Now showing items 1-20 of 40

    • Semantic Technologies for Clinically Relevant Personal Health Applications 

      Chen, Ching-Hau; Gruen, Daniel; Harris, Jonathan; Hendler, James A.; McGuinness, Deborah L.; Monti, Marco; Rastogi, Nidhi; Seneviratne, Oshani; Zaki, Mohammed J (Springer, 2022-11-23)
      Despite recent advances in digital health solutions and machine learning, personal health applications that aim to modify health behaviors are still limited in their ability to offer more personalized decision support. ...
    • DeFi Survival Analysis: Insights into Risks and User Behavior 

      Green, Aaron; Cammilleri, Christopher; Erickson, John S.; Seneviratne, Oshani; Bennett, Kristin (Springer, 2022-06)
      We propose a decentralized finance (DeFi) survival analysis approach for discovering and characterizing user behavior and risks in lending protocols. We demonstrate how to gather and prepare DeFi transaction data for ...
    • Understanding Clinician Workflows to Design AI Risk Prediction Models 

      Zhang, O.; Chari, Shruthi; Saiz, F. S.; Gruen, Daniel; Acharya, P.; Seneviratne, Oshani; Meter, P.; McGuinness, Deborah L.; Chakraborty, P. (AMIA, 2022-04)
    • Incentivized Research Data Sharing, Reusing, and Repurposing with Blockchain Technologies 

      Seneviratne, Oshani; McGuinness, Deborah L. (University of Hawaii at Manoa, 2022)
      Data sharing is very important for accelerating scientific research, business innovations, and for informing individuals. Yet, concerns over data privacy, cost, and lack of secure data-sharing solutions have prevented data ...
    • Towards Clinically Relevant Explanations for Type-2 Diabetes Risk Prediction with the Explanation Ontology 

      Chari, Shruthi; Chakraborty, Prithwish; Seneviratne, Oshani; Ghalwash, Mohamed; Gruen, Daniel M.; Sow, Daby; McGuinness, Deborah L. (AMIA, 2021-11)
    • Enhancing Clinical Relevance of Health Behavior Insights via Semantics 

      Harris, Jonathan; McGuinness, Deborah L.; Monti, Marco; Seneviratne, Oshani; Zaki, Mohammed J.; Chen, Ching-Hua (AMIA, 2021-11)
    • Personal Health Knowledge Graph for Clinically Relevant Diet Recommendations 

      Seneviratne, Oshani; Harris, Jonathan; Chen, Ching-Hua; McGuinness, Deborah L. (AKBC, 2021-10)
      We propose a knowledge model for capturing dietary preferences and personal context to provide personalized dietary recommendations. We develop a knowledge model called the Personal Health Ontology, which is grounded in ...
    • The Punya Platform: Building Mobile Research Apps with Linked Data and Semantic Features 

      Patton, E.W.; Van Woensel, W.; Seneviratne, Oshani; Loseto, G.; Scioscia, F.; Kagal, L. (Springer, Cham, 2021-10)
      Modern smartphones offer advanced sensing, connectivity, and processing capabilities for data acquisition, processing, and generation: but it can be difficult and costly to develop mobile research apps that leverage these ...
    • BlockIoT: Blockchain-based Health Data Integration using IoT Devices 

      Shukla, Manan; Lin, Jianjing; Seneviratne, Oshani (arXiv, 2021-10)
      The development and adoption of Electronic Health Records (EHR) and health monitoring Internet of Things (IoT) Devices have enabled digitization of patient records and has also substantially transformed the healthcare ...
    • Leveraging Clinical Context for User-Centered Explainability: A Diabetes Use Case 

      Chari, Shruthi; Chakraborty, Prithwish; Ghalwash, Mohamed; Seneviratne, Oshani; Eyigöz, Elif; Gruen, Daniel; Suarez Saiz, Fernando; Chen, Ching Hua; Meyer Rojas, Pablo; McGuinness, Deborah L. (CoRR, 2021-07-01)
      Academic advances of AI models in high-precision domains, like healthcare, need to be made explainable in order to enhance real-world adoption. Our past studies and ongoing interactions indicate that medical experts can ...
    • WebSci 21 Companion 

