Browsing Tetherless World Publications by Author "Seneviratne, Oshani"

Now showing items 1-20 of 40

    • 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 ...

    • Automating Population Health Studies through Semantics and Statistics Semantic Statistics (SemStats) 

      New, Alexander; Qi, Miao; Chari, Shruthi; Rashid, Sabbir; Seneviratne, Oshani; McCusker, Jamie; Erickson, John S.; McGuinness, Deborah L.; Bennett, Kristin P. (Springer, 2019-10)
      With the rapid development of the Semantic Web, machines are able to understand the contextual meaning of data, including in the field of automated semantics-driven statistical reasoning. This paper introduces a ...

    • 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 ...

    • Characterizing Common Quarterly Behaviors In DeFi Lending Protocols 

      Green, Aaron; Giannattasio, Michael; Wang, Keran; Erickson, John S.; Seneviratne, Oshani; Bennett, Kristin P. (Springer, Cham., 2023-07)
      The emerging decentralized financial ecosystem (DeFi) is comprised of numerous protocols, one type being lending protocols. People make transactions in lending protocols, each of which is attributed to a specific blockchain ...

    • DeFi Survival Analysis: Insights into Risks and User Behavior 

      Green, Aaron; Cammilleri, Christopher; Erickson, John S.; Seneviratne, Oshani; Bennett, Kristin P. (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 ...

    • 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 ...

    • Directions for Explainable Knowledge-Enabled Systems 

      Chari, Shruthi; Gruen, Daniel M.; Seneviratne, Oshani; McGuinness, Deborah L. (IOS Press, 2020)
      Interest in the field of Explainable Artificial Intelligence has been growing for decades and has accelerated recently. As Artificial Intelligence models have become more complex, and often more opaque, with the incorporation ...

    • Enabling Trust in Clinical Decision Support Recommendations through Semantics 

      Seneviratne, Oshani; Das, Amar K.; Chari, Shruthi; Agu, Nkechinyere; Rashid, Sabbir; Chen, Ching-Hua; McCusker, Jamie; Hendler, James A.; McGuinness, Deborah L. (CEUR Workshop Proceedings (CEUR-WS.org), 2019)
      In an ideal world, the evidence presented in a clinical guideline would cover all aspects of patient care and would apply to all types of patients; however, in practice, this rarely is the case. Existing medical decision ...

    • 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)

    • Explanation Ontology in Action: A Clinical Use-Case 

      Chari, Shruthi; Seneviratne, Oshani; Gruen, Daniel; Foreman, Morgan; Das, Amar; McGuinness, Deborah L. (2020-11-01)
      We addressed the problem of a lack of semantic representation for user-centric explanations and different explanation types in our Explanation Ontology (https://purl.org/heals/eo). Such a representation is increasingly ...

    • 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 ...

    • FoodKG Enabled Questions and Answers Application 

      Haussmann, Steven; Chen, Yu; Seneviratne, Oshani; Rastogi, Nidhi; Codella, James; Chen, Ching Hua; McGuinness, Deborah L.; Zaki, Mohammed (2019-10-01)
      We demonstrate the usage of our FoodKG [3], a food knowledge graph designed to assist in food recommendation. This resource, which brings together recipes, nutrition, food taxonomies, and links into existing ontologies, ...

    • FoodKG: A Semantics-Driven Knowledge Graph for Food Recommendation 

      Haussmann, Steven; Seneviratne, Oshani; Chen, Yu; Ne'eman, Yarden; Codella, James; Chen, Ching Hua; McGuinness, Deborah L.; Zaki, Mohammed (2019-10-01)
      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 ...

    • Foundations of Explainable Knowledge-Enabled Systems 

      Chari, Shruthi; Seneviratne, Oshani; Gruen, Daniel M.; McGuinness, Deborah L. (IOS Press, 2020-04)
      Explainability has been an important goal since the early days of Artificial Intelligence. Several approaches for producing explanations have been developed. However, many of these approaches were tightly coupled with the ...

    • 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 ...

    • Improving Identified Comorbidities using Semantically Annotated Disease Graph 

      Agu, Nkechinyere; Seneviratne, Oshani; McGuinness, Deborah L. (AMIA, 2018-11-08)

    • 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 ...

    • Informing clinical assessment by contextualizing post-hoc explanations of risk prediction models in type-2 diabetes 

      Chari, Shruthi; Acharya, Prasant; Gruen, Daniel M.; Zhang, Olivia; Eyigoz, Elif K.; Ghalwash, Mohamed; Seneviratne, Oshani; Saiz, Fernando Suarez; Meyer, Pablo; Chakraborty, Prithwish; McGuinness, Deborah L. (Elsevier, 2023-02)
      Medical experts may use Artificial Intelligence (AI) systems with greater trust if these are supported by ‘contextual explanations’ that let the practitioner connect system inferences to their context of use. However, their ...

    • 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 ...

    • Knowledge Extraction of Cohort Characteristics in Research Publications 

      Franklin, Jade; Chari, Shruthi; Foreman, Morgan A.; Seneviratne, Oshani; Gruen, Daniel M.; McCusker, Jamie; Das, Amar K.; McGuinness, Deborah L. (AMIA, 2020)
      When healthcare providers review the results of a clinical trial study to understand its applicability to their practice, they typically analyze how well the characteristics of the study cohort correspond to those of the ...