A semiparametric bivariate probit model for joint modeling of outcomes in STEMI patients

In this work we analyse the relationship among in-hospital mortality and a treatment effectiveness outcome in patients affected by ST-Elevation myocardial infarction. The main idea is to carry out a joint modeling of the two outcomes applying a Semiparametric Bivariate Probit Model to data arising from a clinical registry called STEMI Archive. A realistic quantification of the relationship between outcomes can be problematic for several reasons. First, latent factors associated with hospitals organization can affect the treatment efficacy and/or interact with patient’s condition at admission time. Moreover, they can also directly influence the mortality outcome. Such factors can be hardly measurable. Thus, the use of classical estimation methods will clearly result in inconsistent or biased parameter estimates. Secondly, covariate-outcomes relationships can exhibit nonlinear patterns. Provided that proper statistical methods for model fitting in such framework are available, it is possible to employ a simultaneous estimation approach to account for unobservable confounders. Such a framework can also provide flexible covariate structures and model the whole conditional distribution of the response

Network analysis of comorbidity patterns in Heart Failure patients using administrative data.

Background: Congestive Heart Failure (HF) is a widespread chronic disease characterized by a very high incidence in elder people. The high mortality and readmission rate of HF strongly depends on the complicated morbidity scenario often characterising it. The aim of this paper is to show the potential and the usefulness of Network models when applied to the analysis of comorbidity patterns in HF, as a new methodological tool to be considered within the epidemiological investigation of this complex disease. Methods: Data were retrieved from the healthcare administrative datawarehouse of Lombardy, the most populated regional district in Italy. Network analysis techniques and community detection algorithms are applied to comorbidities registered in hospital discharge papers of HF patients, in 7 cohorts between 2006 and 2012. Results: The relevance network indexes applied to the 7 cohorts identified, hypertension, arrythmia, renal and pulmonary diseases as the most relevant nodes related to death, in terms of prevalence and closeness/strength of the relationship. Moreover, some relevant clusters of nodes have been identified in all the cohorts, i.e. those related to cancer, lung diseases liver diseases and heart/circulation related problems. It seems that such patterns do not evolve along time (i.e., nor indexes of relevance computed on the nodes of the networks neither communities change significantly from one year/cohort to another), featuring HF comorbidity burden as stable over the years. Conclusions: Network analysis can be a useful tool in epidemiologic framework when relational data are the objective of the investigation, since it allows to visualize and make inference on patterns of association among nodes (here HF comorbidities) by means of both qualitative indexes and clustering techniques

Polimi at CLinkaRT: a Conditional Random Field vs a BERT-based approach

In the context of the EVALITA 2023 challenge, we present the models we have developed for the CLinkaRT task, which aims to identify medical examinations and their corresponding results in Italian clinical documents. We propose two distinct approaches: one utilising a Conditional Random Field (CRF), a probabilistic graphical model traditionally used for Named Entity Recognition, and the other based on BERT, the transformer-based model that is currently state-of-the-art for many Natural Language Processing tasks. Both models incorporate external knowledge from publicly available medical resources and are enhanced with heuristic rules to establish associations between exams and results. Our comparative analysis elects the CRF-based model as the winner, securing the third position in the competition ranking, but the BERT-based model demonstrated competitive performance

Mixed-effects high-dimensional multivariate regression via group-lasso regularization

Linear mixed modeling is a well-established technique widely employed when observations possess a grouping structure. Nonetheless, this standard methodology is no longer applicable when the learning framework encompasses a multivariate response and high-dimensional predictors. To overcome these issues, in the present paper a penalized estimation procedure for multivariate linear mixed-effects models (MLMM) is introduced. In details, we propose to regularize the likelihood via a group-lasso penalty, forcing only a subset of the estimated parameters to be preserved across all components of the multivariate response. The methodology is employed to develop novel surrogate biomarkers for cardiovascular risk factors, such as lipids and blood pressure, from whole-genome DNA methylation data in a multi-center study. The described methodology performs better than current stateof- art alternatives in predicting a multivariate continuous outcome

Daily deal shoppers: What drives social couponing?

