques

AbstractFlavor wheels are visual tools built from standardized sensory lexicons that are used in many different industries to improve communication, marketing, and quality discrimination among products. To date, flavor wheels have been developed for spices, coffee, wine, beer, and many other foods and beverages—yet no flavor wheel has been constructed for hard cider. While there is no single established method for constructed flavor wheels, most approaches are based on sensory descriptive analysis (DA) and free word sorting activities that investigate the semantic similarity and dissimilarity of descriptive terms. This research study utilized multiple DA studies for the generation of a sensory lexicon, followed by independent word sorting tasks with cider industry professionals (N = 40) and untrained consumers (N = 58) to establish two flavor wheels that are broadly understandable to a variety of industry stakeholders. Based on the results of DISTATIS and additive tree partitioning, this research showcases a workflow for developing and refining flavor wheels that incorporate both stakeholder and researcher input and can be built upon by other analysts.Practical ApplicationsThis research demonstrates an accessible methodology for developing flavor wheels that incorporates input from diverse parties and considers the semantic associations of terms used by cider consumers and industry professionals. The proposed methodology is a useful framework for other sensory scientists seeking to efficiently develop flavor wheels for multiple audiences. As a demonstration, this research also delivers two flavor wheels—one that highlights discrepancies in how industry consumers and producers semantically understand sensory experiences compared to trained sensory personnel, and a second wheel that showcases how hard cider can more objectively be described. Together, these flavor wheels are tools for improving sensory communication, education, and marketing in the US hard cider industry.

Generalized Bayesian Additive Regression Trees Models: Beyond Conditional Conjugacy

Bayesian additive regression trees have seen increased interest in recent years due to their ability to combine machine learning techniques with principled uncertainty quantification. The Bayesian backfitting algorithm used to fit BART models, however, limits their application to a small class of models for which conditional conjugacy exists. In this article, we greatly expand the domain of applicability of BART to arbitrary generalized BART models by introducing a very simple, tuning-parameter-free, reversible jump Markov chain Monte Carlo algorithm. Our algorithm requires only that the user be able to compute the likelihood and (optionally) its gradient and Fisher information. The potential applications are very broad; we consider examples in survival analysis, structured heteroskedastic regression, and gamma shape regression.

Implementation of Proximal and Remote Soil Sensing, Data Fusion and Machine Learning to Improve Phosphorus Spatial Prediction for Farms in Ontario, Canada

One of the challenges in site-specific phosphorus (P) management is the substantial spatial variability in plant available P across fields. To overcome this barrier, emerging sensing, data fusion, and spatial predictive modeling approaches are needed to accurately reveal the spatial heterogeneity of P. Seven spatially variable fields located in Ontario, Canada are clustered into two zones; four fields are located in eastern Ontario and three others are located in western Ontario. This study compares Bayesian Additive Regression Trees (BART), Support Vector Machine regressor (SVM), and Ordinary Kriging (OK), along with novel data fusion concepts, to analyze integrated high-density spatial data layers related to spatial variability in soil available P. Feature selection and interaction detection using BART variable selection and Recursive Feature Elimination (RFE) for SVM were applied to 42 predictors, including soil-vegetation indices derived from PlanetScope multispectral imagery, high-density apparent soil electrical conductivity (ECa), and high-resolution topographic attributes derived from DUALEM-21S and a Real-Time Kinematic (RTK) global navigation satellite systems (GNSS) receiver, respectively. Modeling spatial heterogeneity of soil available P with BART showed higher accuracy than SVM and OK in both zones of this study when trained and tested on ground truth data from clusters of farms. A BART variable selection approach resulted in six auxiliary predictors of soil available P in the eastern zone, while only four predictors were selected to predict P in the western zone. RFE for SVM resulted in models with 15 and 12 auxiliary predictors in the eastern and western Ontario zones. Topographic elevation was the most influential predictor of soil available P in both zones. Compared with the SVM and OK methods, BART exhibited lower average RMSE values for individual fields of 1.86 ppm and 3.58 ppm across the eastern and western Ontario zones, respectively, along with higher R2 values of 0.85 and 0.83, respectively. In contrast, SVM had RMSE values for individual fields in the eastern and western Ontario zones, respectively, averaging 5.04 ppm and 7.51 ppm and R2 values of 0.27 and 0.43. RMSE values for soil available P in individual fields across the eastern and western Ontario zones averaged 4.77 ppm and 7.81 ppm, respectively, with the OK method, while R2 values averaged 0.19 and 0.44. The selection of suitable auxiliary predictors and data fusion, combined with BART spatial machine learning algorithms, have potential to be a useful tool to accurately estimate spatial patterns in soil available P for agricultural fields in Ontario, Canada.

