Events in clinical narratives are naturally associated with medical trials such as surgery, vaccination, lab test, medication, medical procedure, diagnosis, and they are interrelated with many temporal relations, however it is difficult... more
Events in clinical narratives are naturally associated with medical trials such as surgery, vaccination, lab test, medication, medical procedure, diagnosis, and they are interrelated with many temporal relations, however it is difficult to define these events quantitatively or consistently in coarse time-bins (e.g. before vaccination, after admission). The grouping of medical events onto temporal clusters is a key to applications such as longitudinal studies, clinical question answering, and information retrieval. In this paper, we developed two algorithms based on Min-conflicts and K-means to enable labeling a sequence of medical events with predefined time-bins. The computation is based solely on temporal similarity and integrated with a timeline visualization tool.
A major obstacle impeding progress on the “web of data” is content creation—a difficult, tedious, and timeconsuming task. How do we make human-scalable, user-friendly tools to enable the web of data? Content integrity is also a major... more
A major obstacle impeding progress on the “web of data” is content creation—a difficult, tedious, and timeconsuming task. How do we make human-scalable, user-friendly tools to enable the web of data? Content integrity is also a major concern. How do we engender confidence in results returned from the web of data? Although seemingly unrelated, we show in this paper that it is exactly their relationship that is the key to solving both problems. As we show in this paper, we can semi-automatically derive both data and metadata from data-rich web pages to create a web of data that we then superimpose over these data-rich web pages. We link the web of data to the current web of pages, resulting in a higher-order “web of knowledge.” This web of knowledge provides provenance and thus engenders the confidence necessary to raise the level of the web from “data” to “knowledge.” We focus mainly on two prototype tools we have implemented: (1) TISP—a tool to automatically generate ontologies for ...
To date, there are no effective treatments for most neurodegenerative diseases. However, certain foods may be associated with these diseases and bring an opportunity to prevent or delay neurodegenerative progression. Our objective is to... more
To date, there are no effective treatments for most neurodegenerative diseases. However, certain foods may be associated with these diseases and bring an opportunity to prevent or delay neurodegenerative progression. Our objective is to construct a knowledge graph for neurodegenerative diseases using literature mining to study their relations with diet. We collected biomedical annotations (Disease, Chemical, Gene, Species, SNP&Mutation) in the abstracts from 4,300 publications relevant to both neurodegenerative diseases and diet using PubTator, an NIH-supported tool that can extract biomedical concepts from literature. A knowledge graph was created from these annotations. Graph embeddings were then trained with the node2vec algorithm to support potential concept clustering and similar concept identification. We found several food-related species and chemicals that might come from diet and have an impact on neurodegenerative diseases. 1 Scientific Background Neurodegenerative disease...
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There are huge and growing amounts of biological data that reside in different online repositories. Most of these Web-based sources only focus on some specific areas or only allow limited types of user queries. To obtain needed... more
There are huge and growing amounts of biological data that reside in different online repositories. Most of these Web-based sources only focus on some specific areas or only allow limited types of user queries. To obtain needed information, biologists usually have to traverse different Web sources and combine their data manually. In this research, we propose a system that can help users to overcome these difficulties. Given a user’s query within the the area of molecular biology, our system can automatically discover appropriate repositories, retrieve useful information from these repositories and integrate the retrieved information together.
The human papillomavirus (HPV) vaccine is the most effective way to prevent HPV-related cancers. Integrating provider vaccine counseling is crucial to improving HPV vaccine completion rates. Automating the counseling experience through a... more
The human papillomavirus (HPV) vaccine is the most effective way to prevent HPV-related cancers. Integrating provider vaccine counseling is crucial to improving HPV vaccine completion rates. Automating the counseling experience through a conversational agent could help improve HPV vaccine coverage and reduce the burden of vaccine counseling for providers. In a previous study, we tested a simulated conversational agent that provided HPV vaccine counseling for parents using the Wizard of OZ protocol. In the current study, we assessed the conversational agent among young college adults (n=24), a population that may have missed the HPV vaccine during their adolescence when vaccination is recommended. We also administered surveys for system and voice usability, and for health beliefs concerning the HPV vaccine. Participants perceived the agent to have high usability that is slightly better or equivalent to other voice interactive interfaces, and there is some evidence that the agent impa...
