Development of a model-based algorithm for the assessment of the Obsessive-Compulsive Disorder

Questa tesi presentata AAS-PD (Sistema di Assessment Adattivo per i disturbi psicologici), un sistema computerizzato di assessment psicologico adattivo per il Disturbo Ossessivo-Compulsivo (DOC). Tale sistema software è basato su una rappresentazione forma del DOC, chiamata Formal Psychological Assessment (FPA), e rappresenta una novità nel campo della psicologia clinica. AAS-PD prende una struttura di conoscenza (struttura clinica), ed esegue l'assessment facendo inferenze probabilistiche su tale struttura, usando come criterio di stop la misura dell'entropia della struttura. I risultati mostrano che AAS-PD assegna correttamente pattern di risposta a stati clinici, evidenziando inoltre alcuni miglioramenti del modello formale da fare. Sviluppi futuri comportano lo sviluppo di un vero e proprio software capace di supportare il clinico nell'assessment dei principali disturbi psicologici / This thesis presents AAS-PD (Adaptive Assessment System for psychological disorders), a computerized adaptive psychological assessment system for the Obsessive-Compulsive Disorder (OCD). This software system is based on a formal representation of the OCD called Formal Psychological Assessment (FPA), and represents an innovation in the field of clinical psychology. AAS-PD requires a knowledge structure (clinical structure), and performs the assessment by making probabilistic inferences of such a structure, using as stop criterion the measure of entropy of the structure. The results show that PD-AAS properly assigns response patterns to clinical states, and note some improvements of the formal model to do. Future developments will involve the development of a real software that supports the clinician in the assessment of the major psychological disordersope

Persuasive Explanation of Reasoning Inferences on Dietary Data

Explainable AI aims at building intelligent systems that are able to provide a clear, and human understandable, justification of their decisions. This holds for both rule-based and data-driven methods. In management of chronic diseases, the users of such systems are patients that follow strict dietary rules to manage such diseases. After receiving the input of the intake food, the system performs reasoning to understand whether the users follow an unhealthy behaviour. Successively, the system has to communicate the results in a clear and effective way, that is, the output message has to persuade users to follow the right dietary rules. In this paper, we address the main challenges to build such systems: i) the natural language generation of messages that explain the reasoner inconsistency; ii) the effectiveness of such messages at persuading the users. Results prove that the persuasive explanations are able to reduce the unhealthy users’ behaviours

Outcome-Oriented Prescriptive Process Monitoring Based on Temporal Logic Patterns

Prescriptive Process Monitoring systems recommend, during the execution of a business process, interventions that, if followed, prevent a negative outcome of the process. Such interventions have to be reliable, that is, they have to guarantee the achievement of the desired outcome or performance, and they have to be flexible, that is, they have to avoid overturning the normal process execution or forcing the execution of a given activity. Most of the existing Prescriptive Process Monitoring solutions, however, while performing well in terms of recommendation reliability, provide the users with very specific (sequences of) activities that have to be executed without caring about the feasibility of these recommendations. In order to face this issue, we propose a new Outcome-Oriented Prescriptive Process Monitoring system recommending temporal relations between activities that have to be guaranteed during the process execution in order to achieve a desired outcome. This softens the mandatory execution of an activity at a given point in time, thus leaving more freedom to the user in deciding the interventions to put in place. Our approach defines these temporal relations with Linear Temporal Logic over finite traces patterns that are used as features to describe the historical process data recorded in an event log by the information systems supporting the execution of the process. Such encoded log is used to train a Machine Learning classifier to learn a mapping between the temporal patterns and the outcome of a process execution. The classifier is then queried at runtime to return as recommendations the most salient temporal patterns to be satisfied to maximize the likelihood of a certain outcome for an input ongoing process execution. The proposed system is assessed using a pool of 22 real-life event logs that have already been used as a benchmark in the Process Mining community.Comment: 38 pages, 6 figures, 8 table

Early mobilisation in critically ill COVID-19 patients: a subanalysis of the ESICM-initiated UNITE-COVID observational study

Background Early mobilisation (EM) is an intervention that may improve the outcome of critically ill patients. There is limited data on EM in COVID-19 patients and its use during the first pandemic wave. Methods This is a pre-planned subanalysis of the ESICM UNITE-COVID, an international multicenter observational study involving critically ill COVID-19 patients in the ICU between February 15th and May 15th, 2020. We analysed variables associated with the initiation of EM (within 72 h of ICU admission) and explored the impact of EM on mortality, ICU and hospital length of stay, as well as discharge location. Statistical analyses were done using (generalised) linear mixed-effect models and ANOVAs. Results Mobilisation data from 4190 patients from 280 ICUs in 45 countries were analysed. 1114 (26.6%) of these patients received mobilisation within 72 h after ICU admission; 3076 (73.4%) did not. In our analysis of factors associated with EM, mechanical ventilation at admission (OR 0.29; 95% CI 0.25, 0.35; p = 0.001), higher age (OR 0.99; 95% CI 0.98, 1.00; p ≤ 0.001), pre-existing asthma (OR 0.84; 95% CI 0.73, 0.98; p = 0.028), and pre-existing kidney disease (OR 0.84; 95% CI 0.71, 0.99; p = 0.036) were negatively associated with the initiation of EM. EM was associated with a higher chance of being discharged home (OR 1.31; 95% CI 1.08, 1.58; p = 0.007) but was not associated with length of stay in ICU (adj. difference 0.91 days; 95% CI − 0.47, 1.37, p = 0.34) and hospital (adj. difference 1.4 days; 95% CI − 0.62, 2.35, p = 0.24) or mortality (OR 0.88; 95% CI 0.7, 1.09, p = 0.24) when adjusted for covariates. Conclusions Our findings demonstrate that a quarter of COVID-19 patients received EM. There was no association found between EM in COVID-19 patients' ICU and hospital length of stay or mortality. However, EM in COVID-19 patients was associated with increased odds of being discharged home rather than to a care facility. Trial registration ClinicalTrials.gov: NCT04836065 (retrospectively registered April 8th 2021)

