Mapping the Patient’s Experience: An Applied Ontological Framework for Phenomenological Psychopathology

Mental health research faces a suite of unresolved challenges that have contributed to a stagnation of research efforts and treatment innovation. One such challenge is how to reliably and validly account for the subjective side of patient symptomatology, that is, the patient’s inner experiences or patient phenomenology. Providing a structured, standardised semantics for patient phenomenology would enable future research in novel directions. In this contribution, we aim at initiating a standardized approach to patient phenomenology by sketching a tentative formalisation within the framework of an applied ontology, in the broader context of existing open-source Open Biomedical Ontologies resources such as the Mental Functioning Ontology. We further discuss a number of prevailing challenges and observations bearing on this task

Community based mappings for the semantic web: MappingsTool

An extension of BioPortal, an open source ontology repository developed by the UNIVERSITY OF STANFORD, that facilitates the manipulation of mappings between ontologies. We provide a flexible web user interface that facilitate the workflow to create a mapping and the exploration of the relations between ontologies.Pera Mira, O. (2011). Community based mappings for the semantic web: MappingsTool. http://hdl.handle.net/10251/11159.Archivo delegad

From Affective Science to Psychiatric Disorder: Ontology as a Semantic Bridge

Advances in emotion and affective science have yet to translate routinely into psychiatric research and practice. This is unfortunate since emotion and affect are fundamental components of many psychiatric conditions. Rectifying this lack of interdisciplinary integration could thus be a potential avenue for improving psychiatric diagnosis and treatment. In this contribution, we propose and discuss an ontological framework for explicitly capturing the complex interrelations between affective entities and psychiatric disorders, in order to facilitate mapping and integration between affective science and psychiatric diagnostics. We build on and enhance the categorisation of emotion, affect and mood within the previously developed Emotion Ontology, and that of psychiatric disorders in the Mental Disease Ontology. This effort further draws on developments in formal ontology regarding the distinction between normal and abnormal in order to formalize the interconnections. This operational semantic framework is relevant for applications including clarifying psychiatric diagnostic categories, clinical information systems, and the integration and translation of research results across disciplines

Coordinated pluralism as a means to facilitate integrative taxonomies of cognition

© 2017 Informa UK Limited, trading as Taylor & Francis Group. The past decade has witnessed a growing awareness of conceptual and methodological hurdles within psychology and neuroscience that must be addressed for taxonomic and explanatory progress in understanding psychological functions to be possible. In this paper, I evaluate several recent knowledge-building initiatives aimed at overcoming these obstacles. I argue that while each initiative offers important insights about how to facilitate taxonomic and explanatory progress in psychology and neuroscience, only a “coordinated pluralism” that incorporates positive aspects of each initiative will have the potential for success

Centrifugalno i centripetalno razmišljanje o biopsihosocijalnom modelu u psihijatriji

The biopsychosocial model, which was deeply influential on psychiatry following its introduction by George L. Engel in 1977, has recently made a comeback. Derek Bolton and Grant Gillett have argued that Engel’s original formulation offered a promising general framework for thinking about health and disease, but that this promise requires new empirical and philosophical tools in order to be realized. In particular, Bolton and Gillett offer an original analysis of the ontological relations between Engel’s biological, social, and psychological levels of analysis. I argue that Bolton and Gillett’s updated model, while providing an intriguing new metaphysical framework for medicine, cannot resolve some of the most vexing problems facing psychiatry, which have to do with how to prioritize different sorts of research. These problems are fundamentally ethical, rather than ontological. Without the right prudential motivation, in other words, the unification of psychiatry under a single conceptual framework seems doubtful, no matter how compelling the model. An updated biopsychosocial model should include explicit normative commitments about the aims of medicine that can give guidance about the sorts of causal connections to be prioritized as research and clinical targets.Biopsihosocijalni model, koji je imao dubok utjecaj na psihijatriju nakon što ga je uveo George L. Engel 1977., nedavno se vratio. Derek Bolton i Grant Gillett tvrde da je Engelova izvorna formulacija ponudila obećavajući opći okvir za razmišljanje o zdravlju i bolesti, ali da to obećanje zahtijeva nove empirijske i filozofske alate kako bi se ostvarilo. Bolton i Gillett nude originalnu analizu ontoloških odnosa između Engelove biološke, društvene i psihološke razine analize. Argumentiram da Boltonov i Gillettov ažurirani model, iako pruža intrigantan novi metafizički okvir za medicinu, ne može riješiti neke od najzahtjevnijih problema s kojima se psihijatrija suočava, a koji se odnose na to kako dati prioritet različitim vrstama istraživanja. Ti su problemi u osnovi etički, a ne ontološki. Bez prave prudencijalne motivacije, drugim riječima, objedinjavanje psihijatrije pod jednim pojmovnim okvirom čini se upitnim, ma koliko uvjerljiv model. Ažurirani biopsihosocijalni model trebao bi uključivati ​​eksplicitne normativne obveze o ciljevima medicine koji mogu dati smjernice o vrstama uzročno-posljedičnih veza kojima se treba dati prioritet kao istraživačkim i kliničkim ciljevima

