Bottom-Up Modeling of Permissions to Reuse Residual Clinical Biospecimens and Health Data

Consent forms serve as evidence of permissions granted by patients for clinical procedures. As the recognized value of biospecimens and health data increases, many clinical consent forms also seek permission from patients or their legally authorized representative to reuse residual clinical biospecimens and health data for secondary purposes, such as research. Such permissions are also granted by the government, which regulates how residual clinical biospecimens may be reused with or without consent. There is a need for increasingly capable information systems to facilitate discovery, access, and responsible reuse of residual clinical biospecimens and health data in accordance with these permissions. Semantic web technologies, especially ontologies, hold great promise as infrastructure for scalable, semantically interoperable approaches in healthcare and research. While there are many published ontologies for the biomedical domain, there is not yet ontological representation of the permissions relevant for reuse of residual clinical biospecimens and health data. The Informed Consent Ontology (ICO), originally designed for representing consent in research procedures, may already contain core classes necessary for representing clinical consent processes. However, formal evaluation is needed to make this determination and to extend the ontology to cover the new domain. This dissertation focuses on identifying the necessary information required for facilitating responsible reuse of residual clinical biospecimens and health data, and evaluating its representation within ICO. The questions guiding these studies include: 1. What is the necessary information regarding permissions for facilitating responsible reuse of residual clinical biospecimens and health data? 2. How well does the Informed Consent Ontology represent the identified information regarding permissions and obligations for reuse of residual clinical biospecimens and health data? We performed three sequential studies to answer these questions. First, we conducted a scoping review to identify regulations and norms that bear authority or give guidance over reuse of residual clinical biospecimens and health data in the US, the permissions by which reuse of residual clinical biospecimens and health data may occur, and key issues that must be considered when interpreting these regulations and norms. Second, we developed and tested an annotation scheme to identify permissions within clinical consent forms. Lastly, we used these findings as source data for bottom-up modelling and evaluation of ICO for representation of this new domain. We found considerable overlap in classes already in ICO and those necessary for representing permissions to reuse residual clinical biospecimens and health data. However, we also identified more than fifty classes that should be added to or imported into ICO. These efforts provide a foundation for comprehensively representing permissions to reuse residual clinical biospecimens and health data. Such representation fills a critical gap for developing applications which safeguard biospecimen resources and enable querying based on their permissions for use. By modeling information about permissions in an ontology, the heterogeneity of these permissions at a range of levels (e.g., federal regulations, consent forms) can be richly represented using entity-relationship links and embedded rules of inference and inheritance. Furthermore, by developing this content in ICO, missing content will be added to the Open Biological and Biomedical Ontology (OBO) Foundry, enabling use alongside other widely adopted ontologies and providing a valuable resource for biospecimen and information management. These methods may also serve as a model for domain experts to interact with ontology development communities to improve ontologies and address gaps which hinder successful uptake.PHDNursingUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/162937/1/eliewolf_1.pd

A Life Cycle Approach to the Development and Validation of an Ontology of the U.S. Common Rule (45 C.F.R. § 46)

Requirements for the protection of human research subjects stem from directly from federal regulation by the Department of Health and Human Services in Title 45 of the Code of Federal Regulations (C.F.R.) part 46. 15 other federal agencies include subpart A of part 46 verbatim in their own body of regulation. Hence 45 C.F.R. part 46 subpart A has come to be called colloquially the ‘Common Rule.’ Overall motivation for this study began as a desire to facilitate the ethical sharing of biospecimen samples from large biospecimen collections by using ontologies. Previous work demonstrated that in general the informed consent process and subsequent decision making about data and specimen release still relies heavily on paper-based informed consent forms and processes. Consequently, well-validated computable models are needed to provide an enhanced foundation for data sharing. This dissertation describes the development and validation of a Common Rule Ontology (CRO), expressed in the OWL-2 Web Ontology Language, and is intended to provide a computable semantic knowledge model for assessing and representing components of the information artifacts of required as part of regulated research under 45 C.F.R. § 46. I examine if the alignment of this ontology with the Basic Formal Ontology and other ontologies from the Open Biomedical Ontology (OBO) Foundry provide a good fit for the regulatory aspects of the Common Rule Ontology. The dissertation also examines and proposes a new method for ongoing evaluation of ontology such as CRO across the ontology development lifecycle and suggest methods to achieve high quality, validated ontologies. While the CRO is not in itself intended to be a complete solution to the data and specimen sharing problems outlined above, it is intended to produce a well-validated computationally grounded framework upon which others can build. This model can be used in future work to build decision support systems to assist Institutional Review Boards (IRBs), regulatory personnel, honest brokers, tissue bank managers, and other individuals in the decision-making process involving biorepository specimen and data sharing

