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

Modeling Faceted Browsing with Category Theory for Reuse and Interoperability

Faceted browsing (also called faceted search or faceted navigation) is an exploratory search model where facets assist in the interactive navigation of search results. Facets are attributes that have been assigned to describe resources being explored; a faceted taxonomy is a collection of facets provided by the interface and is often organized as sets, hierarchies, or graphs. Faceted browsing has become ubiquitous with modern digital libraries and online search engines, yet the process is still difficult to abstractly model in a manner that supports the development of interoperable and reusable interfaces. We propose category theory as a theoretical foundation for faceted browsing and demonstrate how the interactive process can be mathematically abstracted in order to support the development of reusable and interoperable faceted systems. Existing efforts in facet modeling are based upon set theory, formal concept analysis, and light-weight ontologies, but in many regards they are implementations of faceted browsing rather than a specification of the basic, underlying structures and interactions. We will demonstrate that category theory allows us to specify faceted objects and study the relationships and interactions within a faceted browsing system. Resulting implementations can then be constructed through a category-theoretic lens using these models, allowing abstract comparison and communication that naturally support interoperability and reuse. In this context, reuse and interoperability are at two levels: between discrete systems and within a single system. Our model works at both levels by leveraging category theory as a common language for representation and computation. We will establish facets and faceted taxonomies as categories and will demonstrate how the computational elements of category theory, including products, merges, pushouts, and pullbacks, extend the usefulness of our model. More specifically, we demonstrate that categorical constructions such as the pullback and pushout operations can help organize and reorganize facets; these operations in particular can produce faceted views containing relationships not found in the original source taxonomy. We show how our category-theoretic model of facets relates to database schemas and discuss how this relationship assists in implementing the abstractions presented. We give examples of interactive interfaces from the biomedical domain to help illustrate how our abstractions relate to real-world requirements while enabling systematic reuse and interoperability. We introduce DELVE (Document ExpLoration and Visualization Engine), our framework for developing interactive visualizations as modular Web-applications in order to assist researchers with exploratory literature search. We show how facets relate to and control visualizations; we give three examples of text visualizations that either contain or interact with facets. We show how each of these visualizations can be represented with our model and demonstrate how our model directly informs implementation. With our general framework for communicating consistently about facets at a high level of abstraction, we enable the construction of interoperable interfaces and enable the intelligent reuse of both existing and future efforts

The State-of-the-Art of Set Visualization

Sets comprise a generic data model that has been used in a variety of data analysis problems. Such problems involve analysing and visualizing set relations between multiple sets defined over the same collection of elements. However, visualizing sets is a non-trivial problem due to the large number of possible relations between them. We provide a systematic overview of state-of-the-art techniques for visualizing different kinds of set relations. We classify these techniques into six main categories according to the visual representations they use and the tasks they support. We compare the categories to provide guidance for choosing an appropriate technique for a given problem. Finally, we identify challenges in this area that need further research and propose possible directions to address these challenges. Further resources on set visualization are available at http://www.setviz.net

The Behavior-Driven Observation. Definition and development of an adaptive observational assessment

