Dermoscopy Differential Diagnosis Explorer (D3X) Ontology to Aggregate and Link Dermoscopic Patterns to Differential Diagnoses: Development and Usability Study.

BACKGROUND: Dermoscopy is a growing field that uses microscopy to allow dermatologists and primary care physicians to identify skin lesions. For a given skin lesion, a wide variety of differential diagnoses exist, which may be challenging for inexperienced users to name and understand. OBJECTIVE: In this study, we describe the creation of the dermoscopy differential diagnosis explorer (D3X), an ontology linking dermoscopic patterns to differential diagnoses. METHODS: Existing ontologies that were incorporated into D3X include the elements of visuals ontology and dermoscopy elements of visuals ontology, which connect visual features to dermoscopic patterns. A list of differential diagnoses for each pattern was generated from the literature and in consultation with domain experts. Open-source images were incorporated from DermNet, Dermoscopedia, and open-access research papers. RESULTS: D3X was encoded in the OWL 2 web ontology language and includes 3041 logical axioms, 1519 classes, 103 object properties, and 20 data properties. We compared D3X with publicly available ontologies in the dermatology domain using a semiotic theory-driven metric to measure the innate qualities of D3X with others. The results indicate that D3X is adequately comparable with other ontologies of the dermatology domain. CONCLUSIONS: The D3X ontology is a resource that can link and integrate dermoscopic differential diagnoses and supplementary information with existing ontology-based resources. Future directions include developing a web application based on D3X for dermoscopy education and clinical practice.

Translating motivational interviewing for the HPV vaccine into a computable ontology model for automated AI conversational interaction.

Human papillomavirus (HPV) vaccinations are lower than expected. To protect the onset of head and neck cancers, innovative strategies to improve the rates are needed. Artificial intelligence may offer some solutions, specifically conversational agents to perform counseling methods. We present our efforts in developing a dialogue model for automating motivational interviewing (MI) to encourage HPV vaccination. We developed a formalized dialogue model for MI using an existing ontology-based framework to manifest a computable representation using OWL2. New utterance classifications were identified along with the ontology that encodes the dialogue model. Our work is available on GitHub under the GPL v.3. We discuss how an ontology-based model of MI can help standardize/formalize MI counseling for HPV vaccine uptake. Our future steps will involve assessing MI fidelity of the ontology model, operationalization, and testing the dialogue model in a simulation with live participants.

A Semantic Approach to Describe Social and Economic Characteristics That Impact Health Outcomes (Social Determinants of Health): Ontology Development Study.

BACKGROUND: Social determinants of health (SDoH) have been described by the World Health Organization as the conditions in which individuals are born, live, work, and age. These conditions can be grouped into 3 interrelated levels known as macrolevel (societal), mesolevel (community), and microlevel (individual) determinants. The scope of SDoH expands beyond the biomedical level, and there remains a need to connect other areas such as economics, public policy, and social factors. OBJECTIVE: Providing a computable artifact that can link health data to concepts involving the different levels of determinants may improve our understanding of the impact SDoH have on human populations. Modeling SDoH may help to reduce existing gaps in the literature through explicit links between the determinants and biological factors. This in turn can allow researchers and clinicians to make better sense of data and discover new knowledge through the use of semantic links. METHODS: An experimental ontology was developed to represent knowledge of the social and economic characteristics of SDoH. Information from 27 literature sources was analyzed to gather concepts and encoded using Web Ontology Language, version 2 (OWL2) and Protégé. Four evaluators independently reviewed the ontology axioms using natural language translation. The analyses from the evaluations and selected terminologies from the Basic Formal Ontology were used to create a revised ontology with a broad spectrum of knowledge concepts ranging from the macrolevel to the microlevel determinants. RESULTS: The literature search identified several topics of discussion for each determinant level. Publications for the macrolevel determinants centered around health policy, income inequality, welfare, and the environment. Articles relating to the mesolevel determinants discussed work, work conditions, psychosocial factors, socioeconomic position, outcomes, food, poverty, housing, and crime. Finally, sources found for the microlevel determinants examined gender, ethnicity, race, and behavior. Concepts were gathered from the literature and used to produce an ontology consisting of 383 classes, 109 object properties, and 748 logical axioms. A reasoning test revealed no inconsistent axioms. CONCLUSIONS: This ontology models heterogeneous social and economic concepts to represent aspects of SDoH. The scope of SDoH is expansive, and although the ontology is broad, it is still in its early stages. To our current understanding, this ontology represents the first attempt to concentrate on knowledge concepts that are currently not covered by existing ontologies. Future direction will include further expanding the ontology to link with other biomedical ontologies, including alignment for granular semantics.

