The Experts below are selected from a list of 342 Experts worldwide ranked by ideXlab platform
Marco Gruteser - One of the best experts on this subject based on the ideXlab platform.
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wireless Device Identification with radiometric signatures
ACM IEEE International Conference on Mobile Computing and Networking, 2008Co-Authors: Vladimir Brik, S Banerjee, Marco GruteserAbstract:We design, implement, and evaluate a technique to identify the source network interface card (NIC) of an IEEE 802.11 frame through passive radio-frequency analysis. This technique, called PARADIS, leverages minute imperfections of transmitter hardware that are acquired at manufacture and are present even in otherwise identical NICs. These imperfections are transmitter-specific and manifest themselves as artifacts of the emitted signals. In PARADIS, we measure differentiating artifacts of individual wireless frames in the modulation domain, apply suitable machine-learning classification tools to achieve significantly higher degrees of NIC Identification accuracy than prior best known schemes.We experimentally demonstrate effectiveness of PARADIS in differentiating between more than 130 identical 802.11 NICs with accuracy in excess of 99%. Our results also show that the accuracy of PARADIS is resilient against ambient noise and fluctuations of the wireless channel.Although our implementation deals exclusively with IEEE 802.11, the approach itself is general and will work with any digital modulation scheme.
Yuval Elovici - One of the best experts on this subject based on the ideXlab platform.
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profiliot a machine learning approach for iot Device Identification based on network traffic analysis
Symposium on Applied Computing, 2017Co-Authors: Yair Meidan, Michael Bohadana, Asaf Shabtai, Juan Guarnizo, Martin Ochoa, Nils Ole Tippenhauer, Yuval EloviciAbstract:In this work we apply machine learning algorithms on network traffic data for accurate Identification of IoT Devices connected to a network. To train and evaluate the classifier, we collected and labeled network traffic data from nine distinct IoT Devices, and PCs and smartphones. Using supervised learning, we trained a multi-stage meta classifier; in the first stage, the classifier can distinguish between traffic generated by IoT and non-IoT Devices. In the second stage, each IoT Device is associated a specific IoT Device class. The overall IoT classification accuracy of our model is 99.281+.
Charles M Davis - One of the best experts on this subject based on the ideXlab platform.
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national projections of time cost and failure in implantable Device Identification consideration of unique Device Identification use
Healthcare, 2015Co-Authors: Natalia Wilson, Jennifer Broatch, Megan Jehn, Charles M DavisAbstract:Abstract Background U.S. health care is responding to significant regulation and meaningful incentives for higher quality care, patient safety, electronic documentation and data exchange. FDA’s Unique Device Identification (UDI) Rule, a relatively new regulation aligned with these goals, requires standard labeling of medical Devices by manufacturers. This lays the foundation for UDI scanning and documentation in the electronic health record, expected to change the landscape of medical Device Identification and postmarket surveillance. Methods We developed national projections for time, cost and failure in implant Identification prior to revision total hip and knee arthroplasty (THA/TKA) using American Association of Hip and Knee Surgeons 2012 membership survey data, Nationwide Inpatient Sample 2011 data and THA/TKA demand projection data. Results Our projections suggest that cumulative surgeon time spent identifying failed implants could reach 133,000 h in 2030, representing opportunity to perform over 500,000 15 min established patient office visits. Staff time could reach 220,000 h with a cost of $3.3 m. Failed implants that cannot be identified may be greater than 50,000 preoperatively and 25,000 intraoperatively in 2030. Conclusion Study projections indicate significant time, cost and inability to identify failed implants, supporting need for improvement of implant documentation. FDA’s UDI Rule sets the foundation for UDI scanning and documentation in the electronic health record, a process poised to serve as the standard system for Device documentation.
