arXiv subjects

Pietro Belloni

Publications and source records attributed to Pietro Belloni.

3 recordsLinked to original sources

arXiv2025

Advances in Ontology--Based Mining of Adverse Drug Reactions

Post--marketing pharmacovigilance is essential for identifying adverse drug reactions (ADRs) that elude detection during pre--marketing clinical trials. This study explores a novel approach that integrates an adverse event (AE) ontology into a zero--inflated negative binomial model to improve ADR detection. By accounting for the biological similarities among correlated AEs and addressing the excess of zero counts, this method more effectively disentangles AE associations. Statistical significance is evaluated using a permutation--based maximum statistic that preserves AE correlations within individual reports. Simulations and an application to real data from the Veneto drug safety database demonstrate that the ontology--based model consistently outperforms classical models such as the Gamma--Poisson Shrinker (GPS). For post--selection inference, we furthermore explore a data thinning technique for convolution--closed families, enabling the creation of independent training and validation datasets while retaining all drug--AE pairs. This approach is compared with conventional random train/test splitting, which may leave some drugs or AEs absent from one subset, and stratified splitting, which requires expanding aggregated counts into individual instances. The data--thinning technique and stratified splitting yield very similar results, with stratified splitting showing a slight benefit, and both clearly outperform random splitting in ensuring reliable and consistent model evaluation.

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arXiv2025

Measuring frailty in the elderly: an indicator based on a super-classifier

Identifying frail older adults in an ageing population is essential for improving healthcare services. This study proposes a composite indicator to assess individual frailty levels using administrative healthcare data. Given the complex and multidimensional nature of frailty, a multi-outcome approach is adopted. Following an extensive literature review, a set of adverse health events is selected as proxies for frailty. These events were modelled using logistic classifiers, with frailty determinants (associated to adverse health events, selected using a gradient tree boosting) serving as covariates. The sensitivity and specificity of each classifier is used to compose their combined likelihood. From this, we derive an indicator capable of quantifying frailty across the population. The indicator shows robust performance across multiple outcomes and over time. Its primary innovation lies in allowing the use of diverse and outcome-specific sets of frailty determinants without any structural constraint. Overall, we offer an effective tool for quantifying frailty among older adults, potentially supporting health authorities in the prevention of frailty-related adverse events.

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arXiv2024

Bayesian Mapping of Mortality Clusters

Disease mapping analyses the distribution of several disease outcomes within a territory. Primary goals include identifying areas with unexpected changes in mortality rates, studying the relation among multiple diseases, and dividing the analysed territory into clusters based on the observed levels of disease incidence or mortality. In this work, we focus on detecting spatial mortality clusters, that occur when neighbouring areas within a territory exhibit similar mortality levels due to one or more diseases. When multiple causes of death are examined together, it is relevant to identify not only the spatial boundaries of the clusters but also the diseases that lead to their formation. However, existing methods in literature struggle to address this dual problem effectively and simultaneously. To overcome these limitations, we introduce Perla, a multivariate Bayesian model that clusters areas in a territory according to the observed mortality rates of multiple causes of death, also exploiting the information of external covariates. Our model incorporates the spatial structure of data directly into the clustering probabilities by leveraging the stick-breaking formulation of the multinomial distribution. Additionally, it exploits suitable global-local shrinkage priors to ensure that the detection of clusters depends on diseases showing concrete increases or decreases in mortality levels, while excluding uninformative diseases. We propose an MCMC algorithm for posterior inference that consists of closed-form Gibbs sampling moves for nearly every model parameter. To demonstrate the flexibility and effectiveness of our methodology, we validate Perla with a series of simulation experiments and two extensive case studies.

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