Background: The adherence of clinicians to clinical practice guidelines is known to be low, including for the management of COVID-19, due to their difficult use at the point of care and their complexity. Clinical decision support systems have been proposed to implement guidelines and improve adherence. One approach is to permit the navigation inside the recommendations, presented as a decision tree, but the size of the tree often limits this approach and may cause erroneous navigation, especially when it does not fit in a single screen. Methods: We proposed an innovative visual interface to allow clinicians easily navigating inside decision trees for the management of COVID-19 patients. It associates a multi-path tree model with the use of the fisheye visual technique, allowing the visualization of large decision trees in a single screen. To evaluate the impact of this tool on guideline adherence, we conducted a randomized controlled trial in a near-real simulation setting, comparing the decisions taken by medical students using Orient-COVID with those taken with paper guidelines or without guidance, when performing on six realistic clinical cases. Results: The results show that paper guidelines had no impact (p=0.97), while Orient-COVID significantly improved the guideline adherence compared to both other groups (p<0.0003). A significant impact of Orient-COVID was identified on several key points during the management of COVID-19: ordering troponin lab tests, prescribing anticoagulant and oxygen therapy. A multifactor analysis showed no difference between male and female participants. Conclusions: The use of an interactive decision tree for the management of COVID-19 significantly improved the clinician adherence to guidelines. Future works will focus on the integration of the system to electronic health records and on the adaptation of the system to other clinical conditions.

Decision Tree Learning is a fundamental problem for Interpretable Machine Learning, yet it poses a formidable optimization challenge. Despite numerous efforts dating back to the early 1990's, practical algorithms have only recently emerged, primarily leveraging Dynamic Programming (DP) and Branch & Bound (B&B) techniques. These breakthroughs led to the development of two distinct approaches. Algorithms like DL8.5 and MurTree operate on the space of nodes (or branches), they are very fast, but do not penalise complex Decision Trees, i.e. they do not solve for sparsity. On the other hand, algorithms like OSDT and GOSDT operate on the space of Decision Trees, they solve for sparsity but at the detriment of speed. In this work, we introduce Branches, a novel algorithm that integrates the strengths of both paradigms. Leveraging DP and B&B, Branches achieves exceptional speed while also solving for sparsity. Central to its efficiency is a novel analytical bound enabling substantial pruning of the search space. Furthermore, Branches does not necessitate binary features. Theoretical analysis demonstrates that Branches has a lower complexity bound compared to state-of-the-art methods, a claim validated through extensive empirical evaluation. Our results illustrate that Branches outperforms the state of the art in terms of speed and number of iterations while consistently yielding optimal Decision Trees.

Material extrusion is one of the most commonly used approaches within the additive manufacturing processes available. Despite its popularity and related technical advancements, process reliability and quality assurance remain only partially solved. In particular, the surface roughness caused by this process is a key concern. To solve this constraint, experimental plans have been exploited to optimize surface roughness in recent years. However, the latter empirical trial and error process is extremely time- and resource-consuming. Thus, this study aims to avoid using large experimental programs to optimize surface roughness in material extrusion. Methodology. This research provides an in-depth analysis of the effect of several printing parameters: layer height, printing temperature, printing speed and wall thickness. The proposed data-driven predictive modeling approach takes advantage of Machine Learning models to automatically predict surface roughness based on the data gathered from the literature and the experimental data generated for testing. Findings. Using 10-fold cross-validation of data gathered from the literature, the proposed Machine Learning solution attains a 0.93 correlation with a mean absolute percentage error of 13 %. When testing with our own data, the correlation diminishes to 0.79 and the mean absolute percentage error reduces to 8 %. Thus, the solution for predicting surface roughness in extrusion-based printing offers competitive results regarding the variability of the analyzed factors. Originality. As available manufacturing data continue to increase on a daily basis, the ability to learn from these large volumes of data is critical in future manufacturing and science. Specifically, the power of Machine Learning helps model surface roughness with limited experimental tests.

Tree-based ensemble methods such as random forests, gradient-boosted trees, and Bayesianadditive regression trees have been successfully used for regression problems in many applicationsand research studies. In this paper, we study ensemble versions of probabilisticregression trees that provide smooth approximations of the objective function by assigningeach observation to each region with respect to a probability distribution. We prove thatthe ensemble versions of probabilistic regression trees considered are consistent, and experimentallystudy their bias-variance trade-off and compare them with the state-of-the-art interms of performance prediction.

Traffic accidents pose a severe global public health issue, leading to 1.19 million fatalities annually, with the greatest impact on individuals aged 5 to 29 years old. This paper addresses the critical need for advanced predictive methods in road safety by conducting a comprehensive review of recent advancements in applying machine learning (ML) techniques to traffic accident analysis and prediction. It examines 191 studies from the last five years, focusing on predicting accident risk, frequency, severity, duration, as well as general statistical analysis of accident data. To our knowledge, this study is the first to provide such a comprehensive review, covering the state-of-the-art across a wide range of domains related to accident analysis and prediction. The review highlights the effectiveness of integrating diverse data sources and advanced ML techniques to improve prediction accuracy and handle the complexities of traffic data. By mapping the current landscape and identifying gaps in the literature, this study aims to guide future research towards significantly reducing traffic-related deaths and injuries by 2030, aligning with the World Health Organization (WHO) targets.