An artificial intelligence natural language processing pipeline for information extraction in neuroradiology

The use of electronic health records in medical research is difficult because of the unstructured format. Extracting information within reports and summarising patient presentations in a way amenable to downstream analysis would be enormously beneficial for operational and clinical research. In this work we present a natural language processing pipeline for information extraction of radiological reports in neurology. Our pipeline uses a hybrid sequence of rule-based and artificial intelligence models to accurately extract and summarise neurological reports. We train and evaluate a custom language model on a corpus of 150000 radiological reports from National Hospital for Neurology and Neurosurgery, London MRI imaging. We also present results for standard NLP tasks on domain-specific neuroradiology datasets. We show our pipeline, called `neuroNLP', can reliably extract clinically relevant information from these reports, enabling downstream modelling of reports and associated imaging on a heretofore unprecedented scale.Comment: 20 pages, 2 png image figure

Conflict within psychosis treatment in the English NHS: investigating the experiences of patients and psychiatrists

The vociferous psychiatric Service User Movement and critical elements within psychiatry form evidence of conflict within the field of psychosis treatment. Psychiatric treatment of psychosis within the English NHS was investigated to understand the conflict-ridden relationships between psychiatrists and patients. The hope was to form a bridge of understanding and dialogue between mainstream psychiatry, its fringes and the Service User Movement. People diagnosed with psychosis who subsequently sought support from the Movement and Consultant Psychiatrists working within the NHS were interviewed, focussing upon their experiences of psychosis treatment. The patients were asked about their experiences of and feelings about their NHS treatment and help received from the Movement and, how and if the experiences had affected their self-understanding. The psychiatrists were asked about their motivations for choosing the speciality, experiences of working with those diagnosed with psychosis and their relationships with patients and other psychiatrists. Interviews were analysed using Framework Analysis, a qualitative method designed to probe individual as well as organisational processes and make policy recommendations. The conflictual dynamic was found to result from a fundamental neglect of existential needs for meaning, hope and relationships in psychiatric training and NHS treatment. The patients felt harmed in treatment because their anxieties about psychic annihilation and need to understand their suffering were ignored. Such harms were found to derive from the lack of focus upon relationships in psychiatric services and training. The psychiatrists suffered low morale and vocational dissatisfaction because their training and work systems left them ill equipped to understand or bear the essential difficulties of the work. Psychoanalytic and other literature is cited to explore unconscious betrayals of human needs in the design of care systems. Ideas are offered to support all involved in the difficult work with psychosis, based upon interventions in psychiatry and other disciplines

Ascertaining Pain in Mental Health Records:Combining Empirical and Knowledge-Based Methods for Clinical Modelling of Electronic Health Record Text