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In this study, we introduce an ontology-driven software engine to provide dialogue interaction functionality for a conversational agent for HPV vaccine counseling. Currently, the HPV vaccination rates are low that risks unprotected... more
In this study, we introduce an ontology-driven software engine to provide dialogue interaction functionality for a conversational agent for HPV vaccine counseling. Currently, the HPV vaccination rates are low that risks unprotected individuals at being infected with HPV, a virus that leads to life-threatening cancers. In addition, we developed a question answering subsystem to support the dialogue engine. In this paper, we discuss our design and development of an ontology-driven dialogue engine that uses the Patient Health Information Dialogue Ontology, an ontology that we previously developed, and a question answering subsystem based on various previous methods to supplement the dialogue engine’s interaction with the user. Our next step is to test the functional ability of the ontology-driven software components and deploy the engine in a live environment to be integrated with a speech interface.
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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... more
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 biomed...
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... more
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.
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... more
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 li...
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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... more
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...
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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... more
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 ...
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With the proliferation of heterogeneous health care data in the last three decades, biomedical ontologies and controlled biomedical terminologies play a more and more important role in knowledge representation and management, data... more
With the proliferation of heterogeneous health care data in the last three decades, biomedical ontologies and controlled biomedical terminologies play a more and more important role in knowledge representation and management, data integration, natural language processing, as well as decision support for health information systems and biomedical research. Biomedical ontologies and controlled terminologies are intended to assure interoperability. Nevertheless, the quality of biomedical ontologies has hindered their applicability and subsequent adoption in real-world applications. Ontology evaluation is an integral part of ontology development and maintenance. In the biomedicine domain, ontology evaluation is often conducted by third parties as a quality assurance (or auditing) effort that focuses on identifying modeling errors and inconsistencies. In this work, we first organized four categorical schemes of ontology evaluation methods in the existing literature to create an integrated...
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Knowledge engineering for ontological knowledgebases is resource and time intensive. To alleviate these issues, especially for novices, automated tools from the natural language domain can assist in the development process of ontologies.... more
Knowledge engineering for ontological knowledgebases is resource and time intensive. To alleviate these issues, especially for novices, automated tools from the natural language domain can assist in the development process of ontologies. We focus towards the development of ontologies for the public health domain and use patient-centric sources from MedlinePlus related to HPV-causing cancers. This paper demonstrates the use of a lightweight open information extraction (OIE) tool to derive accurate knowledge triples that can lead to the seeding of an ontological knowledgebase. We developed a custom application, which interfaced with an information extraction software library, to help facilitate the tasks towards producing knowledge triples from textual sources. The results of our efforts generated accurate extractions ranging from 80-89% precision. These triples can later be transformed to OWL/RDF representation for our planned ontological knowledgebase. OIE delivers an effective and ...
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As one of the serious public health issues, vaccination refusal has been attracting more and more attention, especially for newly approved human papillomavirus (HPV) vaccines. Understanding public opinion towards HPV vaccines, especially... more
As one of the serious public health issues, vaccination refusal has been attracting more and more attention, especially for newly approved human papillomavirus (HPV) vaccines. Understanding public opinion towards HPV vaccines, especially concerns on social media, is of significant importance for HPV vaccination promotion. In this study, we leveraged a hierarchical machine learning based sentiment analysis system to extract public opinions towards HPV vaccines from Twitter. English tweets containing HPV vaccines-related keywords were collected from November 2, 2015 to March 28, 2016. Manual annotation was done to evaluate the performance of the system on the unannotated tweets corpus. Followed time series analysis was applied to this corpus to track the trends of machine-deduced sentiments and their associations with different days of the week. The evaluation of the unannotated tweets corpus showed that the micro-averaging F scores have reached 0.786. The learning system deduced the ...
