The extreme rise of the Internet of Things and the increasing access of people to web applications have led to the expanding use of diverse e-commerce solutions, which was even more obvious during the COVID-19 pandemic. Large amounts of heterogeneous data from multiple sources reside in e-commerce environments and are often characterized by data source inaccuracy and unreliability. In this regard, various fusion techniques can play a crucial role in addressing such challenges and are extensively used in numerous e-commerce applications. This paper’s goal is to conduct an academic literature review of prominent fusion-based solutions that can assist in tackling the everyday challenges the e-commerce environments face as well as in their needs to make more accurate and better business decisions. For categorizing the solutions, a novel 4-fold categorization approach is introduced including product-related, economy-related, business-related, and consumer-related solutions, followed by relevant subcategorizations, based on the wide variety of challenges faced by e-commerce. Results from the 65 fusion-related solutions included in the paper show a great variety of different fusion applications, focusing on the fusion of already existing models and algorithms as well as the existence of a large number of different machine learning techniques focusing on the same e-commerce-related challenge.

Intensive Care Unit (ICU) readmissions are associated with increased morbidity, mortality, and healthcare costs. Therefore, determining an appropriate timing of ICU discharge is critical. In this context, deep learning (DL) approaches have attracted significant attention. We conducted a systematic review of studies developing or validating DL models for ICU readmission prediction, published up to March 4th, 2025, and indexed in PubMed, Embase, Scopus, and Web of Science. We summarised them along multiple dimensions, including outcome and population definition, DL architecture, reproducibility, generalizability, and explainability, and provided a meta-analytic estimate of model performance. We included 24 studies encompassing 49 DL models, predominantly trained on US-based datasets, and rarely subjected to external validation. There was considerable variability across study settings, including the definition and timeframe of the ICU readmission outcome, as well as DL architecture used, alongside a substantial risk of bias. Technical reproducibility and model interpretation were rare. A meta-analysis of AUROC values from 11 studies yielded a mean of 0.78 (95% CI = 0.72–0.84), with very high heterogeneity (I2 = 99.9%). Models targeting disease-specific ICU subpopulations achieved significantly higher performance (mean AUROC = 0.92, 95% CI = 0.89–0.95, p = 0.002), and substantially lower heterogeneity (I2 = 17.1%). DL models showed promising performances in predicting ICU readmissions, but exhibited several shortcomings, including low reproducibility, over-reliance on a few US-based datasets, and limited explainability. Additionally, the high heterogeneity and risk of bias limited our ability to assess their pooled performance through meta-analysis. Taken together, our observations suggest that the quality of the evidence regarding the application of DL approaches to ICU readmission prediction is poor, thus hindering their clinical applicability.

The rapid evolution of sensors and communication technologies has led to the production and transfer of mass data streams from vehicles either inside their electronic units or to the outside world using the internet infrastructure. The “outside world”, in most cases, consists of third-party applications, such as fleet or traffic management control centers, which utilize vehicular data for reporting and monitoring functionalities. Such applications, in most cases, in order to facilitate their needs, require the exchange and processing of vast amounts of data which can be handled by the so-called Big Data technologies. The purpose of this study is to present a hybrid platform suitable for data collection, storing and analysis enhanced with quality control actions. In particular, the collected data contain various formats originating from different vehicle sensors and are stored in the aforementioned platform in a continuous way. The stored data in this platform must be checked in order to determine and validate them in terms of quality. To do so, certain actions, such as missing values checks, format checks, range checks, etc., must be carried out. The results of the quality control functions are presented herein, and useful conclusions are drawn in order to avoid possible data quality problems which may occur in further analysis and use of the data, e.g., for training of artificial intelligence models.

Intensive Care Unit (ICU) readmissions are associated with adverse clinical outcomes and increased healthcare costs. Although existing models for predicting 30-day ICU readmission show high predictive performance, they fail to account for model uncertainty, potentially resulting in overconfident and unreliable decision-making. We propose a novel Ensemble Bayesian Model Averaging (EBMA)-based framework which balances predictive discrimination with uncertainty by penalizing models that are confident but incorrect. It achieved excellent calibration (Brier score = 0.051), while maintaining discriminatory performance comparable to or exceeding that of the best individual models (AUROC > 0.716). These findings suggest that our EBMA-based framework provides a more robust and clinically reliable approach for ICU readmission prediction and decision support.

