Browsing by Author "Rubin, Daniel L."

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    The LOINC RSNA radiology playbook - a unified terminology for radiology procedures
    (Oxford Academic, 2018-07-01) Vreeman, Daniel J.; Abhyankar, Swapna; Wang, Kenneth C.; Carr, Christopher; Collins, Beverly; Rubin, Daniel L.; Langlotz, Curtis P.; Medicine, School of Medicine
    Objective: This paper describes the unified LOINC/RSNA Radiology Playbook and the process by which it was produced. Methods: The Regenstrief Institute and the Radiological Society of North America (RSNA) developed a unification plan consisting of six objectives 1) develop a unified model for radiology procedure names that represents the attributes with an extensible set of values, 2) transform existing LOINC procedure codes into the unified model representation, 3) create a mapping between all the attribute values used in the unified model as coded in LOINC (ie, LOINC Parts) and their equivalent concepts in RadLex, 4) create a mapping between the existing procedure codes in the RadLex Core Playbook and the corresponding codes in LOINC, 5) develop a single integrated governance process for managing the unified terminology, and 6) publicly distribute the terminology artifacts. Results: We developed a unified model and instantiated it in a new LOINC release artifact that contains the LOINC codes and display name (ie LONG_COMMON_NAME) for each procedure, mappings between LOINC and the RSNA Playbook at the procedure code level, and connections between procedure terms and their attribute values that are expressed as LOINC Parts and RadLex IDs. We transformed all the existing LOINC content into the new model and publicly distributed it in standard releases. The organizations have also developed a joint governance process for ongoing maintenance of the terminology. Conclusions: The LOINC/RSNA Radiology Playbook provides a universal terminology standard for radiology orders and results.
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    Privacy preservation for federated learning in health care
    (Elsevier, 2024-07-12) Pati, Sarthak; Kumar, Sourav; Varma, Amokh; Edwards, Brandon; Lu, Charles; Qu, Liangqiong; Wang, Justin J.; Lakshminarayanan, Anantharaman; Wang, Shih-han; Sheller, Micah J.; Chang, Ken; Singh, Praveer; Rubin, Daniel L.; Kalpathy-Cramer, Jayashree; Bakas, Spyridon; Pathology and Laboratory Medicine, School of Medicine
    Artificial intelligence (AI) shows potential to improve health care by leveraging data to build models that can inform clinical workflows. However, access to large quantities of diverse data is needed to develop robust generalizable models. Data sharing across institutions is not always feasible due to legal, security, and privacy concerns. Federated learning (FL) allows for multi-institutional training of AI models, obviating data sharing, albeit with different security and privacy concerns. Specifically, insights exchanged during FL can leak information about institutional data. In addition, FL can introduce issues when there is limited trust among the entities performing the compute. With the growing adoption of FL in health care, it is imperative to elucidate the potential risks. We thus summarize privacy-preserving FL literature in this work with special regard to health care. We draw attention to threats and review mitigation approaches. We anticipate this review to become a health-care researcher's guide to security and privacy in FL.