Pine processionary moth outbreaks and droughts have different tree ring signatures in Mediterranean pines

Defoliating insects’ outbreaks play a critical role in trees’ carbon cycle. The pine processionary moth (PPM; Thaumetopoea pityocampa) is the major defoliating insect of Mediterranean coniferous forests. The frequency and intensity of PPM outbreaks is projected to increase as winter temperatures become milder due to climate warming. An accurate evaluation of this projection requires a wide spatial baseline of the historical PPM incidence. PPM outbreaks affect tree secondary growth leading to narrow rings, providing a tree level signal. However, PPM defoliation rings can be confounded with drought rings, the most frequent cause of narrow rings in Mediterranean environments. Thus, an accurate identification of PPM rings demands the consideration of additional tree ring traits. Here, we introduce a multiproxy approach to identify and distinguish PPM and drought events. We sampled four Pinus nigra (3) and P. sylvestris (1) stands in Spain. We identified and verified years of PPM defoliation using remote sensing analysis and field observations of Regional Forest Service. We identified drought events through the combination of climatic data with radial growth reductions. We considered climate growth residuals, among-trees growth variability, latewood percentage (%LW), intrinsic water-use efficiency (iWUE) and minimum blue intensity (BI) to discern between droughts and PPM outbreaks. In comparison with drought rings, PPM rings showed 1) more negative residuals in climate growth models, 2) higher secondary growth variance, 3) higher percentage of latewood and 4) lower iWUE. Minimum BI did not differ between drought and PPM rings, but was lower than in the rest of the rings. The combination of these traits provides a signature to identify PPM rings, opening the opportunity to reconstruct PPM incidence on a broad spatio-temporal scale.

Abstract P405: Calibrating Routine Blood Pressures Among Preterm Infants: A Retrospective, Cross-Sectional Analysis

Background: Among preterm infants (< 12 months postnatal age and born < 37 weeks gestation), oscillometric systolic blood pressure (SBP) measurements are error-prone. However, some preterm infants have “gold standard” SBPs measured using an arterial line, in addition to the error-prone oscillometric SBPs. Objective: To compare different statistical methods to calibrate oscillometric SBPs to mimic arterial line SBPs Methods: We sampled participants from an existing pilot study of preterm infants treated with an antihypertensive therapy at the University of Alabama at Birmingham. Participants' data was obtained from the hospital's data warehouse for electronic medical records. Participants for this analysis had at least one arterial line SBP measurement, and for each of these "gold standard" measurements we identified the closest paired oscillometric SBP measurement (<10 minutes apart). We also collected participant factors such as race (white vs. black/African American), gender, gestational age at birth (weeks), postnatal age at arterial line SBP measurement (weeks), and whether the participant had received an antihypertensive medication. We predicted the arterial line SBPs from oscillometric SBPs and covariates using linear regression, linear regression with restricted cubic splines, random forest, and Bayesian additive regression trees (BART). Prediction errors were calculated using 10-fold cross-validation. We used complete case analysis. Results: We identified 822 measurements among 28 participants. Ten of 28 (36%) participants were black and 13 of 28 (46%) were female. The median (Q1, Q3) gestational age at birth was 33 (29, 35) weeks. The root mean squared error for predicting arterial line SBP from oscillometric SBP by itself was 17.1 mmHg. The root mean squared error of the predicted arterial line SPBs from different statistical methods varied little from the most error (linear regression with restricted cubic splines: 13.5 mmHg) to the least error (BART: 12.6 mmHg), meaning that any calibration resulted in at least a 21% reduction in error compared to the oscillometric SBPs. BART tended to underpredict the arterial line SBPs that were above 100 mmHg. Conclusions: Among preterm infants, statistical calibration is a viable strategy for reducing the variability of error-prone oscillometric SBPs. The different statistical models performed similarly in this small pilot study, but a larger sample size using additional important variables (e.g., birth weight) is required to confirm this finding.