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Research Interests: Computer Science, Biomedical Engineering, Natural Language Processing, Machine Learning, Data Mining, and 11 moreSemantics, Electronic Health Records, Medicine, TIME, Humans, Data Collection, Model Based Clinical Decision Support Systems, Information Storage and Retrieval, Theoretical Models, Time, and Internet
Analysing public opinions on HPV vaccines on social media using machine learning based approaches will help us understand the reasons behind the low vaccine coverage and come up with corresponding strategies to improve vaccine uptake. To... more
Analysing public opinions on HPV vaccines on social media using machine learning based approaches will help us understand the reasons behind the low vaccine coverage and come up with corresponding strategies to improve vaccine uptake. To propose a machine learning system that is able to extract comprehensive public sentiment on HPV vaccines on Twitter with satisfying performance. We collected and manually annotated 6,000 HPV vaccines related tweets as a gold standard. SVM model was chosen and a hierarchical classification method was proposed and evaluated. Additional feature sets evaluation and model parameters optimization was done to maximize the machine learning model performance. A hierarchical classification scheme that contains 10 categories was built to access public opinions toward HPV vaccines comprehensively. A 6,000 annotated tweets gold corpus with Kappa annotation agreement at 0.851 was created and made public available. The hierarchical classification model with optimi...
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Vaccines have been one of the most successful public health interventions to date. The use of vaccination, however, also comes with possible adverse events. The U.S. FDA/CDC Vaccine Adverse Event Reporting System (VAERS) currently... more
Vaccines have been one of the most successful public health interventions to date. The use of vaccination, however, also comes with possible adverse events. The U.S. FDA/CDC Vaccine Adverse Event Reporting System (VAERS) currently contains more 200,000 reports for post-vaccination events that occur after the administration of vaccines licensed in the United States. Although the data from VAERS has been applied to many public health and vaccine safety studies, each individual report does not necessary indicate a casuality relationship between the vaccine and the reported symptoms. Further statistical analysis and summarization needs to be done before this data can be leveraged. In this paper, we introduces our preliminary work on summarzing the VAERS data and representing the vaccine-symptom correlations as well as the meta data of their relations using RDF. We then apply network analysis approaches to the RDF data to illustrate a use case of the data. We further discuss our vision on integrating the data with vaccine information from other sources using RDF linked approach to faciliate more comprehensive analyses.
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Ontologies are useful in many branches of biomedical research. For instance, in the vaccine domain, the community-based Vaccine Ontology (VO) has been widely used to promote vaccine data standardization, integration, and computer-assisted... more
Ontologies are useful in many branches of biomedical research. For instance, in the vaccine domain, the community-based Vaccine Ontology (VO) has been widely used to promote vaccine data standardization, integration, and computer-assisted reasoning. However, a major challenge in the VO has been to construct ontologies of vaccine functions, given incomplete vaccine knowledge and inconsistencies in how this knowledge is manually curated. In this study, we show that network-based analysis of vaccine-related networks can identify underlying structural information consistent with that captured by the VO, and commonalities in the vaccine adverse events for vaccines and for diseases to produce new hypotheses about pathomechanisms involving the vaccine and the disease status. First, a vaccine-vaccine network was inferred by applying a bipartite network projection strategy to the vaccine-disease network extracted from the Semantic MEDLINE database. In total, 76 vaccines and 573 relationships...
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The semantic interoperability of electronic healthcare records (EHRs) systems is a major challenge in the medical informatics area. International initiatives pursue the use of semantically interoperable clinical models, and ontologies... more
The semantic interoperability of electronic healthcare records (EHRs) systems is a major challenge in the medical informatics area. International initiatives pursue the use of semantically interoperable clinical models, and ontologies have frequently been used in semantic interoperability efforts. The objective of this paper is to propose a generic, ontology-based, flexible approach for supporting the automatic transformation of clinical models, which is illustrated for the transformation of Clinical Element Models (CEMs) into openEHR archetypes. Our transformation method exploits the fact that the information models of the most relevant EHR specifications are available in the Web Ontology Language (OWL). The transformation approach is based on defining mappings between those ontological structures. We propose a way in which CEM entities can be transformed into openEHR by using transformation templates and OWL as common representation formalism. The transformation architecture explo...
