Now showing items 21-40 of 40

    • Knowledge Integration for Disease Characterization: A Breast Cancer Example 

      Seneviratne, Oshani; Rashid, Sabbir; Chari, Shruthi; Bennett, Kristin P.; Hendler, James A.; McGuinness, Deborah L. (2018-07-20)
      With the rapid advancements in cancer research, the information that is useful for characterizing disease, staging tumors, and creating treatment and survivorship plans has been changing at a pace that creates challenges ...
    • 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 ...
    • Making Study Populations Visible through Knowledge Graphs 

      Chari, Shruthi; Qim, Miao; Agu, Nkechinyere; Seneviratne, Oshani; McCusker, Jamie; Bennett, Kristin P.; Das, Amar; McGuinness, Deborah L. (2019-10-12)
    • Ontology-enabled Analysis of Study Populations 

      Chari, Shruthi; Qim, Miao; Agu, Nkechinyere; Seneviratne, Oshani; McCusker, Jamie; Bennett, Kristin P.; Das, Amar; McGuinness, Deborah L. (2019-10-01)
      We address the problem of modeling study populations in research studies in a declarative manner. Research studies often have a great degree of variability in the reporting of population descriptions. To make study populations ...
    • Ontology-enabled Breast Cancer Characterization 

      Seneviratne, Oshani; Rashid, Sabbir; Chari, Shruthi; McCusker, Jamie; Bennett, Kristin P.; Hendler, James A.; McGuinness, Deborah L. (2018-10-01)
      We address the problem of characterizing breast cancer, which today is done using staging guidelines. Our demo will show different breast cancer staging results that leverage the Whyis semantic nanopublication knowledge ...
    • 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 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 ...
    • 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 ...
    • 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 ...
    • Reflections on successful research in artificial intelligence: An introduction 

      Chen, Ching-Hua; Hendler, James A.; Rashid, Sabbir; Seneviratne, Oshani; Sow, Daby; Srivastava, Biplav (Association for the Advancement of Artificial Intelligence, 2019-11-20)
      This editorial introduces the special topic articles on reflections on successful research in artificial intelligence. Consisting of a combination of interviews and full-length articles, the special topic articles examine ...
    • Semantic Graph Analysis to Combat Cryptocurrency Misinformation on the Web 

      Kazenoff, Daniel; Seneviratne, Oshani; McGuinness, Deborah L. (CEUR-WS, 2020)
      With the hype around blockchain technologies, misinformation on ‘get rich quick’ scams are becoming rampant. In this work, we describe a solution that puts in the groundwork to identify fraudulent users and track them ...
    • 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 ...
    • Semantic Modeling of Cohort Descriptions in Research Studies 

      Chari, Shruthi; Weerawarana, Rukmal; Seneviratne, Oshani; McCusker, Jamie; McGuinness, Deborah L.; Das, Amar (2018-10-29)
      Recommendations in ADA’s Standards of Medical Care in Diabetes guideline are supported by findings from scientific publications (primarily clinical trials and case studies). We propose an approach rooted in Information ...
    • 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. ...
    • Semantically enabling clinical decision support recommendations 

      Seneviratne, Oshani; Das, Amar K.; Chari, Shruthi; Agu, Nkechinyere N.; Rashid, Sabbir M.; McCusker, Jamie P.; Franklin, Jade S.; Qi, Miao; Bennett, Kristin P.; Chen, Ching-Hua; Hendler, James A.; McGuinness, Deborah L. (BMC/Springer Nature, 2023-07-18)
      Background Clinical decision support systems have been widely deployed to guide healthcare decisions on patient diagnosis, treatment choices, and patient management through evidence-based recommendations. These recommendations ...
    • 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 ...
    • A Survey on Personal Health Knowledge Graphs 

      Shirai, Sola S.; Seneviratne, Oshani; McGuinness, Deborah L. (Knowledge Graph Conference, 2020)
    • 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)
    • 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)
    • WebSci 21 Companion 

      Seneviratne, Oshani; Singh, V.; Freire, A.; Luo, J. D. (ACM, 2021-06)