This paper contributes to the service marketing literature with a focus on deal-of-the-day (DoD) website shopping. The work explores drivers of adoption of DoD shopping among young consumers. We show that value conscious consumers are less oriented towards DoD while deal-prone consumers are more likely to purchase DoD. In contrast to previous research, which found that price savings are the main reason for coupon use, our study finds that Enjoyment plays a major role in young consumers\u2019 DoD shopping behaviour. DoD platforms could leverage Enjoyment to create a compelling value proposition for both consumer and merchant attraction and retention

Clustering Italian medical texts: a case study on referrals

In the medical domain, there is a large amount of valuable information that is stored in textual format. These unstructured data have long been ignored, due to the difficulties of introducing them in statistical models, but in the last years, the field of Natural Language Processing (NLP) has seen relevant improvements, with models capable of achieving relevant results in various tasks, including information extraction, classification and clustering. NLP models are typically language-specific and often domain-specific, but most of the work to date has been focused on the English language, especially in the medical domain. In this work, we propose a pipeline for clustering Italian medical texts, with a case study on clinical questions reported in referral

Low delta-V near-Earth asteroids: A survey of suitable targets for space missions

In the last decades Near-Earth Objects (NEOs) have become very important targets to study, since they can give us clues to the formation, evolution and composition of the Solar System. In addition, they may represent either a threat to humankind, or a repository of extraterrestrial resources for suitable space-borne missions. Within this framework, the choice of next-generation mission targets and the characterisation of a potential threat to our planet deserve special attention. To date, only a small part of the 11,000 discovered NEOs have been physically characterised. From ground and space-based observations one can determine some basic physical properties of these objects using visible and infrared spectroscopy. We present data for 13 objects observed with different telescopes around the world (NASA-IRTF, ESO-NTT, TNG) in the 0.4 - 2.5 um spectral range, within the NEOSURFACE survey (http://www.oa-roma.inaf.it/planet/NEOSurface.html). Objects are chosen from among the more accessible for a rendez-vous mission. All of them are characterised by a delta-V (the change in velocity needed for transferring a spacecraft from low-Earth orbit to rendez-vous with NEOs) lower than 10.5 km/s, well below the Solar System escape velocity (12.3 km/s). We taxonomically classify 9 of these objects for the first time. 11 objects belong to the S-complex taxonomy; the other 2 belong to the C-complex. We constrain the surface composition of these objects by comparing their spectra with meteorites from the RELAB database. We also compute olivine and pyroxene mineralogy for asteroids with a clear evidence of pyroxene bands. Mineralogy confirms the similarity with the already found H, L or LL ordinary chondrite analogues.Comment: 9 pages, 7 figures, to be published in A&A Minor changes by language edito

Critical values improvement for the standard normal homogeneity test by combining Monte Carlo and regression approaches

The distribution of the test statistics of homogeneity tests is often unknown, requiring the estimation of the critical values through Monte Carlo simulations. The computation of the critical values at low \u3b1, especially when the distribution of the statistics changes with the series length (sample cardinality), requires a considerable number of simulations to achieve a reasonable precision of the estimates (i.e., 10^6 simulations or more for each series length). If, in addition, the test requires a noteworthy computational effort, the estimation of the critical values may need unacceptably long runtimes. To overcome the problem, the paper proposes a regression-based refinement of an initial Monte Carlo estimate of the critical values, also allowing an approximation of the achieved improvement. Moreover, the paper presents an application of the method to two tests: SNHT (standard normal homogeneity test, widely used in climatology), and SNH2T (a version of SNHT showing a squared numerical complexity). For both, the paper reports the critical values for \u3b1 ranging between 0.1 and 0.0001 (useful for the p-value estimation), and the series length ranging from 10 (widely adopted size in climatological change-point detection literature) to 70,000 elements (nearly the length of a daily data time series 200 years long), estimated with coefficients of variation within 0.22%. For SNHT, a comparison of our results with approximated, theoretically derived, critical values is also performed; we suggest adopting those values for the series exceeding 70,000 elements

Setting rents in residential real estate: a methodological proposal using multiple criteria decision analysis

The real estate sector has been negatively affected by the recent economic recession, which has forced structural changes that impact property value and price. Recent pressures have also motivated reduced liquidity and access to credit, causing a drop in property sales and, thus, boosting the rental housing market. It is worth noting, however, that the rental housing segment is not with-out difficulties and complexity, namely in terms of legislation and rental value revaluation. In light of this reasoning, this study aims to develop a multiple criteria decision support system for calculation of residential rents. By integrating cognitive maps and the measuring attractiveness by a categorical based evaluation technique (MACBETH), we also aim to introduce simplicity and transparency in the decision making framework. The practical implications, advantages and shortfalls of our proposal are also analyzed