"Analisi dell'aderenza alle linee guida nel trattamento della neutropenia e anemia del paziente onco-ematologico"

Obiettivo tesi Lo studio si propone di analizzare l’utilizzo dei fattori di crescita e delle eritropoietine nel trattamento della neutropenia e dell’anemia nel paziente onco-ematologico, verificandone l’aderenza o meno alle linee guida AIOM 2010. Ciò acquista un particolare significato anche in considerazione dell’attuale congiuntura economica che interessa il Servizio Sanitario Nazionale che vede da una parte una minore disponibilità di risorse e dall’altra una crescente richiesta di terapie ad alto costo; questo è particolarmente rilevante in ambito oncologico. In considerazione del fatto che i farmaci di cui trattiamo incidono in modo significativo sulla spesa farmaceutica risulta importante adottare tutti gli strumenti di conoscenza affinchè il loro uso sia appropriato e possa rispettare i profili di costo/efficacia. Partendo dall’idea della figura del “Farmacista di Dipartimento”, che propone l’inserimento di questa figura professionale in reparto quale ausilio nella valutazione delle scelte terapeutiche, per favorire una corretta politica di Governo clinico che tenga conto sia dell’appropriatezza che del miglior utilizzo delle risorse, si è cercato di fissare i dati una prima esperienza di osservazione diretta sulle scelte di terapia nel paziente neutropenico/anemico post trattamento chemioterapico. Questo potrebbe essere un punto di partenza per la definizione di linee guida di indirizzo e per la pianificazione di possibili strumenti di monitoraggio per la sorveglianza dei trattamenti in uso, per ottenere una condivisione con gli specialisti ed elaborare delle raccomandazioni e degli indicatori per evidenziare l’aderenza o meno alle stesse

Multiple-instance Learning as a Framework to Explain with Shapley Coefficients

Explainability and interpretability have become questions of fundamental importance for a safe and responsible deployment of modern machine learning models in high-stakes scenarios. Many examples exist of accidental behavior of autonomous systems that systematically under perform on minorities, or emulate hateful human behavior. Notwithstanding the recent advances in fair and interpretable machine learning, several theoretical issues remain open on the validity of popular explanation methods. In this thesis, we study multiple-instance learning as a framework to explaining model predictions with Shapley coefficients. In particular, we focus on local explanations, i.e. we seek to find the most important features in an input towards a model’s prediction. We show that a principle approach to explainability can produce fast and exact explanation methods that provide precise mathematical guarantees on their speed and accuracy. We apply our new explanation method to a medical imaging task of clinical importance– intracranial hemorrhage detection–where the use of autonomous systems can support radiologists in their daily work, for example, by prioritizing the most severe cases or provide a second opinion for subtle ones. We find that an explainability-driven approach can significantly reduce the number of labels needed to train a model, and therefore make collecting new datasets cheaper

From Shapley back to Pearson: Hypothesis Testing via the Shapley Value

The complex nature of artificial neural networks raises concerns on their reliability, trustworthiness, and fairness in real-world scenarios. The Shapley value -- a solution concept from game theory -- is one of the most popular explanation methods for machine learning models. More traditionally, from the perspective of statistical learning, feature importance is defined in terms of conditional independence. So far, these two approaches to interpretability and feature importance have been considered separate and distinct. In this work, we show that Shapley-based explanation methods and conditional independence testing are closely related. We introduce the $\textbf{SHAP}$ley $\textbf{L}$ocal $\textbf{I}$ndependence $\textbf{T}$est ($\textbf{SHAPLIT}$), a novel testing procedure inspired by the Conditional Randomization Test (CRT) for a specific notion of local (i.e., on a sample) conditional independence. With it, we prove that for binary classification problems, each marginal contribution in the Shapley value is an upper bound to the $p$-value of this conditional independence test. Furthermore, we show that the Shapley value itself provides an upper bound to the $p$-value of a global SHAPLIT null hypothesis. As a result, we grant the Shapley value with a precise statistical sense of importance with false positive rate control

How to Trust Your Diffusion Model: A Convex Optimization Approach to Conformal Risk Control

Score-based generative modeling, informally referred to as diffusion models, continue to grow in popularity across several important domains and tasks. While they provide high-quality and diverse samples from empirical distributions, important questions remain on the reliability and trustworthiness of these sampling procedures for their responsible use in critical scenarios. Conformal prediction is a modern tool to construct finite-sample, distribution-free uncertainty guarantees for any black-box predictor. In this work, we focus on image-to-image regression tasks and we present a generalization of the Risk-Controlling Prediction Sets (RCPS) procedure, that we term $K$-RCPS, which allows to $(i)$ provide entrywise calibrated intervals for future samples of any diffusion model, and $(ii)$ control a certain notion of risk with respect to a ground truth image with minimal mean interval length. Differently from existing conformal risk control procedures, ours relies on a novel convex optimization approach that allows for multidimensional risk control while provably minimizing the mean interval length. We illustrate our approach on two real-world image denoising problems: on natural images of faces as well as on computed tomography (CT) scans of the abdomen, demonstrating state of the art performance