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In this paper, we show how we have applied the Clinical Narrative Temporal Relation Ontology (CNTRO) and its associated temporal reasoning system (the CNTRO Timeline Library) to trend temporal information within medical device adverse... more
In this paper, we show how we have applied the Clinical Narrative Temporal Relation Ontology (CNTRO) and its associated temporal reasoning system (the CNTRO Timeline Library) to trend temporal information within medical device adverse event report narratives. 238 narratives documenting occurrences of late stent thrombosis adverse events from the Food and Drug Administration's (FDA) Manufacturing and User Facility Device Experience (MAUDE) database were annotated and evaluated using the CNTRO Timeline Library to identify, order, and calculate the duration of temporal events. The CNTRO Timeline Library had a 95% accuracy in correctly ordering events within the 238 narratives. 41 narratives included an event in which the duration was documented, and the CNTRO Timeline Library had an 80% accuracy in correctly determining these durations. 77 narratives included documentation of a duration between events, and the CNTRO Timeline Library had a 76% accuracy in determining these durations...
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A huge amount of associations among different biological entities (e.g., disease, drug, and gene) are scattered in millions of biomedical articles. Systematic analysis of such heterogeneous data can infer novel associations among... more
A huge amount of associations among different biological entities (e.g., disease, drug, and gene) are scattered in millions of biomedical articles. Systematic analysis of such heterogeneous data can infer novel associations among different biological entities in the context of personalized medicine and translational research. Recently, network-based computational approaches have gained popularity in investigating such heterogeneous data, proposing novel therapeutic targets and deciphering disease mechanisms. However, little effort has been devoted to investigating associations among drugs, diseases, and genes in an integrative manner. We propose a novel network-based computational framework to identify statistically over-expressed subnetwork patterns, called network motifs, in an integrated disease-drug-gene network extracted from Semantic MEDLINE. The framework consists of two steps. The first step is to construct an association network by extracting pair-wise associations between ...
ABSTRACT
Research Interests: Computer Science and RDF
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The generation of a semantic clinical infostructure requires linking ontologies, clinical models and terminologies [1]. Here we describe an approach that would permit data coming from different sources and represented in different... more
The generation of a semantic clinical infostructure requires linking ontologies, clinical models and terminologies [1]. Here we describe an approach that would permit data coming from different sources and represented in different standards to be queried in a homogeneous and integrated way. Our assumption is that data providers should be able to agree and share the meaning of the data they want to exchange and to exploit. We will describe how Clinical Element Model (CEM) and OpenEHR datasets can be jointly exploited in Semantic Web environments.
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Evaluation of premarketing drug safety in clinical trials is often limited, due to the relatively small sample size and short follow-up time. The data collected in the postmarketing spontaneous reporting systems such as Food and Drug... more
Evaluation of premarketing drug safety in clinical trials is often limited, due to the relatively small sample size and short follow-up time. The data collected in the postmarketing spontaneous reporting systems such as Food and Drug Administration Adverse Event Reporting System as well as electronic medical record systems provide crucial information to evaluate postmarketing drug safety. In this article, we assess the strengths and limitations of Food and Drug Administration Adverse Event Reporting System and electronic medical record data in studying the postmarketing pharmacovigilance outcomes for 12 selected antidepressant drugs. In addition, we evaluate the consistency of the results obtained from these two data sources, and provide potential directions for evidence integration.
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Background Semantic web technology has been applied widely in the biomedical informatics field. Large numbers of biomedical datasets are available online in the resource description framework (RDF) format. Semantic relationship mining... more
Background Semantic web technology has been applied widely in the biomedical informatics field. Large numbers of biomedical datasets are available online in the resource description framework (RDF) format. Semantic relationship mining among genes, disorders, and drugs is widely used in, for example, precision medicine and drug repositioning. However, most of the existing studies focused on a single dataset. It is not easy to find the most current relationships among disorder-gene-drug relationships since the relationships are distributed in heterogeneous datasets. How to mine their semantic relationships from different biomedical datasets is an important issue. Methods First, a variety of biomedical datasets were converted into RDF triple data; then, multisource biomedical datasets were integrated into a storage system using a data integration algorithm. Second, nine query patterns among genes, disorders, and drugs from different biomedical datasets were designed. Third, the gene-di...