Random Forests are widely claimed to capture interactions well. However, some simple examples suggest that they perform poorly in the presence of certain pure interactions that the conventional CART criterion struggles to capture during tree construction. We argue that simple alternative partitioning schemes used in the tree growing procedure can enhance identification of these interactions. In a simulation study we compare these variants to conventional Random Forests and Extremely Randomized trees. Our results validate that the modifications considered enhance the model's fitting ability in scenarios where pure interactions play a crucial role.

New technologies such as Machine Learning (ML) gave great potential for evaluating industry workflows and automatically generating key performance indicators (KPIs). However, despite established standards for measuring the efficiency of industrial machinery, there is no precise equivalent for workers' productivity, which would be highly desirable given the lack of a skilled workforce for the next generation of industry workflows. Therefore, an ML solution combining data from manufacturing processes and workers' performance for that goal is required. Additionally, in recent times intense effort has been devoted to explainable ML approaches that can automatically explain their decisions to a human operator, thus increasing their trustworthiness. We propose to apply explainable ML solutions to differentiate between expert and inexpert workers in industrial workflows, which we validate at a quality assessment industrial workstation. Regarding the methodology used, input data are captured by a manufacturing machine and stored in a NoSQL database. Data are processed to engineer features used in automatic classification and to compute workers' KPIs to predict their level of expertise (with all classification metrics exceeding 90 %). These KPIs, and the relevant features in the decisions are textually explained by natural language expansion on an explainability dashboard. These automatic explanations made it possible to infer knowledge from expert workers for inexpert workers. The latter illustrates the interest of research in self-explainable ML for automatically generating insights to improve productivity in industrial workflows.

Tree ensembles achieve state-of-the-art performance on numerous prediction tasks. We propose Joint Optimization of Piecewise Linear ENsembles (JOPLEN), which jointly fits piecewise linear models at all leaf nodes of an existing tree ensemble. In addition to enhancing the expressiveness of an ensemble, JOPLEN allows several common penalties, including sparsity-promoting matrix norms and subspace-norms, to be applied to nonlinear prediction. We demonstrate the performance of JOPLEN on over 100 regression and classification datasets and with a variety of penalties. JOPLEN leads to improved prediction performance relative to not only standard random forest and gradient boosted tree ensembles, but also other methods for enhancing tree ensembles. We demonstrate that JOPLEN with a nuclear norm penalty learns subspace-aligned functions. Additionally, JOPLEN combined with a Dirty LASSO penalty is an effective feature selection method for nonlinear prediction in multitask learning.

The rise of machine learning methods on heavily resource constrained devices requires not only the choice of a suitable model architecture for the target platform, but also the optimization of the chosen model with regard to execution time consumption for inference in order to optimally utilize the available resources. Random forests and decision trees are shown to be a suitable model for such a scenario, since they are not only heavily tunable towards the total model size, but also offer a high potential for optimizing their executions according to the underlying memory architecture. In addition to the straightforward strategy of enforcing shorter paths through decision trees and hence reducing the execution time for inference, hardware-aware implementations can optimize the execution time in an orthogonal manner. One particular hardware-aware optimization is to layout the memory of decision trees in such a way, that higher probably paths are less likely to be evicted from system caches. This works particularly well when splits within tree nodes are uneven and have a high probability to visit one of the child nodes. In this paper, we present a method to reduce path lengths by rewarding uneven probability distributions during the training of decision trees at the cost of a minimal accuracy degradation. Specifically, we regularize the impurity computation of the CART algorithm in order to favor not only low impurity, but also highly asymmetric distributions for the evaluation of split criteria and hence offer a high optimization potential for a memory architecture-aware implementation. We show that especially for binary classification data sets and data sets with many samples, this form of regularization can lead to an reduction of up to approximately four times in the execution time with a minimal accuracy degradation.

Computational notebooks have become the primary coding environment for data scientists. However, research on their code quality is still emerging, and the code shared is often of poor quality. Given the importance of maintenance and reusability, understanding the metrics that affect notebook code comprehensibility is crucial. Code understandability, a qualitative variable, is closely tied to user opinions. Traditional approaches to measuring it either use limited questionnaires to review a few code pieces or rely on metadata such as likes and votes in software repositories. Our approach enhances the measurement of Jupyter notebook understandability by leveraging user comments related to code understandability. As a case study, we used 542,051 Kaggle Jupyter notebooks from our previous research, named DistilKaggle. We employed a fine-tuned DistilBERT transformer to identify user comments associated with code understandability. We established a criterion called User Opinion Code Understandability (UOCU), which considers the number of relevant comments, upvotes on those comments, total notebook views, and total notebook upvotes. UOCU proved to be more effective than previous methods. Furthermore, we trained machine learning models to predict notebook code understandability based solely on their metrics. We collected 34 metrics for 132,723 final notebooks as features in our dataset, using UOCU as the label. Our predictive model, using the Random Forest classifier, achieved 89% accuracy in predicting the understandability levels of computational notebooks.