The spread of COVID-19 vaccine information in Arabic on YouTube: A network exposure study.

Objective: The Arabic-speaking world had the lowest vaccine rates worldwide. The region's increasing reliance on social media as a source of COVID-19 information coupled with the increasing popularity of YouTube in the Middle East and North Africa region begs the question of what COVID-19 vaccine content is available in Arabic on YouTube. Given the platform's reputation for being a hotbed for vaccine-related misinformation in English, this study explored the COVID-19 vaccine-related content an individual is likely to be exposed to on YouTube when using keyword-based search or redirected to YouTube from another platform from an anti-vaccine seed video in Arabic. Methods: Only using the Arabic language, four networks of videos based on YouTube's recommendations were created in April 2021. Two search networks were created based on Arabic pro-vaccine and anti-vaccine keywords, and two seed networks were created from conspiracy theory and anti-vaccine expert seed videos. The network exposure model was used to examine the video contents and network structures. Results: Results show that users had a low chance of being exposed to anti-vaccine content in Arabic compared to the results of a previous study of YouTube content in English. Of the four networks, only the anti-vaccine expert network had a significant likelihood of exposing the user to more anti-vaccine videos. Implications were discussed. Conclusion: YouTube deserves credit for its efforts to clean up and limit anti-vaccine content exposure in Arabic on its platform, but continuous evaluations of the algorithm functionality are warranted.

DEVO: an ontology to assist with dermoscopic feature standardization.

BACKGROUND: The utilization of dermoscopic analysis is becoming increasingly critical for diagnosing skin diseases by physicians and even artificial intelligence. With the expansion of dermoscopy, its vocabulary has proliferated, but the rapid evolution of the vocabulary of dermoscopy without standardized control is counterproductive. We aimed to develop a domain-specific ontology to formally represent knowledge for certain dermoscopic features. METHODS: The first phase involved creating a fundamental-level ontology that covers the fundamental aspects and elements in describing visualizations, such as shapes and colors. The second phase involved creating a domain ontology that harnesses the fundamental-level ontology to formalize the definitions of dermoscopic metaphorical terms. RESULTS: The Dermoscopy Elements of Visuals Ontology (DEVO) contains 1047 classes, 47 object properties, and 16 data properties. It has a better semiotic score compared to similar ontologies of the same domain. Three human annotators also examined the consistency, complexity, and future application of the ontology. CONCLUSIONS: The proposed ontology was able to harness the definitions of metaphoric terms by decomposing them into their visual elements. Future applications include providing education for trainees and diagnostic support for dermatologists, with the goal of generating responses to queries about dermoscopic features and integrating these features to diagnose skin diseases.

CRENO: An ontology to model concepts relating to culture, race, ethnicity, and nationality for health data.

Generating categories and classifications is a common function in life science research; however, categorizing the human population based on "race" remains controversial. There is an awareness and recognition of social-economic disparities with respect to health which are sometimes impacted by someone's ethnicity or race. This work describes an endeavor to develop a computable ontology model to represent a standardization of the concepts surrounding culture, race, ethnicity, and nationality - concepts misrepresented widely. We constructed an OWL ontology based on reliable resources with iterative human expert evaluations and aligned it to existing biomedical ontological models. The effort produced a preliminary ontology that expresses concepts related to classes of ethnic, racial, national, and cultural identities and showcases how health disparity data can be linked and expressed within our ontological framework. Future work will explore automated methods to expand the ontology and its utilization for clinical informatics.

Systematic design and data-driven evaluation of social determinants of health ontology (SDoHO).