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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... more
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 t...
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The human papillomavirus (HPV) vaccine protects adolescents and young adults from 9 high-risk HPV virus types that cause 90% of cervical and anal cancers and 70% of oropharyngeal cancers. This study extends our previous research analyzing... more
The human papillomavirus (HPV) vaccine protects adolescents and young adults from 9 high-risk HPV virus types that cause 90% of cervical and anal cancers and 70% of oropharyngeal cancers. This study extends our previous research analyzing online content concerning the HPV vaccination in social media platforms used by young adults, in which we used Pathfinder network scaling and methods of distributional semantics to characterize differences in knowledge organization reflected in consumer- and expert-generated online content. The current study extends this approach to evaluate HPV vaccine perceptions among young adults who populate Reddit, a major social media platform. We derived Pathfinder networks from estimates of semantic relatedness obtained by learning word embeddings from Reddit posts and compared these to networks derived from human expert estimation of the relationship between key concepts. Results revealed that users of Reddit, predominantly comprising young adults in the ...
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Background In the United States and parts of the world, the human papillomavirus vaccine uptake is below the prescribed coverage rate for the population. Some research have noted that dialogue that communicates the risks and benefits, as... more
Background In the United States and parts of the world, the human papillomavirus vaccine uptake is below the prescribed coverage rate for the population. Some research have noted that dialogue that communicates the risks and benefits, as well as patient concerns, can improve the uptake levels. In this paper, we introduce an application ontology for health information dialogue called Patient Health Information Dialogue Ontology for patient-level human papillomavirus vaccine counseling and potentially for any health-related counseling. Results The ontology’s class level hierarchy is segmented into 4 basic levels - Discussion, Goal, Utterance, and Speech Task. The ontology also defines core low-level utterance interaction for communicating human papillomavirus health information. We discuss the design of the ontology and the execution of the utterance interaction. Conclusion With an ontology that represents patient-centric dialogue to communicate health information, we have an applicat...
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Background The safety of vaccines is a critical factor in maintaining public trust in national vaccination programs. This study aimed to evaluate the safety profiles of human papillomavirus (HPV) vaccines with regard to the distribution... more
Background The safety of vaccines is a critical factor in maintaining public trust in national vaccination programs. This study aimed to evaluate the safety profiles of human papillomavirus (HPV) vaccines with regard to the distribution of adverse events (AE) across gender and age, and the correlations across various AEs using the Food and Drug Administration/Centers for Disease Control and Prevention Vaccine Adverse Event Reporting System (VAERS). Methods For analyses, 27,348 patients aged between 9 and 25 years old with at least one AE reported in VAERS between the year of 2006 and 2017 were included. AEs were summarized into two levels: the lower level preferred term (PT) and higher level system organ classes (SOCs) based on the structure of Medical Dictionary for Regulatory Activities (MedDRA). A series of statistical analyses were applied on both levels of AEs. Zero-truncated Poisson regression and multivariate logistic regression models were first developed to assess the rate ...
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The Research Domain Criteria, launched by the National Institute of Mental Health, is a new dimensional and interdisciplinary research framework for mental disorders. The Research Domain Criteria matrix is its core part. Since an ontology... more
The Research Domain Criteria, launched by the National Institute of Mental Health, is a new dimensional and interdisciplinary research framework for mental disorders. The Research Domain Criteria matrix is its core part. Since an ontology has the strengths of supporting semantic inferencing and automatic data processing, we would like to transform the Research Domain Criteria matrix into an ontological structure. In terms of data normalization, which is the essential part of an ontology representation, the Research Domain Criteria elements (mainly in the Units of Analysis) have some limitations. In this article, we propose a series of solutions to improve data normalization of the Research Domain Criteria elements in the Units of Analysis, including leveraging standard terminologies (i.e. the Unified Medical Language System Metathesaurus), context-combining queries, and domain expertise. The evaluation results show the positive (Yes) percentage is more than 80 percent, indicating ou...
