Quaderni MOX
Pubblicazioni
del Laboratorio di Modellistica e Calcolo Scientifico MOX. I lavori riguardano prevalentemente il campo dell'analisi numerica, della statistica e della modellistica matematica applicata a problemi di interesse ingegneristico. Il sito del Laboratorio MOX è raggiungibile
all'indirizzo mox.polimi.it
Trovati 1253 prodotti
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90/2023 - 10/11/2023
Gregorio, C.; Baj, G.; Barbati, G.; Ieva, F.
Dynamic treatment effect phenotyping through functional survival analysis | Abstract | | In recent years, research interest in personalised treatments has been growing. However, treatment effect heterogeneity and possibly time-varying treatment effects are still often overlooked in clinical studies. Statistical tools are needed for the identification of treatment response patterns, taking into account that treatment response is not constant over time. We aim to provide an innovative method to obtain dynamic treatment effect phenotypes on a time-to-event outcome, conditioned
on a set of relevant effect modifiers. The proposed method does not require the assumption of proportional hazards for the treatment effect, which is rarely realistic. We propose a spline-based survival neural network, inspired by the Royston-Parmar survival model, to estimate time-varying conditional treatment effects. We then exploit the functional nature of the resulting estimates to apply a functional clustering of the treatment effect curves in order to identify different patterns of treatment effects. The application that motivated this work is the discontinuation of treatment with Mineralocorticoid receptor Antagonists (MRAs) in patients with heart failure, where there is no clear evidence as to which patients it is the safest choice to discontinue treatment and, conversely, when it leads to a higher risk of adverse events. The data come from an electronic health record database. A simulation study was performed to assess the performance of the spline-based neural network and the stability of the treatment response phenotyping procedure. In light of the results, the suggested approach has the potential to support personalized medical choices by assessing treatment responses in various medical contexts over a period of time. |
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89/2023 - 08/11/2023
Savaré, L.; Ieva, F.; Corrao, G.; Lora, A.
Capturing the variety of clinical pathways in patients with schizophrenic disorders through state sequences analysis | Abstract | | Background Care pathways are increasingly being used to enhance the quality of care and optimize the use
of resources for health care. Nevertheless, recommendations regarding the sequence of care are mostly based
on consensus-based decisions as there is a lack of evidence on effective treatment sequences. In a real-world setting,
classical statistical tools were insufficient to consider a phenomenon with such high variability adequately and have
to be integrated with novel data mining techniques suitable for identifying patterns in complex data structures.
Data-driven techniques can potentially support empirically identifying effective care sequences by extracting them
from data collected routinely. The purpose of this study is to perform a state sequence analysis (SSA) to identify different
patterns of treatment and to asses whether sequence analysis may be a useful tool for profiling patients according
to the treatment pattern.
Methods The clinical application that motivated the study of this method concerns the mental health field. In fact,
the care pathways of patients affected by severe mental disorders often do not correspond to the standards required
by the guidelines in this field. In particular, we analyzed patients with schizophrenic disorders (i.e., schizophrenia,
schizotypal or delusional disorders) using administrative data from 2015 to 2018 from Lombardy Region. This methodology
considers the patient’s therapeutic path as a conceptual unit, composed of a succession of different states,
and we show how SSA can be used to describe longitudinal patient status.
Results We define the states to be the weekly coverage of different treatments (psychiatric visits, psychosocial interventions,
and anti-psychotic drugs), and we use the longest common subsequences (dis)similarity measure to compare
and cluster the sequences. We obtained three different clusters with very different patterns of treatments.
Conclusions This kind of information, such as common patterns of care that allowed us to risk profile patients, can
provide health policymakers an opportunity to plan optimum and individualized patient care by allocating appropriate
resources, analyzing trends in the health status of a population, and finding the risk factors that can be leveraged
to prevent the decline of mental health status at the population level.
Keywords State sequence analysis, Care pathways, Schizophrenic disorder |
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88/2023 - 08/11/2023
Masci, C.; Spreafico, M.; Ieva, F.
Joint modelling of recurrent and terminal events with discretely-distributed non-parametric frailty: application on re-hospitalizations and death in heart failure patients | Abstract | | In the context of clinical and biomedical studies, joint frailty models have been developed to study the
joint temporal evolution of recurrent and terminal events, capturing both the heterogeneous susceptibility
to experiencing a new episode and the dependence between the two processes. While discretely-distributed
frailty is usually more exploitable by clinicians and healthcare providers, existing literature on joint frailty
models predominantly assumes continuous distributions for the random effects. In this article, we present a
novel joint frailty model that assumes bivariate discretely-distributed non-parametric frailties, with an unknown
finite number of mass points. This approach facilitates the identification of latent structures among subjects,
grouping them into sub-populations defined by a shared frailty value. We propose an estimation routine via
Expectation-Maximization algorithm, which not only estimates the number of subgroups but also serves as an
unsupervised classification tool. This work is motivated by a study of patients with Heart Failure (HF) receiving
ACE inhibitors treatment in the Lombardia region of Italy. Recurrent events of interest are hospitalizations
due to HF and terminal event is death for any cause. |
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86/2023 - 04/11/2023
Ferraccioli, F.; Sangalli, L.M.; Finos, L.
