Psychotherapeutic cognition extractor : Lexical ontologies to facilitate cognitive behavioral therapy and trait modelling

Abstract

Abstract Use of artificial intelligence in psychotherapy has mostly, not gone beyond the level of close – ended web based questionnaires. In this research, a novel computational intelligence based approach is proposed to facilitate the psychotherapeutics procedures via creating comfortable platforms both to the consultants and patients. Lack of annotated datasets creates a major barrier in creating stable knowledge models in the arena of artificial intelligence. Amount of annotated datasets available in psychotherapy domain is almost nil. Therefore, after recognizing exclusive attributes like, rich domain modelling capability, contextual knowledge representation ability, ease of scalability and no need of a comprehensively large annotated datasets, it`s decided ontologies to be the ideal form of AI technology to be used in this research, as it characteristics suits very well with the nature of the attempted research problem to be solved. Ontologies are defined as an ideal way of encoding human intelligence to machine intelligence format. Therefore, after consulting multiple consultant psychologists and psychiatrists, covering the entire island, knowledge associated with cognitive behavioral therapy (CBT) procedures and personality trait assessment based on Big Five (Openness, Conscientiousness, Extraversion, Agreeableness and Neuroticism) OCEAN trait modelling are derived. Henceforth, pool of lexicon based ontological structures are proposed, presenting the machine readable form of knowledge extracted from human consultants. To assure smooth, operation of the proposed ontology pools in cognition segmentation, multiple clusters of Part-Of-Speech (POS) tag based rule repositories are derived considering the potential structural aspects of each of cognition expression patterns. Thereafter, each of these cognition specific POS rule sequence is mapped with a specified object property in the ontology. Further, each object property of the ontology, is linked with an annotated cognition specific lexicon pool. This mapping sequence will assure computational process enforcement of the both CBT and OCEAN based psychotherapeutic procedures. The developed application has been tested in both quantitative and qualitative forms. Thematic Analysis evaluation technique is used to assess the textual feedbacks provided by the consultants. Further, confusion matrices have been derived denoting true, positives, true negatives, false positives and false negatives associated with the cognition segmentations processed by the v prototype. With the help of these information, quantitative measures such as precision, recall, sensitivity F-measure and accuracy dimensions are derived. This research contributes to the health informatics domain, which is a very important and new discipline coming under computer science, by releasing, pool of lexicon based ontological structures which can be used for the effective segmentation of the human cognition and trait assessment, facilitating the consultants’ role towards improving the therapeutic alliance in between patient and the consultant. Further the overall architecture provided by this research prototype is an easily expandable base line architecture to cater with new knowledge requirements as well. Therefore, new knowledge aspects associated with CBT and OCEAN characteristics can be easily absorbed by the same architecture proposed by the prototype, making it a stable architecture addressing scalability of the prototype as well. The combination of all these knowledge structures and ontological mappings can be released as one knowledge api, facilitating the reusability and expandability of the structures proposed by any researcher who is interested in coupling cognition segmentation aspects to a potential healthcare information system to be developed.