The observation in psychological assessment provides clinicians with a variety of useful insights about the symptoms of mental disorders. Nonetheless, the application of observational instruments has decreased during the last years, mainly due to their administration complexity and time consumption. A consequence of this general reduction in application is that some innovations fruitfully applied by other psychological assessment instruments, such as the self-reports, are still unexplored. For instance, little focus has been put on the possibility of implementing observational measures with adaptive algorithms. In observational assessment, these algorithms have been applied only by some software developed for observers training; their implementation in observational assessment instruments is still an open challenge. The aim of the present Ph.D. project is to develop an observational adaptive instrument able to help clinicians to generate accurate behavioral response patterns reducing, simultaneously, the time of the observational assessment. The definition of such an instrument has been a sequential process that started from a deep analysis of the items that should be observed, followed by the consideration of how to observe each of them. These first issues were accounted in Chapter 1, in which an overview of the literature was performed in order to examine all the features necessary to adequately conduct an observational assessment. A specific attention was dedicated on the possible biases that could affect raters, leading to higher probabilities of false positive and negatives on the observed behaviors. Finally, the state of the art relative to the application of adaptive algorithms in observational assessments was introduced and discussed. The second step toward the definition of the expected instrument consisted in defining a non adaptive checklist evaluating the behaviors of a mental disorder, possibly based on a formal methodology. In Chapter 2, the Formal Psychological Assessment (FPA) was introduced, describing its deterministic and probabilistic features. FPA is a methodology allowing to define assessment instruments starting from the relation between a set of items and a set of clinical issues of a disorder. In Chapter's end, it was shown how FPA could be extended also to observational assessment composed by multiple measures. In Chapter 3, the FPA was applied to develop the paper-and-pencil version of the final checklist. The negative symptomatology of schizophrenia was selected as the target mental disorder. A set of 138 items describing nonverbal behaviors was selected from instruments frequently used in the evaluation of schizophrenia. This list was then mapped to a list of 14 negative symptoms, selected in both scientific literature and DSM-5. The application of formal and logical steps provided by FPA led to a final checklist of 22 items, divided into two subscales, exhaustively investigating the 14 negative symptoms. In particular, it emerged how the mapping between items and investigated symptoms defined a deterministic model of assessment in which the clinician could be informed not only of which negative symptoms are evaluated by each item, but also of the relations among items. This model of assessment was later validated, in order to convert it into a probabilistic model that would have been correctly implemented into an adaptive instrument. In Chapter 4, the validation procedure is described. 172 videos of clinical interviews were observed by two independent raters, who filled the new checklist during one-zero sampling observations and generated modal response patterns for both subscales. Such patterns were used to apply the Basic local Independence Model (BLIM), a probabilistic model allowing to estimate the global fit indexes of the checklist and the false positive and negative rates for each item. Results showed adequate fit indexes for both subscales of the checklist with acceptable error rates for each item, which were extremely low especially in respect to false positive rates. The obtained probabilistic model of assessment and its parameters estimates were then used to calibrate an observational adaptive algorithm. In Chapter 5, the first version of the Behavior-Driven Observation (BDO) was introduced, namely the adaptive observational checklist proposed by the present project. After its formulation, the BDO was tested on real data by a simulation study in which both its accuracy and efficiency were examined. Results showed how the BDO algorithm was able to accurately reproduce almost all the non adaptive response patterns, with an average reduction by 38% of suggested items to complete the entire assessment. Finally, the accuracy and the efficiency of the BDO were tested during real observations, in order to understand if the BDO led to accurately replicate the non adaptive response patterns when used by human raters, with similar savings in terms of efficiency. Two independent trained raters observed twice the videos of twenty patients with a diagnosis of schizophrenia with negative symptoms, filling the two checklist's versions during observations. The observations on the same patient were far one week from each other. A very good intra-rater agreement emerged for each rater, suggesting both a good coherence over time of raters and a good ability of the BDO to replicate the response patterns of its non adaptive counterpart. Likewise, encouraging results were found in regard to BDO's efficiency: The savings in terms of suggested items were the same of the simulation study, for each rater; moreover, such savings corresponded to a reduction of the observational time. Taken together, the results of this Ph.D. project suggest that is possible to define an adaptive observational checklist able to help clinician to collect information not otherwise detectable with other assessment modalities. The BDO, in fact, could guide the observation by suggesting which behavior should be observed, taking into account the false positive/negative rates for each behavior. In this way, the accuracy of the final clinical output is increased as well as the efficiency of its generation. Such a clinical output could provide clinicians with a comprehensive set of information, such as the precise response pattern observed during the observation, the most plausible symptoms related to that response pattern and their probability values. All this information, in turn, can be finally integrated with other ones collected from different assessment instruments (e.g., interview, self-report), in order to have a broader frame of patient's condition and, maybe, set an individualized treatment