Introduction Intensive Care Unit (ICU) readmissions increase morbidity, mortality, and healthcare costs. Existing deep learning (DL) models for predicting 30-day ICU readmission show limited clinical utility due to poor calibration and overconfident predictions that can mislead clinicians. While combining multiple predictive models to improve overall performance is a well-established and often effective strategy, the most widely used ensemble approaches overlook model uncertainty, which can still lead to overconfident decision-making. In contrast, Ensemble Bayesian Model Averaging (EBMA) provides a robust framework for incorporating model uncertainty. While EBMA rewards discriminative power, it also penalizes models that provide overconfident yet inaccurate predictions, resulting in more reliable and clinically meaningful risk estimates. Aims To develop an EBMA-based framework to integrate predictions of 30-day ICU readmission from multiple DL, machine learning (ML), and traditional scoring models. Methods We analyzed first ICU admissions for adult patients (≥18 years) from the MIMIC-III database (n=32,784 stays). We randomly split the data into training (80%), calibration (13%), and test (7%) sets. Drawing from our own and other systematic reviews, we adapted 17 DL models — including RNN-based architectures, CNN-LSTM combinations, and a transformer-based model — and 6 ML models (two logistic regression models, Naïve Bayes, random forest, support vector machine, and XGBoost), training all models on the same training set. Their predictions, together with the traditional SWIFT and APACHE-II scores, served as input for our EBMA-based framework. Model weights were then estimated on the calibration set. We assessed the calibration and discrimination of the obtained ensemble on the test set using the Brier Score and AUROC, respectively. We shared the code supporting this study at https://github.com/ekoumantakis/ebma-icu-readmission. Results Our EBMA-based framework achieved a Brier Score of 0.051 and an AUROC of 0.728, outperforming all individual models in calibration while matching or exceeding their discrimination performance. These results remained robust after excluding lower-weighted models and, notably, even after removing the highest-weighted and best-calibrated model. Compared with individual models, similarly well-calibrated models (i.e., with similar Brier Scores) achieved AUROC values of at most 0.683, indicating inferior discrimination. In contrast, the most discriminative model (AUROC = 0.716) exhibited poor calibration (Brier Score 0.148). While our EBMA-based framework achieved only a marginal improvement in AUROC over this model (+1.7%), it substantially improved calibration, yielding more reliable and clinically valuable predictions. Conclusions Well-calibrated predictions are more valuable for clinical decision-making than high discrimination alone. Our EBMA-based framework reliably integrates multiple models, delivering superior calibration while maintaining a discrimination comparable to the best-performing individual model. Our framework shifts away from the “winner-takes-all” paradigm of standalone models toward a more robust, ensemble-based strategy. Future work should focus on external validation and on boosting model diversity to further improve discriminative performance.

Introduction: In critical environments, patient discharge decisions present significant challenges, particularly regarding the prevention of readmissions. However, traditional AI systems focus on correlations rather than causation, resulting in issues related to explainability and generalization. This study conducts a comparative evaluation between standard predictive and causal-fused models to assess their ability to mitigate clinical confounding on predicting readmissions. Methodology: Utilizing the MIMIC-IV dataset, we predicted 30-day ICU readmissions through a comparative analysis of associative models (XGBoost, LSTM) and their causal-fused equivalents. The Fast Causal Inference (FCI) algorithm mapped latent confounding via a Partial Ancestral Graph (PAG), while counterfactuals were computed using Causal Forests to estimate the Average Treatment Effect (ATE), which was integrated into the predictive models. Performance was evaluated via AUROC, AUPRC, calibration metrics, precision, recall, and F1-score while feature extraction was used to monitor feature realignment. Results: The causal-fused LSTM model maintained a stable AUROC (0.7342 to 0.7357), while the XGBoost AUROC improved (0.6517 to 0.6909). Feature importance extraction revealed a structural realignment; whereas standard models relied heavily on non-actionable frailty proxies such as polypharmacy, causal integration elevated the individualized causal effect of length of stay as a primary predictive driver. The estimated ATE for length of stay was calculated at −0.038. Conclusions: The comparison showed that transitioning to causal-fused AI mathematically resolves a clinical Simpson’s Paradox, while also realigning the features based on causal mechanisms without sacrificing predictability. By making this shift, we can mitigate reliance on administrative noise and promote de-confounded interventions rather than passive correlations.