OBJECTIVE: Social determinants of health (SDoH) play critical roles in health outcomes and well-being. Understanding the interplay of SDoH and health outcomes is critical to reducing healthcare inequalities and transforming a "sick care" system into a "health-promoting" system. To address the SDOH terminology gap and better embed relevant elements in advanced biomedical informatics, we propose an SDoH ontology (SDoHO), which represents fundamental SDoH factors and their relationships in a standardized and measurable way. MATERIAL AND METHODS: Drawing on the content of existing ontologies relevant to certain aspects of SDoH, we used a top-down approach to formally model classes, relationships, and constraints based on multiple SDoH-related resources. Expert review and coverage evaluation, using a bottom-up approach employing clinical notes data and a national survey, were performed. RESULTS: We constructed the SDoHO with 708 classes, 106 object properties, and 20 data properties, with 1,561 logical axioms and 976 declaration axioms in the current version. Three experts achieved 0.967 agreement in the semantic evaluation of the ontology. A comparison between the coverage of the ontology and SDOH concepts in 2 sets of clinical notes and a national survey instrument also showed satisfactory results. DISCUSSION: SDoHO could potentially play an essential role in providing a foundation for a comprehensive understanding of the associations between SDoH and health outcomes and paving the way for health equity across populations. CONCLUSION: SDoHO has well-designed hierarchies, practical objective properties, and versatile functionalities, and the comprehensive semantic and coverage evaluation achieved promising performance compared to the existing ontologies relevant to SDoH.

Application of an ontology for model cards to generate computable artifacts for linking machine learning information from biomedical research.

Model card reports provide a transparent description of machine learning models which includes information about their evaluation, limitations, intended use, etc. Federal health agencies have expressed an interest in model cards report for research studies using machine-learning based AI. Previously, we have developed an ontology model for model card reports to structure and formalize these reports. In this paper, we demonstrate a Java-based library (OWL API, FaCT++) that leverages our ontology to publish computable model card reports. We discuss future directions and other use cases that highlight applicability and feasibility of ontology-driven systems to support FAIR challenges.

Toward a standard formal semantic representation of the model card report.

BACKGROUND: Model card reports aim to provide informative and transparent description of machine learning models to stakeholders. This report document is of interest to the National Institutes of Health's Bridge2AI initiative to address the FAIR challenges with artificial intelligence-based machine learning models for biomedical research. We present our early undertaking in developing an ontology for capturing the conceptual-level information embedded in model card reports. RESULTS: Sourcing from existing ontologies and developing the core framework, we generated the Model Card Report Ontology. Our development efforts yielded an OWL2-based artifact that represents and formalizes model card report information. The current release of this ontology utilizes standard concepts and properties from OBO Foundry ontologies. Also, the software reasoner indicated no logical inconsistencies with the ontology. With sample model cards of machine learning models for bioinformatics research (HIV social networks and adverse outcome prediction for stent implantation), we showed the coverage and usefulness of our model in transforming static model card reports to a computable format for machine-based processing. CONCLUSIONS: The benefit of our work is that it utilizes expansive and standard terminologies and scientific rigor promoted by biomedical ontologists, as well as, generating an avenue to make model cards machine-readable using semantic web technology. Our future goal is to assess the veracity of our model and later expand the model to include additional concepts to address terminological gaps. We discuss tools and software that will utilize our ontology for potential application services.

Understanding Information Needs and Barriers to Accessing Health Information Across All Stages of Pregnancy: Systematic Review.