Development of a model-based algorithm for the assessment of the Obsessive-Compulsive Disorder

Questa tesi presentata AAS-PD (Sistema di Assessment Adattivo per i disturbi psicologici), un sistema computerizzato di assessment psicologico adattivo per il Disturbo Ossessivo-Compulsivo (DOC). Tale sistema software è basato su una rappresentazione forma del DOC, chiamata Formal Psychological Assessment (FPA), e rappresenta una novità nel campo della psicologia clinica. AAS-PD prende una struttura di conoscenza (struttura clinica), ed esegue l'assessment facendo inferenze probabilistiche su tale struttura, usando come criterio di stop la misura dell'entropia della struttura. I risultati mostrano che AAS-PD assegna correttamente pattern di risposta a stati clinici, evidenziando inoltre alcuni miglioramenti del modello formale da fare. Sviluppi futuri comportano lo sviluppo di un vero e proprio software capace di supportare il clinico nell'assessment dei principali disturbi psicologici / This thesis presents AAS-PD (Adaptive Assessment System for psychological disorders), a computerized adaptive psychological assessment system for the Obsessive-Compulsive Disorder (OCD). This software system is based on a formal representation of the OCD called Formal Psychological Assessment (FPA), and represents an innovation in the field of clinical psychology. AAS-PD requires a knowledge structure (clinical structure), and performs the assessment by making probabilistic inferences of such a structure, using as stop criterion the measure of entropy of the structure. The results show that PD-AAS properly assigns response patterns to clinical states, and note some improvements of the formal model to do. Future developments will involve the development of a real software that supports the clinician in the assessment of the major psychological disorder

Semantic Image Interpretation - Integration of Numerical Data and Logical Knowledge for Cognitive Vision

Semantic Image Interpretation (SII) is the process of generating a structured description of the content of an input image. This description is encoded as a labelled direct graph where nodes correspond to objects in the image and edges to semantic relations between objects. Such a detailed structure allows a more accurate searching and retrieval of images. In this thesis, we propose two well-founded methods for SII. Both methods exploit background knowledge, in the form of logical constraints of a knowledge base, about the domain of the images. The first method formalizes the SII as the extraction of a partial model of a knowledge base. Partial models are built with a clustering and reasoning algorithm that considers both low-level and semantic features of images. The second method uses the framework Logic Tensor Networks to build the labelled direct graph of an image. This framework is able to learn from data in presence of the logical constraints of the knowledge base. Therefore, the graph construction is performed by predicting the labels of the nodes and the relations according to the logical constraints and the features of the objects in the image. These methods improve the state-of-the-art by introducing two well-founded methodologies that integrate low-level and semantic features of images with logical knowledge. Indeed, other methods, do not deal with low-level features or use only statistical knowledge coming from training sets or corpora. Moreover, the second method overcomes the performance of the state-of-the-art on the standard task of visual relationship detection

Semantic Image Interpretation - Integration of Numerical Data and Logical Knowledge for Cognitive Vision

Semantic Image Interpretation (SII) is the process of generating a structured description of the content of an input image. This description is encoded as a labelled direct graph where nodes correspond to objects in the image and edges to semantic relations between objects. Such a detailed structure allows a more accurate searching and retrieval of images. In this thesis, we propose two well-founded methods for SII. Both methods exploit background knowledge, in the form of logical constraints of a knowledge base, about the domain of the images. The first method formalizes the SII as the extraction of a partial model of a knowledge base. Partial models are built with a clustering and reasoning algorithm that considers both low-level and semantic features of images. The second method uses the framework Logic Tensor Networks to build the labelled direct graph of an image. This framework is able to learn from data in presence of the logical constraints of the knowledge base. Therefore, the graph construction is performed by predicting the labels of the nodes and the relations according to the logical constraints and the features of the objects in the image. These methods improve the state-of-the-art by introducing two well-founded methodologies that integrate low-level and semantic features of images with logical knowledge. Indeed, other methods, do not deal with low-level features or use only statistical knowledge coming from training sets or corpora. Moreover, the second method overcomes the performance of the state-of-the-art on the standard task of visual relationship detection

Ontology Based Semantic Image Interpretation

Semantic image interpretation (SII) leverages Semantic Web ontologies for generating a mathematical structure that describes the content of images. SII algorithms consider the ontologies only in a late phase of the SII process to enrich these structures. In this research proposal we study a well-founded framework that combines logical knowledge with low-level image features in the early phase of SII. The image content is represented with a partial model of an ontology. Each element of the partial model is grounded to a set of segments of the image. Moreover, we propose an approximate algorithm that searches for the most plausible partial model. The comparison of our method with a knowledge-blind baseline shows that the use of ontologies significantly improves the results

SeXAI: Introducing Concepts into Black Boxes for Explainable Artificial Intelligence

The interest in Explainable Artificial Intelligence (XAI) research is dramatically grown during the last few years. The main reason is the need of having systems that beyond being effective are also able to describe how a certain output has been obtained and to present such a description in a comprehensive manner with respect to the target users. A promising research direction making black boxes more transparent is the exploitation of semantic information. Such information can be exploited from different perspectives in order to provide a more comprehensive and interpretable representation of AI models. In this paper, we present the first version of SeXAI, a semantic-based explainable framework aiming to exploit semantic information for making black boxes more transparent. After a theoretical discussion, we show how this research direction is suitable and worthy of investigation by showing its application to a real-world use case