A comparison of Bayesian and frequentist approaches to incorporating clinical and biological information for the prediction of response to standardized pediatric colitis therapy.

The prospective cohort study PROTECT is the largest study in pediatric ulcerative colitis (UC) with standardized treatments, providing valuable data for predicting clinical outcomes. PROTECT and previous studies have identified characteristics associated with clinical outcomes. In this study, we aimed to compare predictive modeling between Bayesian analysis including machine learning and frequentist analysis. The key outcomes for this analysis were week 4, 12 and 52 corticosteroid (CS)-free remission following standardized treatment from diagnosis. We developed predictive modeling with multivariable Bayesian logistic regression (BLR), Bayesian additive regression trees (BART) and frequentist logistic regression (FLR). The effect estimate of each risk factor was estimated and compared between the BLR and FLR models. The predictive performance of the models was assessed including area under curve (AUC) of the receiver operating characteristic (ROC) curve. Ten-fold cross-validation was performed for internal validation of the models. The estimation contained 95% credible (or confidence) interval (CI). The statistically significant associations between the risk factors and early or late outcomes were consistent between all BLR and FLR models. The model performance was similar while BLR and BART models had narrower credible intervals of AUCs. To predict week 4 CS-free remission, the BLR model had AUC of 0.69 (95% CI 0.67-0.70), the BART model had AUC of 0.70 (0.67-0.72), and the FLR had AUC of 0.70 (0.65-0.76). To predict week 12 CS-free remission, the BLR model had AUC of 0.78 (0.77-0.79), the BART model had AUC of 0.78 (0.77-0.79), and the FLR model had AUC of 0.79 (0.74-0.83). To predict week 52 CS-free remission, the BLR model had AUC of 0.69 (0.68-0.70), the BART model had AUC of 0.69 (0.67-0.70), and the FLR model had AUC of 0.69 (0.64-0.74). The BART model identified nonlinear associations. BLR and BART models had intuitive interpretation on interval estimation, better precision in estimating the AUC and can be alternatives for predicting clinical outcomes in pediatric patients with UC. BART model can estimate nonlinear nonparametric association.

Investigating Growth-at-Risk Using a Multicountry Nonparametric Quantile Factor Model

We develop a nonparametric quantile panel regression model. Within each quantile, the quantile function is a combination of linear and nonlinear parts, which we approximate using Bayesian Additive Regression Trees (BART). Cross-sectional information is captured through a conditionally heteroscedastic latent factor. The nonparametric feature enhances flexibility, while the panel feature increases the number of observations in the tails. We develop Bayesian methods for inference and apply several versions of the model to study growth-at-risk dynamics in a panel of 11 advanced economies. Our framework usually improves upon single-country quantile models in recursive growth forecast comparisons.

Extinction risk predictions for the world's flowering plants to support their conservation.

More than 70% of all vascular plants lack conservation status assessments. We aimed to address this shortfall in knowledge of species extinction risk by using the World Checklist of Vascular Plants to generate the first comprehensive set of predictions for a large clade: angiosperms (flowering plants, c. 330 000 species). We used Bayesian Additive Regression Trees (BART) to predict the extinction risk of all angiosperms using predictors relating to range size, human footprint, climate, and evolutionary history and applied a novel approach to estimate uncertainty of individual species-level predictions. From our model predictions, we estimate 45.1% of angiosperm species are potentially threatened with a lower bound of 44.5% and upper bound of 45.7%. Our species-level predictions, with associated uncertainty estimates, do not replace full global, or regional Red List assessments, but can be used to prioritise predicted threatened species for full Red List assessment and fast-track predicted non-threatened species for Least Concern assessments. Our predictions and uncertainty estimates can also guide fieldwork, inform systematic conservation planning and support global plant conservation efforts and targets.