Social modulation of peripersonal space boundaries

The space around the body, i.e., peripersonal space (PPS), is conceived as a multisensory-motor interface between body and environment. PPS is represented by frontoparietal neurons integrating tactile, visual, and auditory stimuli occurring near the body [1-7]. PPS is plastic, because it extends by using a tool to reach far objects [8-10]. Although interactions with others occur within PPS, little is known about how social environment modulates it. Here, we show that presence and interaction with others shape PPS representation. Participants performed a tactile detection task on their face while concurrent task-irrelevant sounds approached toward or receded from their face. Because a sound affects touch when occurring within PPS [6, 10-12], we calculated the critical distance where sounds speeded up tactile reaction time as a proxy of PPS boundaries. Experiment 1 shows that PPS boundaries shrink when subjects face another individual, as compared to a mannequin, placed in far space. Experiment 2 and 3 show that, after playing an economic game with another person, PPS boundaries between self and other merge, but only if the other behaved cooperatively. These results reveal that PPS representation is sensitive to social modulation, showing a link between low-level sensorimotor processing and high-level social cognition. © 2013 Elsevier Ltd

Disentangling predictive processing in the brain: A meta-analytic study in favour of a predictive network

According to the predictive coding (PC) theory, the brain is constantly engaged in predicting its upcoming states and refning these predictions through error signals. Despite extensive research investigating the neural bases of this theory, to date no previous study has systematically attempted to defne the neural mechanisms of predictive coding across studies and sensory channels, focussing on functional connectivity. In this study, we employ a coordinate-based meta-analytical approach to address this issue. We frst use the Activation Likelihood Estimation (ALE) algorithm to detect spatial convergence across studies, related to prediction error and encoding. Overall, our ALE results suggest the ultimate role of the left inferior frontal gyrus and left insula in both processes. Moreover, we employ a meta-analytic connectivity method (Seed-Voxel Correlations Consensus). This technique reveals a large, bilateral predictive network, which resembles large-scale networks involved in taskdriven attention and execution. In sum, we fnd that: (i) predictive processing seems to occur more in certain brain regions than others, when considering diferent sensory modalities at a time; (ii) there is no evidence, at the network level, for a distinction between error and prediction processing

The Mindedness of Maternal Touch: An Investigation of Maternal Mind-Mindedness and Mother-Infant Touch Interactions

Increasing evidence shows that maternal touch may promote emotion regulation in infants, however less is known about how parental higher-order social cognition abilities are translated into tactile, affect-regulatory behaviours towards their infants. During 10 min book-reading, mother-infant sessions when infants were 12 months old (N = 45), we investigated maternal mind-mindedness (MM), the social cognitive ability to understand an infant’s mental state, by coding the contingency of maternal verbal statements towards the infants’ needs and desires. We also rated spontaneous tactile interactions in terms of their emotional contingency. We found that frequent non-attuned mind-related comments were associated with touch behaviours that were not contingent with the infant’s emotions; ultimately discouraging affective tactile responses from the infant. However, comments that were more appropriate to infant’s mental states did not necessarily predict more emotionally-contingent tactile behaviours. These findings suggest that when parental high-order social cognitive abilities are compromised, they are also likely to translate into inappropriate, tactile attempts to regulate infant’s emotions

WNT signalling in prostate cancer

Genome sequencing and gene expression analyses of prostate tumours have highlighted the potential importance of genetic and epigenetic changes observed in WNT signalling pathway components in prostate tumours-particularly in the development of castration-resistant prostate cancer. WNT signalling is also important in the prostate tumour microenvironment, in which WNT proteins secreted by the tumour stroma promote resistance to therapy, and in prostate cancer stem or progenitor cells, in which WNT-β-catenin signals promote self-renewal or expansion. Preclinical studies have demonstrated the potential of inhibitors that target WNT receptor complexes at the cell membrane or that block the interaction of β-catenin with lymphoid enhancer-binding factor 1 and the androgen receptor, in preventing prostate cancer progression. Some WNT signalling inhibitors are in phase I trials, but they have yet to be tested in patients with prostate cancer

Listening to a conversation with aggressive content expands the interpersonal space

The distance individuals maintain between themselves and others can be defined as ‘interpersonal space’. This distance can be modulated both by situational factors and individual characteristics. Here we investigated the influence that the interpretation of other people interaction, in which one is not directly involved, may have on a person’s interpersonal space. In the current study we measured, for the first time, whether the size of interpersonal space changes after listening to other people conversations with neutral or aggressive content. The results showed that the interpersonal space expands after listening to a conversation with aggressive content relative to a conversation with a neutral content. This finding suggests that participants tend to distance themselves from an aggressive confrontation even if they are not involved in it. These results are in line with the view of the interpersonal space as a safety zone surrounding one’s body