Connectionist and Process Modelling of Long-Term Sequence: The Integration of Relative Judgements, Representation and Learning

A large amount of psychological research is devoted to the representation of sequences. It is a fundamental upon which most of the processes of cognition are based. Despite the amount of research into sequencing, there has been relatively little investigation of the types of representations generated in response to sequential information. These representations must allow operations to be performed on individual elements, as well as operations between and among elements. This thesis begins by describing the effects found when subjects are asked to make relative order judgements using sequences which are in long-term storage (e.g. the alphabet or number series). These effects are then used to examine some of the theories and models which have been developed, with a view toward generating a general purpose mechanism with the ability to model all of the different effects found with different types of stimuli. In the course of developing the new model, the neuropsychological findings in the area are examined in Chapter Two. Deficit studies and neuropsychological investigations are able to isolate which aspects of a task appear to be processed in different structures. If a patient loses the ability to perform one aspect of a task but not another, trying to model both of these aspects in one network may be counter-productive. The construction of a new model is begun in Chapter Three. This model is developed in the PDP environment as it offers the ability to change (learn) as a result of experience, and demands a more thorough definition of the mechanisms operating within the network. Chapter Four details a formal definition of the Serial Order Network (SON) model outlined in Chapter Three, including a section devoted to relative order judgements, called the Response Generation Network (RGN) and undertakes a comparison between the SON/RGN and Poltrock's (1990) random walk model described in Chapter One. A review of some of the sequence learning networks developed is undertaken in Chapter Five. This review is used to choose sequence learning networks, which may be used to learn the type of representation needed. These sequence learning networks are investigated in Chapter Six for their ability to learn the sequence incrementally. It is determined that not one of these networks is appropriate. Thus, in Chapter Seven a recitation mechanism is added directly to the representation in the SON. The resulting system is investigated, and it is determined that the system's success in recitation is not dependent on an idiosyncratic setting of the parameters in the network. The definition of the SON and complementary recitation network is not sufficient. The resulting mechanisms should also be compatible with the developmental literature for both children learning sequences for the first time, and adults learning novel sequences. A review of this literature is conducted in Chapter Eight. It is also necessary to explain how this SON representation can be developed, and how the model can be used to explain the seeming hierarchic nature of some sequences. In Chapter Nine a mechanism designed to mimic a hierarchical structure for the representation of a sequence is developed. A learning mechanism is defined for the resulting system. This system is then investigated for its ability to recite both hierarchic and non-hierarchic sequences, and to generate the relative order and developmental effects referred to in Chapters One and Nine. The model developed in this thesis is the only model existent which is able to explain sequence learning, representation and relative order effects, and represents an advance in the approach to modelling sequence information

Development of a cardiac-centered frailty ontology

Abstract Background A Cardiac-centered Frailty Ontology can be an important foundation for using NLP to assess patient frailty. Frailty is an important consideration when making patient treatment decisions, particularly in older adults, those with a cardiac diagnosis, or when major surgery is a consideration. Clinicians often report patient’s frailty in progress notes and other documentation. Frailty is recorded in many different ways in patient records and many different validated frailty-measuring instruments are available, with little consistency across instruments. We specifically explored concepts relevant to decisions regarding cardiac interventions. We based our work on text found in a large corpus of clinical notes from the Department of Veterans Affairs (VA) national Electronic Health Record (EHR) database. Results The full ontology has 156 concepts, with 246 terms. It includes 86 concepts we expect to find in clinical documents, with 12 qualifier values. The remaining 58 concepts represent hierarchical groups (e.g., physical function findings). Our top-level class is clinical finding, which has children clinical history finding, instrument finding, and physical examination finding, reflecting the OGMS definition of clinical finding. Instrument finding is any score found for the existing frailty instruments. Within our ontology, we used SNOMED-CT concepts where possible. Some of the 86 concepts we expect to find in clinical documents are associated with the properties like ability interpretation. The concept ability to walk can either be able, assisted or unable. Each concept-property level pairing gets a different frailty score. Each scored concept received three scores: a frailty score, a relevance to cardiac decisions score, and a likelihood of resolving after the recommended intervention score. The ontology includes the relationship between scores from ten frailty instruments and frailty as assessed using ontology concepts. It also included rules for mapping ontology elements to instrument items for three common frailty assessment instruments. Ontology elements are used in two clinical NLP systems. Conclusions We developed and validated a Cardiac-centered Frailty Ontology, which is a machine-interoperable description of frailty that reflects all the areas that clinicians consider when deciding which cardiac intervention will best serve the patient as well as frailty indications generally relevant to medical decisions. The ontology owl file is available on Bioportal at http://bioportal.bioontology.org/ontologies/CCFO