BACKGROUND: Understanding consumers' health information needs across all stages of the pregnancy trajectory is crucial to the development of mechanisms that allow them to retrieve high-quality, customized, and layperson-friendly health information. OBJECTIVE: The objective of this study was to identify research gaps in pregnancy-related consumer information needs and available information from different sources. METHODS: We conducted a systematic review of CINAHL, Cochrane, PubMed, and Web of Science for relevant articles that were published from 2009 to 2019. The quality of the included articles was assessed using the Critical Appraisal Skills Program. A descriptive data analysis was performed on these articles. Based on the review result, we developed the Pregnancy Information Needs Ontology (PINO) and made it publicly available in GitHub and BioPortal. RESULTS: A total of 33 articles from 9 countries met the inclusion criteria for this review, of which the majority were published no earlier than 2016. Most studies were either descriptive (9/33, 27%), interviews (7/33, 21%), or surveys/questionnaires (7/33, 21%); 20 articles mentioned consumers' pregnancy-related information needs. Half (9/18, 50%) of the human-subject studies were conducted in the United States. More than a third (13/33, 39%) of all studies focused on during-pregnancy stage; only one study (1/33, 3%) was about all stages of pregnancy. The most frequent consumer information needs were related to labor delivery (9/20, 45%), medication in pregnancy (6/20, 30%), newborn care (5/20, 25%), and lab tests (6/20, 30%). The most frequently available source of information was the internet (15/24, 63%). PINO consists of 267 classes, 555 axioms, and 271 subclass relationships. CONCLUSIONS: Only a few articles assessed the barriers to access to pregnancy-related information and the quality of each source of information; further work is needed. Future work is also needed to address the gaps between the information needed and the information available.

Community-clinic linkages for promoting HIV prevention: organizational networks for PrEP client referrals and collaborations.

Community-clinic linkages may help communities increase HIV pre-exposure prophylaxis (PrEP) uptake. Referrals from community-based organizations may be particularly important for linking Black men who have sex with men (MSM) to PrEP. This study describes PrEP referral and HIV/STI prevention networks among organizations that serve MSM in Houston, TX (N = 40), and Chicago, IL (N = 28), and compares network positions of organizations based on percentage of Black/African American clients. A majority of organizations conducted PrEP awareness/promotion activities, but fewer made PrEP referrals, with little overlap between the collaboration and referral networks. The networks tended to have a densely connected core group of organizations and more a peripheral group of organizations linking into the core with relatively few times among themselves; this core/periphery structure is efficient, but vulnerable to disruptions. The percentage of Black/African American clients organizations served was not related to most measures of network centrality. However, in Houston's collaboration network, higher Black-serving organizations tended not to hold as influential positions for controlling communications or flows of resources. The findings indicate a potential to leverage collaborations into PrEP referral pathways to enhance PrEP promotion efforts and identify opportunities to address racial disparities in PrEP uptake.

The ontology of fast food facts: conceptualization of nutritional fast food data for consumers and semantic web applications.

BACKGROUND: Fast food with its abundance and availability to consumers may have health consequences due to the high calorie intake which is a major contributor to life threatening diseases. Providing nutritional information has some impact on consumer decisions to self regulate and promote healthier diets, and thus, government regulations have mandated the publishing of nutritional content to assist consumers, including for fast food. However, fast food nutritional information is fragmented, and we realize a benefit to collate nutritional data to synthesize knowledge for individuals. METHODS: We developed the ontology of fast food facts as an opportunity to standardize knowledge of fast food and link nutritional data that could be analyzed and aggregated for the information needs of consumers and experts. The ontology is based on metadata from 21 fast food establishment nutritional resources and authored in OWL2 using Protégé. RESULTS: Three evaluators reviewed the logical structure of the ontology through natural language translation of the axioms. While there is majority agreement (76.1% pairwise agreement) of the veracity of the ontology, we identified 103 out of the 430 statements that were erroneous. We revised the ontology and publicably published the initial release of the ontology. The ontology has 413 classes, 21 object properties, 13 data properties, and 494 logical axioms. CONCLUSION: With the initial release of the ontology of fast food facts we discuss some future visions with the continued evolution of this knowledge base, and the challenges we plan to address, like the management and publication of voluminous amount of semantically linked fast food nutritional data.

Patient-Provider Communication Training Models for Interactive Speech Devices.

Patient-provider communication plays a major role in healthcare with its main goal being to improve the patient's health and build a trustworthy relationship between the patient and the doctor. Provider's efficiency and effectiveness in communication can be improved through training in order to meet the essential elements of communication that are relevant during medical encounters. We surmised that speech-enabled conversational agents could be used as a training tool. In this study, we propose designing an ontology-based interaction model that can direct software agents to train dental and medical students. We transformed sample scenario scripts into a formalized ontology training model that links utterances of the user and the machine that expresses patient-provider communication. We created two instance-based models from the ontology to test the operational execution of the model using a prototype software engine. The assessment revealed that the dialogue engine was able to handle about 62% of the dialogue links. Future direction of this work will focus on further enhancing and capturing the features of patient-provider communication, and eventual deployment for pilot testing.