Nonparametric tests for semiparametric regression models | Abstract | | Semiparametric regression models have received considerable attention over the last decades, because of their flexibility and their good finite sample performances. Here we propose an innovative nonparametric test for the linear part of the models, based on random sign-flipping of an appropriate transformation of the residuals, that exploits a spectral decomposition of the residualizing matrix associated with the nonparametric part of the model. The test can be applied to a vast class of extensively used semiparametric regression models with roughness penalties, with nonparametric components defined over one-dimensional, as well as over multi-dimensional domains, including for instance models based on univariate or multivariate splines. We prove the good asymptotic properties of the proposed test. Moreover, by means of extensive simulation studies, we show the superiority of the proposed test with respect to current parametric alternatives, demonstrating its excellent control of the Type I error, accompanied by a good power, even in challenging data scenarios, where instead current parametric alternatives fail. |
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85/2023 - 04/11/2023
Arnone, E.; De Falco, C.; Formaggia, L.; Meretti, G.; Sangalli, L.M.
Computationally efficient techniques for Spatial Regression with Differential Regularization | Abstract | | We investigate some computational aspects of an innovative class of PDE-regularized statistical models: Spatial Regression with Partial Differential Equation regularization (SR-PDE). These physics-informed regression methods can account for the physics of the underlying phenomena and handle data observed over spatial domains with nontrivial shapes, such as domains with concavities and holes or curved domains. The computational bottleneck in SR-PDE estimation is the solution of a computationally demanding linear system involving a low-rank but dense block. We address this aspect by innovatively using Sherman–Morrison–Woodbury identity. We also investigate the efficient selection of the smoothing parameter in SR-PDE estimates. Specifically, we propose ad hoc optimization methods to perform Generalized Cross-Validation, coupling suitable reformulation of key matrices, e.g., those based on Sherman–Morrison–Woodbury formula, with stochastic trace estimation, to approximate the equivalent degrees of freedom of the problem. These solutions permit high computational efficiency also in the context of massive data. |
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83/2023 - 02/11/2023
Cavinato, L.; Massi, M.C.; Sollini, M.; Kirienko , M.; Ieva, F.
Dual adversarial deconfounding autoencoder for joint batch-effects removal from multi-center and multi-scanner radiomics data | Abstract | | Medical imaging represents the primary tool for investigating and monitoring several diseases, including cancer. The advances in quantitative image analysis have developed towards the extraction of biomarkers able to support clinical decisions. To produce robust results, multi-center studies are often set up. However, the imaging information must be denoised from confounding factors—known as batch-effect—like scanner-specific and center-specific influences. Moreover, in non-solid cancers, like lymphomas, effective biomarkers require an imaging-based representation of the disease that accounts for its multi-site spreading over the patient’s body. In this work, we address the dual-factor deconfusion problem and we propose a deconfusion algorithm to harmonize the imaging information of patients affected by Hodgkin Lymphoma in a multi-center setting. We show that the proposed model successfully denoises data from domain-specific variability (p-value < 0.001) while it coherently preserves the spatial relationship between imaging descriptions of peer lesions (p-value = 0), which is a strong prognostic biomarker for tumor heterogeneity assessment. This harmonization step allows to significantly improve the performance in prognostic models with respect to state-of-the-art methods, enabling building exhaustive patient representations and delivering more accurate analyses (p-values < 0.001 in training, p-values < 0.05 in testing). This work lays the groundwork for performing large-scale and reproducible analyses on multi-center data that are urgently needed to convey the translation of imaging-based biomarkers into the clinical practice as effective prognostic tools. The code is available on GitHub at this https://github.com/LaraCavinato/Dual-ADAE. |
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82/2023 - 31/10/2023
Pozzi, G.; Ciarletta, P.
Geometric control by active mechanics of epithelial gap closure | Abstract | | Epithelial wound healing is one of the most important biological processes occurring during the lifetime of an organism. It is a self-repair mechanism closing wounds or gaps within tissues to restore
their functional integrity. In this work we derive a new diffuse interface approach for modelling the gap closure by means of a variational principle in the framework of non-equilibrium thermodynamics.
We investigate the interplay between the crawling with lamellipodia protrusions and the supracellular tension exerted by the actomyosin cable on the closure dynamics. These active features are modeled
as Korteweg forces into a generalised chemical potential. From an asymptotic analysis, we drive a pressure jump across the gap edge in the sharp interface limit. Moreover, the chemical potential diffuses
as a Mullins-Sekerka system, and its interfacial value is given by a Gibbs-Thompson relation for its local potential driven by the curvature-dependent purse-string tension. The Finite Element simulations show an excellent quantitative agreement between the closing dynamics and the morphology of the edge with respect to existing biological experiments. The resulting force patterns are also in good qualitative agreement with existing traction force microscopy measurements. Our results shed light on the geometrical control of the gap closure dynamics resulting from the active forces that are
chemically activated around the gap edge. |
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81/2023 - 31/10/2023
Buchwald, S.; Ciaramella, G; Salomon, J.; Sugny, D.
A SPIRED code for the reconstruction of spin distribution | Abstract | | In Nuclear Magnetic Resonance (NMR), it is of crucial importance to have
an accurate knowledge of the sample probability distribution corresponding to inhomogeneities of the magnetic fields. An accurate identification of the sample distribution requires a set of experimental data that is sufficiently rich to extract all fundamental information. These data depend strongly on the control fields (and their number) used experimentally. In this work, we present and analyze a greedy reconstruction algorithm, and provide the corresponding SPIRED code, for the computation of a set of control functions allowing the generation of data that are appropriate for the accurate reconstruction of a sample distribution. In particular, the focus is on NMR and the Bloch system with inhomogeneities in the magnetic fields in all spatial directions. Numerical examples illustrate this general study. |
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