A BAYESIAN MACHINE LEARNING APPROACH FOR ESTIMATING HETEROGENEOUS SURVIVOR CAUSAL EFFECTS: APPLICATIONS TO A CRITICAL CARE TRIAL.

Assessing heterogeneity in the effects of treatments has become increasingly popular in the field of causal inference and carries important implications for clinical decision-making. While extensive literature exists for studying treatment effect heterogeneity when outcomes are fully observed, there has been limited development in tools for estimating heterogeneous causal effects when patient-centered outcomes are truncated by a terminal event, such as death. Due to mortality occurring during study follow-up, the outcomes of interest are unobservable, undefined, or not fully observed for many participants in which case principal stratification is an appealing framework to draw valid causal conclusions. Motivated by the Acute Respiratory Distress Syndrome Network (ARDSNetwork) ARDS respiratory management (ARMA) trial, we developed a flexible Bayesian machine learning approach to estimate the average causal effect and heterogeneous causal effects among the always-survivors stratum when clinical outcomes are subject to truncation. We adopted Bayesian additive regression trees (BART) to flexibly specify separate mean models for the potential outcomes and latent stratum membership. In the analysis of the ARMA trial, we found that the low tidal volume treatment had an overall benefit for participants sustaining acute lung injuries on the outcome of time to returning home but substantial heterogeneity in treatment effects among the always-survivors, driven most strongly by biologic sex and the alveolar-arterial oxygen gradient at baseline (a physiologic measure of lung function and degree of hypoxemia). These findings illustrate how the proposed methodology could guide the prognostic enrichment of future trials in the field.

Does crouch alter the effects of neuromuscular impairments on gait? A simulation study

Cerebral palsy (CP) is a neurologic injury that impacts control of movement. Individuals with CP also often develop secondary impairments like weakness and contracture. Both altered motor control and secondary impairments influence how an individual walks after neurologic injury. However, understanding the complex interactions between and relative effects of these impairments makes analyzing and improving walking capacity in CP challenging. We used a sagittal-plane musculoskeletal model and neuromuscular control framework to simulate crouch and nondisabled gait. We perturbed each simulation by varying the number of synergies controlling each leg (altered control), and imposed weakness and contracture. A Bayesian Additive Regression Trees (BART) model was also used to parse the relative effects of each impairment on the muscle activations required for each gait pattern. By using these simulations to evaluate gait-pattern specific effects of neuromuscular impairments, we identified some advantages of crouch gait. For example, crouch tolerated 13 % and 22 % more plantarflexor weakness than nondisabled gait without and with altered control, respectively. Furthermore, BART demonstrated that plantarflexor weakness had twice the effect on total muscle activity required during nondisabled gait than crouch gait. However, crouch gait was also disadvantageous in the presence of vasti weakness: crouch gait increased the effects of vasti weakness on gait without and with altered control. These simulations highlight gait-pattern specific effects and interactions between neuromuscular impairments. Utilizing computational techniques to understand these effects can elicit advantages of gait deviations, providing insight into why individuals may select their gait pattern and possible interventions to improve energetics.

The impact of early childhood education and medical care on children

Causal inference is a statistical approach that aims to understand and quantify the causal relationship between variables, allowing us to determine the impact of one variable on another while accounting for potential confounding factors. In this study, we chose the Infant Health and Development Program (IHDP) dataset to test whether high-quality early childhood education and medical care will enhance their cognitive and academic ability. We use some common and classic algorithms to achieve this study by calculating the CATE (Conditional Average Treatment Effect) of the dataset; if the result is a negative number, that means early treatment doesn’t improve children’s further ability; if the result is a positive number, that means treatment improves it. After fitting the targeted models, BART(Bayesian Additive Regression Trees) and Metalearners, into this dataset, we found out that all the models will get positive CATE, which means that these early treatments positively affect children’s future development. Also, we can judge which model gives us a more accurate result according to the standard variance of TE(treatment effect). The result of this study will provide us with an insightful idea about whether we should give children some treatments regarding education and medical care and which model is more suitable for this casual inference test.