      Seneviratne, Oshani; Singh, V.; Freire, A.; Luo, J. D. (ACM, 2021-06)
    • The Personal Health Library: Taking EHR design to the Next Level 

      Ammar, Nariman; Seneviratne, Oshani; Bailey, James; Davis, Robert; McGuinness, Deborah L.; Shaban-Nejad, Arash (The Knowledge Graph Conference, 2021-05)
      Technology maturation in EHR design has evolved over the years. Formal data semantics and rich knowledge encoded in ontologies lead to new era of personalized precision medicine. Also, Web technologies have enabled EHRs ...
    • Applying Personal Knowledge Graphs to Health 

      Shirai, Sola; Seneviratne, Oshani; McGuinness, Deborah L. (arXiv, 2021-04)
      Knowledge graphs that encapsulate personal health information, or personal health knowledge graphs (PHKG), can help enable personalized health care in knowledge-driven systems. In this paper we provide a short survey of ...
    • Semantic Modeling for Food Recommendation Explanations 

      Padhiar, I.; Seneviratne, Oshani; Chari, Shruthi; Gruen, Daniel M.; McGuinness, Deborah L. (IEEE, 2021-03)
      With the increased use of AI methods to provide recommendations in the health, specifically dietary recommendation space, there is also an increased need for explainability of those recommendations. Such explanations would ...
    • Designing for AI Explainability in Clinical Context 

      Gruen, Daniel M.; Chari, Shruthi; Foreman, Morgan A.; Seneviratne, Oshani; Richesson, Rachel; Das, Amar K.; McGuinness, Deborah L. (AAAI, 2021-02)
      The growing use of artificial intelligence in medical settings has led to increased interest in AI Explainability (XAI). While research on XAI has largely focused on the goal of increasing users' appropriate trust and ...
    • Ingredient Substitutions Using a Knowledge Graph of Food 

      Shirai, Sola; Seneviratne, Oshani; Gordon, Minor; Chen, Ching Hua; McGuinness, Deborah L. (2021-01-25)
      People can affect change in their eating patterns by substituting ingredients in recipes. Such substitutions may be motivated by specific goals, like modifying the intake of a specific nutrient or avoiding a particular ...
    • Identifying Ingredient Substitutions Using a Knowledge Graph of Food 

      Shirai, Sola S.; Seneviratne, Oshani; Gordon, Minor E.; Chen, Ching-Hua; McGuinness, Deborah L. (Frontiers Media S.A., 2021-01-25)
      People can affect change in their eating patterns by substituting ingredients in recipes. Such substitutions may be motivated by specific goals, like modifying the intake of a specific nutrient or avoiding a particular ...
    • Provenance in Data Science: From Data Models to Context-Aware Knowledge Graphs 

      McGuinness, Deborah L.; Seneviratne, Oshani; Sikos, Leslie (Springer, Cham, 2021)
      Presents a collection of provenance techniques and state-of-the-art metadata-enhanced, provenance-aware, knowledge graph-based representations to be used for information processing, management, aggregation, fusion, and ...
    • Explanation Ontology: A Model of Explanations for User-Centered AI 

      Chari, Shruthi; Seneviratne, Oshani; Gruen, Daniel; Foreman, Morgan; Das, Amar; McGuinness, Deborah L. (2020-11-01)
      Explainability has been a goal for Artificial Intelligence (AI) systems since their conception, with the need for explainability growing as more complex AI models are increasingly used in critical, high-stakes settings ...
    • Semantics-Driven Ingredient Substitution in the FoodKG 

      Shirai, Sola; Seneviratne, Oshani; Gordon, Minor; Chen, Ching Hua; McGuinness, Deborah L. (2020-11-01)
      The proliferation of recipes and other food information on the Web presents an opportunity for discovering and organizing diet-related knowledge into a knowledge graph. Currently, there are several ontologies related to ...