The smashHitCore Ontology for GDPR-Compliant Sensor Data Sharing in Smart Cities

The adoption of the General Data Protection Regulation (GDPR) has resulted in a significant shift in how the data of European Union citizens is handled. A variety of data sharing challenges in scenarios such as smart cities have arisen, especially when attempting to semantically represent GDPR legal bases, such as consent, contracts and the data types and specific sources related to them. Most of the existing ontologies that model GDPR focus mainly on consent. In order to represent other GDPR bases, such as contracts, multiple ontologies need to be simultaneously reused and combined, which can result in inconsistent and conflicting knowledge representation. To address this challenge, we present the smashHitCore ontology. smashHitCore provides a unified and coherent model for both consent and contracts, as well as the sensor data and data processing associated with them. The ontology was developed in response to real-world sensor data sharing use cases in the insurance and smart city domains. The ontology has been successfully utilised to enable GDPR-complaint data sharing in a connected car for insurance use cases and in a city feedback system as part of a smart city use case

ONS : an ontology for a standardized description of interventions and observational studies in nutrition

Background: The multidisciplinary nature of nutrition research is one of its main strengths. At the same time, however, it presents a major obstacle to integrate data analysis, especially for the terminological and semantic interpretations that specific research fields or communities are used to. To date, a proper ontology to structure and formalize the concepts used for the description of nutritional studies is still lacking. Results: We have developed the Ontology for Nutritional Studies (ONS) by harmonizing selected pre-existing de facto ontologies with novel health and nutritional terminology classifications. The ONS is the result of a scholarly consensus of 51 research centers in nine European countries. The ontology classes and relations are commonly encountered while conducting, storing, harmonizing, integrating, describing, and searching nutritional studies. The ONS facilitates the description and specification of complex nutritional studies as demonstrated with two application scenarios. Conclusions: The ONS is the first systematic effort to provide a solid and extensible formal ontology framework for nutritional studies. Integration of new information can be easily achieved by the addition of extra modules (i.e., nutrigenomics, metabolomics, nutrikinetics, and quality appraisal). The ONS provides a unified and standardized terminology for nutritional studies as a resource for nutrition researchers who might not necessarily be familiar with ontologies and standardization concepts

An ontology to standardize research output of nutritional epidemiology : from paper-based standards to linked content

Background: The use of linked data in the Semantic Web is a promising approach to add value to nutrition research. An ontology, which defines the logical relationships between well-defined taxonomic terms, enables linking and harmonizing research output. To enable the description of domain-specific output in nutritional epidemiology, we propose the Ontology for Nutritional Epidemiology (ONE) according to authoritative guidance for nutritional epidemiology. Methods: Firstly, a scoping review was conducted to identify existing ontology terms for reuse in ONE. Secondly, existing data standards and reporting guidelines for nutritional epidemiology were converted into an ontology. The terms used in the standards were summarized and listed separately in a taxonomic hierarchy. Thirdly, the ontologies of the nutritional epidemiologic standards, reporting guidelines, and the core concepts were gathered in ONE. Three case studies were included to illustrate potential applications: (i) annotation of existing manuscripts and data, (ii) ontology-based inference, and (iii) estimation of reporting completeness in a sample of nine manuscripts. Results: Ontologies for food and nutrition (n = 37), disease and specific population (n = 100), data description (n = 21), research description (n = 35), and supplementary (meta) data description (n = 44) were reviewed and listed. ONE consists of 339 classes: 79 new classes to describe data and 24 new classes to describe the content of manuscripts. Conclusion: ONE is a resource to automate data integration, searching, and browsing, and can be used to assess reporting completeness in nutritional epidemiology