In recent years, state-of-the-art clinical Natural Language Processing (NLP), as in other domains, has been dominated by neural networks and other statistical models. In contrast to the unstructured nature of Electronic Health Record (EHR) text, biomedical knowledge is increasingly available in structured and codified forms, underpinned by curated databases, machine-readable clinical guidelines, and logically defined terminologies. This thesis examines the incorporation of external medical knowledge into clinical NLP and tests these methods on a use case of ascertaining physical pain in clinical notes of mental health records.Pain is a common reason for accessing healthcare resources and has been a growing area of research, especially its impact on mental health. Pain also presents a unique NLP problem due to its ambiguous nature and the varying circumstances in which it can be used. For these reasons, pain has been chosen as a use case, making it a good case study for the application of the methods explored in this thesis. Models are built by assimilating both structured medical knowledge and clinical NLP and leveraging the inherent relations that exist within medical ontologies. The data source used in this project is a mental health EHR database called CRIS, which contains de-identified patient records from the South London and Maudsley NHS Foundation Trust, one of the largest mental health providers in Western Europe.A lexicon of pain terms was developed to identify documents within CRIS mentioning painrelated terms. Gold standard annotations were created by conducting manual annotations on these documents. These gold standard annotations were used to build models for a binary classification task, with the objective of classifying sentences from the clinical text as “relevant”, which indicates the sentence contains relevant mentions of pain, i.e., physical pain affecting the patient, or “not relevant”, which indicates the sentence does not contain mentions of physical pain, or the mention does not relate to the patient (ex: someone else in physical pain). Two models incorporating structured medical knowledge were built:1. a transformer-based model, SapBERT, that utilises a knowledge graph of the UMLS ontology, and2. a knowledge graph embedding model that utilises embeddings from SNOMED CT, which was then used to build a random forest classifier. This was achieved by modelling the clinical pain terms and their relations from SNOMED CT into knowledge graph embeddings, thus combining the data-driven view of clinical language, with the logical view of medical knowledge.These models have been compared with NLP models (binary classifiers) that do not incorporate such structured medical knowledge:1. a transformer-based model, BERT_base, and2. a random forest classifier model.Amongst the two transformer-based models, SapBERT performed better at the classification task (F1-score: 0.98), and amongst the random forest models, the one incorporating knowledge graph embeddings performed better (F1-score: 0.94). The SapBERT model was run on sentences from a cohort of patients within CRIS, with the objective of conducting a prevalence study to understand the distribution of pain based on sociodemographic and diagnostic factors.The contribution of this research is both methodological and practical, showing the difference between a conventional NLP approach of binary classification and one that incorporates external knowledge, and further utilising the models obtained from both these approaches ina prevalence study which was designed based on inputs from clinicians and a patient and public involvement group. The results emphasise the significance of going beyond the conventional approach to NLP when addressing complex issues such as pain.<br/

Enacting the Semantic Web: Ontological Orderings, Negotiated Standards, and Human-machine Translations

Artificial intelligence (AI) that is based upon semantic search has become one of the dominant means for accessing information in recent years. This is particularly the case in mobile contexts, as search based AI are embedded in each of the major mobile operating systems. The implications are such that information is becoming less a matter of choosing between different sets of results, and more of a presentation of a single answer, limiting both the availability of, and exposure to, alternate sources of information. Thus, it is essential to understand how that information comes to be structured and how deterministic systems like search based AI come to understand the indeterminate worlds they are tasked with interrogating. The semantic web, one of the technologies underpinning these systems, creates machine-readable data from the existing web of text and formalizes those machine-readable understandings in ontologies. This study investigates the ways that those semantic assemblages structure, and thus define, the world. In accordance with assemblage theory, it is necessary to study the interactions between the components that make up such data assemblages. As yet, the social sciences have been slow to systematically investigate data assemblages, the semantic web, and the components of these important socio-technical systems. This study investigates one major ontology, Schema.org. It uses netnographic methods to study the construction and use of Schema.org to determine how ontological states are declared and how human-machine translations occur in those development and use processes. This study has two main findings that bear on the relevant literature. First, I find that development and use of the ontology is a product of negotiations with technical standards such that ontologists and users must work around, with, and through the affordances and constraints of standards. Second, these groups adopt a pragmatic and generalizable approach to data modeling and semantic markup that determines ontological context in local and global ways. This first finding is significant in that past work has largely focused on how people work around standards’ limitations, whereas this shows that practitioners also strategically engage with standards to achieve their aims. Second, the particular approach that these groups use in translating human knowledge to machines, differs from the formalized and positivistic approaches described in past work. At a larger level, this study fills a lacuna in the collective understanding of how data assemblages are constructed and operate