Using Machine Learning-Based Approaches for the Detection and Classification of Human Papillomavirus Vaccine Misinformation: Infodemiology Study of Reddit Discussions.

BACKGROUND: The rapid growth of social media as an information channel has made it possible to quickly spread inaccurate or false vaccine information, thus creating obstacles for vaccine promotion. OBJECTIVE: The aim of this study is to develop and evaluate an intelligent automated protocol for identifying and classifying human papillomavirus (HPV) vaccine misinformation on social media using machine learning (ML)-based methods. METHODS: Reddit posts (from 2007 to 2017, N=28,121) that contained keywords related to HPV vaccination were compiled. A random subset (2200/28,121, 7.82%) was manually labeled for misinformation and served as the gold standard corpus for evaluation. A total of 5 ML-based algorithms, including a support vector machine, logistic regression, extremely randomized trees, a convolutional neural network, and a recurrent neural network designed to identify vaccine misinformation, were evaluated for identification performance. Topic modeling was applied to identify the major categories associated with HPV vaccine misinformation. RESULTS: A convolutional neural network model achieved the highest area under the receiver operating characteristic curve of 0.7943. Of the 28,121 Reddit posts, 7207 (25.63%) were classified as vaccine misinformation, with discussions about general safety issues identified as the leading type of misinformed posts (2666/7207, 36.99%). CONCLUSIONS: ML-based approaches are effective in the identification and classification of HPV vaccine misinformation on Reddit and may be generalizable to other social media platforms. ML-based methods may provide the capacity and utility to meet the challenge involved in intelligent automated monitoring and classification of public health misinformation on social media platforms. The timely identification of vaccine misinformation on the internet is the first step in misinformation correction and vaccine promotion.

"Down the Rabbit Hole" of Vaccine Misinformation on YouTube: Network Exposure Study.

BACKGROUND: Social media platforms such as YouTube are hotbeds for the spread of misinformation about vaccines. OBJECTIVE: The aim of this study was to explore how individuals are exposed to antivaccine misinformation on YouTube based on whether they start their viewing from a keyword-based search or from antivaccine seed videos. METHODS: Four networks of videos based on YouTube recommendations were collected in November 2019. Two search networks were created from provaccine and antivaccine keywords to resemble goal-oriented browsing. Two seed networks were constructed from conspiracy and antivaccine expert seed videos to resemble direct navigation. Video contents and network structures were analyzed using the network exposure model. RESULTS: Viewers are more likely to encounter antivaccine videos through direct navigation starting from an antivaccine video than through goal-oriented browsing. In the two seed networks, provaccine videos, antivaccine videos, and videos containing health misinformation were all found to be more likely to lead to more antivaccine videos. CONCLUSIONS: YouTube has boosted the search rankings of provaccine videos to combat the influence of antivaccine information. However, when viewers are directed to antivaccine videos on YouTube from another site, the recommendation algorithm is still likely to expose them to additional antivaccine information.

Expressing and Executing Informed Consent Permissions Using SWRL: The All of Us Use Case.

The informed consent process is a complicated procedure involving permissions as well a variety of entities and actions. In this paper, we discuss the use of Semantic Web Rule Language (SWRL) to further extend the Informed Consent Ontology (ICO) to allow for semantic machine-based reasoning to manage and generate important permission-based information that can later be viewed by stakeholders. We present four use cases of permissions from the All of Us informed consent document and translate these permissions into SWRL expressions to extend and operationalize ICO. Our efforts show how SWRL is able to infer some of the implicit information based on the defined rules, and demonstrate the utility of ICO through the use of SWRL extensions. Future work will include developing formal and generalized rules and expressing permissions from the entire document, as well as working towards integrating ICO into software systems to enhance the semantic representation of informed consent for biomedical research.

Experimenting with a Prototype Interactive Narrative Game to Improve Knowledge and Beliefs for the HPV Vaccine.