Who Benefited Most from the Internet-Based Conversational Engagement RCT (I-CONECT)? Application of the Personalized Medicine Approach to a Behavioral Intervention Study

BackgroundMany Alzheimer’s Disease (AD) clinical trials have failed to demonstrate treatment efficacy on cognition. It is conceivable that a complex disease like AD may not have the same treatment effect due to many heterogeneities of disease processes and individual traits.ObjectivesWe employed an individual-level treatment response (ITR) approach to determine the characteristics of treatment responders and estimated time saved in cognitive decline using the Internet-based Conversational Engagement Clinical Trial (I-CONECT) behavioral intervention study as a model.Design and SettingI-CONECT is a multi-site, single-blind, randomized controlled trial aimed to improve cognitive functions through frequent conversational interactions via internet/webcam. The experimental group engaged in video chats with study staff 4 times/week for 6 months; the control group received weekly 10-minute check-in phone calls.ParticipantsOut of 186 randomized participants, current study used 139 participants with complete information on both baseline and 6-month follow-up (73 with mild cognitive impairment (MCI), 66 with normal cognition; 64 in the experimental group, and 75 in the control group).MeasurementsITR scores were generated for the Montreal Cognitive Assessment (MoCA) (global cognition, primary outcome) and Category Fluency Animals (CFA) (semantic fluency, secondary outcome) that showed significant efficacy in the trial. ITR scores were generated through 300 iterations of 3-fold cross-validated random forest models. The average treatment difference (ATD) curve and the area between the curves (ABC) were estimated to measure the heterogeneity of treatment responses. Responder traits were identified using SHapley Additive exPlanations (SHAP) and decision tree models. The time saved in cognitive decline was explored to gauge clinical meaningfulness.ResultsABC statistics showed substantial heterogeneity in treatment response with MoCA but modest heterogeneity in treatment response with CFA. Age, cognitive status, time spent with family and friends, education, and personality were important characteristics that influenced treatment responses. Intervention group participants in the upper 30% of ITR scores demonstrated potential delays of 3 months in semantic fluency (CFA) and 6 months in global cognition (MoCA), assuming a 5-fold faster natural cognitive decline compared to the control group during the post-treatment period.ConclusionsITR-based analyses are valuable in profiling treatment responders for features that can inform future trial design and clinical practice. Reliably measuring time saved in cognitive decline is an area of ongoing research to gain insight into the clinical meaningfulness of treatment.

Visualisations for Bayesian Additive Regression Trees

Tree-based regression and classification has become a standard tool in modern data science. Bayesian Additive Regression Trees (BART) has in particular gained wide popularity due its flexibility in dealing with interactions and non-linear effects. BART is a Bayesian tree-based machine learning method that can be applied to both regression and classification problems and yields competitive or superior results when compared to other predictive models. As a Bayesian model, BART allows the practitioner to explore the uncertainty around predictions through the posterior distribution. In this paper, we present new Visualisation techniques for exploring BART models. We construct conventional plots to analyse a model’s performance and stability as well as create new tree-based plots to analyse variable importance, interaction, and tree structure. We employ Value Suppressing Uncertainty Palettes (VSUP) to construct heatmaps that display variable importance and interactions jointly using colour scale to represent posterior uncertainty. Our new Visualisations are designed to work with the most popular BART R packages available, namely BART, dbarts, and bartMachine. Our approach is implemented in the R package bartMan (BART Model ANalysis).

Early ICD implantation following out-of-hospital cardiac arrest: a retrospective cohort study from the Swedish Registry for Cardiopulmonary Resuscitation

BackgroundIt is unclear whether an implantable cardioverter-defibrillator (ICD) is generally beneficial in survivors of out-of-hospital cardiac arrest (OHCA).ObjectiveWe studied the association between ICD implantation prior to discharge and survival in...