Now showing items 1-20 of 22

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

      New, Alexander; Qi, Miao; Chari, Shruthi; Rashid, Sabbir M.; 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 ...
    • 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 ...
    • 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 ...
    • 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 ...
    • G-PROV: A Provenance Encoding Structure for Guideline Evidence 

      Stephen, Shirly; Seneviratne, Oshani; McGuinness, Deborah L.; Chari, Shruthi; Das, Amar (AMIA, 2018-11-03)
    • G-PROV: Provenance Management for Clinical Practice Guidelines 

      Agu, Nkechinyere; Keshan, Neha; Chari, Shruthi; Seneveratne, Oshani; Rashid, Sabbir M.; Das, Amar K.; McCusker, Jamie; McGuinness, Deborah L. (CEUR-WS, 2019-10)
      Providing provenance of treatment suggestions made by clinical decision support systems can enhance transparency and trust in these systems by healthcare practitioners. Provenance can aid in resolving ambiguity and conflicts ...
    • G-Prov: Provenance management for clinical practice guidelines 

      Agu, Nkechinyere; Keshan, Neha; Chari, Shruthi; Seneviratne, Oshani; Rashid, Sabbir; Das, Amar; McCusker, Jamie; McGuinness, Deborah L. (CEUR Workshop Proceedings, 2019-10-27)
      Providing provenance of treatment suggestions made by clinical decision support systems can enhance transparency and trust in these systems by healthcare practitioners. Provenance can aid in resolving ambiguity and conflicts ...
    • 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 ...
    • Knowledge Integration for Disease Characterization: A Breast Cancer Example 

      Seneviratne, Oshani; Rashid, Sabbir; Chari, Shruthi; Bennett, Kristin; 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 ...
    • A Linked Data Representation for Summary Statistics and Grouping Criteria 

      McCusker, Jamie; Dumontier, Michel; Chari, Shruthi; McGuinness, Deborah L. (CEUR-WS, 2019)
      . Summary statistics are fundamental to data science, and are the buidling blocks of statistical reasoning. Most of the data and statistics made available on government web sites are aggregate, however, until now, we ...
    • Making Study Populations Visible through Knowledge Graphs 

      Chari, Shruthi; Qim, Miao; Agu, Nkechinyere; Seneviratne, Oshani; McCusker, Jamie; Bennett, Kristin; 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; 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; 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 ...
    • 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 ...
    • Semantically-targeted analytics for reproducible scientific discovery 

      New, Alexander; Chari, Shruthi; Qim, Miao; Rashid, Sabbir; Erickson, John S.; McGuinness, Deborah L.; Bennett, Kristin (2019-05-13)
      We develop a semantics-driven, automated approach for dynamically performing rigorous scientific studies. This framework may be applied to a wide variety of data and study types; here, we demonstrate its suitability for ...