Narratives can have a powerful impact on our health-related beliefs, attitudes, and behaviors. The human papillomavirus (HPV) vaccine can protect against human papillomavirus that leads to different types of cancers. However, HPV vaccination rates are low. This study explored the effectiveness of a narrative-based interactive game about the HPV vaccines as a method to communicate knowledge and perhaps create behavioral outcomes. We developed a serious storytelling game called Vaccination Vacation inspired by personal narratives of individuals who were impacted by the HPV. We tested the game using a randomized control study of 99 adult participants and compared the HPV knowledge and vaccine beliefs of the Gamer Group (who played the game, n = 44) and the Reader group (who read a vaccine information sheet, n = 55). We also evaluated the usability of the game. In addition to high usability, the interactive game slightly impacted the beliefs about the HPV vaccine over standard delivery of vaccine information, especially among those who never received the HPV vaccine. We also observed some gender-based differences in perception towards usability and the likelihood of frequently playing the game. A narrative-based game could bring positive changes to players' HPV-related health beliefs. The combination of more comprehensive HPV vaccine information with the narratives may produce a larger impact. Narrative-based games can be effectively used in other vaccine education interventions and warrant future research.

Dental EHR-infused Persona Ontologies to Enrich Dental Dialogue Interaction of Agents.

The quality of patient-provider communication can predict the healthcare outcomes in patients, and therefore, training dental providers to handle the communication effort with patients is crucial. In our previous work, we developed an ontology model that can standardize and represent patient-provider communication, which can later be integrated in conversational agents as tools for dental communication training. In this study, we embark on enriching our previous model with an ontology of patient personas to portray and express types of dental patient archetypes. The Ontology of Patient Personas that we developed was rooted in terminologies from an OBO Foundry ontology and dental electronic health record data elements. We discuss how this ontology aims to enhance the aforementioned dialogue ontology and future direction in executing our model in software agents to train dental students.

Conversational ontology operator: patient-centric vaccine dialogue management engine for spoken conversational agents.

BACKGROUND: Previously, we introduced our Patient Health Information Dialogue Ontology (PHIDO) that manages the dialogue and contextual information of the session between an agent and a health consumer. In this study, we take the next step and introduce the Conversational Ontology Operator (COO), the software engine harnessing PHIDO. We also developed a question-answering subsystem called Frankenstein Ontology Question-Answering for User-centric Systems (FOQUS) to support the dialogue interaction. METHODS: We tested both the dialogue engine and the question-answering system using application-based competency questions and questions furnished from our previous Wizard of OZ simulation trials. RESULTS: Our results revealed that the dialogue engine is able to perform the core tasks of communicating health information and conversational flow. Inter-rater agreement and accuracy scores among four reviewers indicated perceived, acceptable responses to the questions asked by participants from the simulation studies, yet the composition of the responses was deemed mediocre by our evaluators. CONCLUSIONS: Overall, we present some preliminary evidence of a functioning ontology-based system to manage dialogue and consumer questions. Future plans for this work will involve deploying this system in a speech-enabled agent to assess its usage with potential health consumer users.

Friend of a Friend with Benefits ontology (FOAF+): extending a social network ontology for public health.

BACKGROUND: Dyadic-based social networks analyses have been effective in a variety of behavioral- and health-related research areas. We introduce an ontology-driven approach towards social network analysis through encoding social data and inferring new information from the data. METHODS: The Friend of a Friend (FOAF) ontology is a lightweight social network ontology. We enriched FOAF by deriving social interaction data and relationships from social data to extend its domain scope. RESULTS: Our effort produced Friend of a Friend with Benefits (FOAF+) ontology that aims to support the spectrum of human interaction. A preliminary semiotic evaluation revealed a semantically rich and comprehensive knowledge base to represent complex social network relationships. With Semantic Web Rules Language, we demonstrated FOAF+ potential to infer social network ties between individual data. CONCLUSION: Using logical rules, we defined interpersonal dyadic social connections, which can create inferred linked dyadic social representations of individuals, represent complex behavioral information, help machines interpret some of the concepts and relationships involving human interaction, query network data, and contribute methods for analytical and disease surveillance.