Getting your DUCs in a row - standardising the representation of Digital Use Conditions

Improving patient care and advancing scientific discovery requires responsible sharing of research data, healthcare records, biosamples, and biomedical resources that must also respect applicable use conditions. Defining a standard to structure and manage these use conditions is a complex and challenging task. This is exemplified by a near unlimited range of asset types, a high variability of applicable conditions, and differing applications at the individual or collective level. Furthermore, the specifics and granularity required are likely to vary depending on the ultimate contexts of use. All these factors confound alignment of institutional missions, funding objectives, regulatory and technical requirements to facilitate effective sharing. The presented work highlights the complexity and diversity of the problem, reviews the current state of the art, and emphasises the need for a flexible and adaptable approach. We propose Digital Use Conditions (DUC) as a framework that addresses these needs by leveraging existing standards, striking a balance between expressiveness versus ambiguity, and considering the breadth of applicable information with their context of use

Ontology and Cognitive Outcomes

The term ‘intelligence’ as used in this paper refers to items of knowledge collected for the sake of assessing and maintaining national security. The intelligence community (IC) of the United States (US) is a community of organizations that collaborate in collecting and processing intelligence for the US. The IC relies on human-machine-based analytic strategies that 1) access and integrate vast amounts of information from disparate sources, 2) continuously process this information, so that, 3) a maximally comprehensive understanding of world actors and their behaviors can be developed and updated. Herein we describe an approach to utilizing outcomes-based learning (OBL) to support these efforts that is based on an ontology of the cognitive processes performed by intelligence analysts. Of particular importance to the Cognitive Process Ontology is the class Representation that is Warranted. Such a representation is descriptive in nature and deserving of trust in its veridicality. The latter is because a Representation that is Warranted is always produced by a process that was vetted (or successfully designed) to reliably produce veridical representations. As such, Representations that are Warranted are what in other contexts we might refer to as ‘items of knowledge’

Design and Architecture of an Ontology-driven Dialogue System for HPV Vaccine Counseling

Speech and conversational technologies are increasingly being used by consumers, with the inevitability that one day they will be integrated in health care. Where this technology could be of service is in patient-provider communication, specifically for communicating the risks and benefits of vaccines. Human papillomavirus (HPV) vaccine, in particular, is a vaccine that inoculates individuals from certain HPV viruses responsible for adulthood cancers - cervical, head and neck cancers, etc. My research focuses on the architecture and development of speech-enabled conversational agent that relies on series of consumer-centric health ontologies and the technology that utilizes these ontologies. Ontologies are computable artifacts that encode and structure domain knowledge that can be utilized by machines to provide high level capabilities, such as reasoning and sharing information. I will focus the agent’s impact on the HPV vaccine domain to observe if users would respond favorably towards conversational agents and the possible impact of the agent on their beliefs of the HPV vaccine. The approach of this study involves a multi-tier structure. The first tier is the domain knowledge base, the second is the application interaction design tier, and the third is the feasibility assessment of the participants. The research in this study proposes the following questions: Can ontologies support the system architecture for a spoken conversational agent for HPV vaccine counseling? How would prospective users’ perception towards an agent and towards the HPV vaccine be impacted after using conversational agent for HPV vaccine education? The outcome of this study is a comprehensive assessment of a system architecture of a conversational agent for patient-centric HPV vaccine counseling. Each layer of the agent architecture is regulated through domain and application ontologies, and supported by the various ontology-driven software components that I developed to compose the agent architecture. Also discussed in this work, I present preliminary evidence of high usability of the agent and improvement of the users’ health beliefs toward the HPV vaccine. All in all, I introduce a comprehensive and feasible model for the design and development of an open-sourced, ontology-driven conversational agent for any health consumer domain, and corroborate the viability of a conversational agent as a health intervention tool