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Healthcare services, particularly in patient-provider interaction, often involve highly emotional situations, and it is important for physicians to understand and respond to their patients' emotions to best ensure their well-being. In... more
Healthcare services, particularly in patient-provider interaction, often involve highly emotional situations, and it is important for physicians to understand and respond to their patients' emotions to best ensure their well-being. In order to model the emotion domain, we have created the Visualized Emotion Ontology (VEO) to provide a semantic definition of 25 emotions based on established models, as well as visual representations of emotions utilizing shapes, lines, and colors. As determined by ontology evaluation metrics, VEO exhibited better machine-readability (z=1.12), linguistic quality (z=0.61), and domain coverage (z=0.39) compared to a sample of cognitive ontologies. Additionally, a survey of 1082 participants through Amazon Mechanical Turk revealed that a significantly higher proportion of people agree than disagree with 17 out of our 25 emotion images, validating the majority of our visualizations. From the development, evaluation, and serialization of the VEO, we hav...
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The emergence of the deep convolutional neural network (CNN) greatly improves the quality of computer-aided supporting systems. However, due to the challenges of generating reliable and timely results, clinical adoption of computer-aided... more
The emergence of the deep convolutional neural network (CNN) greatly improves the quality of computer-aided supporting systems. However, due to the challenges of generating reliable and timely results, clinical adoption of computer-aided diagnosis systems is still limited. Recent informatics research indicates that machine learning algorithms need to be combined with sufficient clinical expertise in order to achieve an optimal result. In this research, we used deep learning algorithms to help diagnose four common cutaneous diseases based on dermoscopic images. In order to facilitate decision-making and improve the accuracy of our algorithm, we summarized classification/diagnosis scenarios based on domain expert knowledge and semantically represented them in a hierarchical structure. Our algorithm achieved an accuracy of 87.25 ± 2.24% in our test dataset with 1067 images. The semantic summarization of diagnosis scenarios can help further improve the algorithm to facilitate future com...
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Healthcare is going through a big data revolution. The amount of data generated by healthcare is expected to increase significantly in the coming years. Therefore, efficient and effective data processing methods are required to transform... more
Healthcare is going through a big data revolution. The amount of data generated by healthcare is expected to increase significantly in the coming years. Therefore, efficient and effective data processing methods are required to transform data into information. In addition, applying statistical analysis can transform the information into useful knowledge. We developed a data mining method that can uncover new knowledge in this enormous field for clinical decision making while generating scientific methods and hypotheses. The proposed pipeline can be generally applied to a variety of data mining tasks in medical informatics. For this study, we applied the proposed pipeline for post-marketing surveillance on drug safety using FAERS, the data warehouse created by FDA. We used 14 kinds of neurology drugs to illustrate our methods. Our result indicated that this approach can successfully reveal insight for further drug safety evaluation.
Data from the Vaccine Adverse Event Reporting System (VAERS) contain spontaneously reported adverse events (AEs) from the public. It has been a major data source for detecting AEs and monitoring vaccine safety. As one major limitation of... more
Data from the Vaccine Adverse Event Reporting System (VAERS) contain spontaneously reported adverse events (AEs) from the public. It has been a major data source for detecting AEs and monitoring vaccine safety. As one major limitation of spontaneous surveillance systems, the VAERS reports by themselves sometimes do not provide enough information to answer certain research questions. For example, patient level demographics are very limited in VAERS due to the protection of patient privacy, such that investigation of differential AE rates across race/ethnicity groups cannot be conducted using VAERS data only. For many vaccines, racial and ethnical difference in immune responses has been found in studies based on racially diverse cohorts. It is of great interest to characterize the differential AE rates by race and ethnicity groups for vaccines. In this study, we propose a novel statistical method to integrate VAERS data with data from other resources for vaccine pharmacovigilance rese...