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revision total hip and knee arthroplasty implant Identification implications for use of unique Device Identification 2012 aahks member survey results
Journal of Arthroplasty, 2014Co-Authors: Natalia Wilson, Megan Jehn, Sally York, Charles M DavisAbstract:FDA's Unique Device Identification (UDI) Rule will mandate manufacturers to assign unique identifiers to their marketed Devices. UDI use is expected to improve implant documentation and Identification. A 2012 American Association of Hip and Knee Surgeons membership survey explored revision total hip and knee arthroplasty implant Identification processes. 87% of surgeons reported regularly using at least 3 methods to identify failed implants pre-operatively. Median surgeon Identification time was 20 min; median staff time was 30 min. 10% of implants could not be identified pre-operatively. 2% could not be identified intra-operatively. UDI in TJA registry and UDI in EMR were indicated practices to best support implant Identification and save time. FDA's UDI rule sets the foundation for UDI use in patient care settings as standard practice for implant documentation.
Joseph P Drozda - One of the best experts on this subject based on the ideXlab platform.
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advancing medical Device innovation through collaboration and coordination of structured data capture pilots report from the medical Device epidemiology network mdepinet specific measurable achievable results oriented time bound smart think tank
Healthcare, 2017Co-Authors: Terrie L Reed, Danica Marinacdabic, Natalia Wilson, Joseph P Drozda, Kevin M Baskin, James E Tcheng, Karen Conway, Theodore Heise, Mitchell W KrucoffAbstract:The Medical Device Epidemiology Network (MDEpiNet) is a public private partnership (PPP) that provides a platform for collaboration on medical Device evaluation and depth of expertise for supporting pilots to capture, exchange and use Device information for improving Device safety and protecting public health. The MDEpiNet SMART Think Tank, held in February, 2013, sought to engage expert stakeholders who were committed to improving the capture of Device data, including Unique Device Identification (UDI), in key electronic health information. Prior to the Think Tank there was limited collaboration among stakeholders beyond a few single health care organizations engaged in electronic capture and exchange of Device data. The Think Tank resulted in what has become two sustainable multi-stakeholder Device data capture initiatives, BUILD and VANGUARD. These initiatives continue to mature within the MDEpiNet PPP structure and are well aligned with the goals outlined in recent FDA-initiated National Medical Device Planning Board and Medical Device Registry Task Force white papers as well as the vision for the National Evaluation System for health Technology.%.
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unique Device identifiers for coronary stent postmarket surveillance and research a report from the food and drug administration medical Device epidemiology network unique Device identifier demonstration
American Heart Journal, 2014Co-Authors: James E Tcheng, Jay Crowley, Terrie L Reed, Madris Tomes, Joseph M Dudas, Kweli P Thompson, Kirk N Garratt, Joseph P DrozdaAbstract:Background Although electronic product Identification in the consumer sector is ubiquitous, unique Identification of medical Devices is just being implemented in 2014. To evaluate unique Device identifiers (UDIs) in health care, the US Food and Drug Administration (FDA) funded the Medical Device Epidemiology Network initiative, including a demonstration of the implementation of coronary stent UDI data in the information systems of a multihospital system (Mercy Health). This report describes the first phase of the demonstration. Methods An expert panel of interventional cardiologists nominated by the American College of Cardiology and the Society for Cardiovascular Angiography and Interventions was convened with representatives of industry, health system members of the Healthcare Transformation Group, the American College of Cardiology National Cardiovascular Data Registry, and FDA to articulate concepts needed to best use UDI-associated data. The expert panel identified 3: (1) use cases for UDI-associated data (eg, research), (2) a supplemental data set of clinically relevant attributes (eg, stent dimensions), and (3) governance and administrative principles for the authoritative management of these data. Results Eighteen use cases were identified, encompassing clinical care, supply chain management, consumer information, research, regulatory, and surveillance domains. In addition to the attributes of the FDA Global Unique Device Identification Database, 9 additional coronary stent-specific attributes were required to address use case requirements. Recommendations regarding governance were elucidated as foundational principles for UDI-associated data management. Conclusions This process for identifying requisite extensions to support the effective use of UDI-associated data should be generalizable. Implementation of a UDI system for medical Devices must anticipate both global and Device-specific information.
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value of unique Device Identification in the digital health infrastructure
JAMA, 2013Co-Authors: Natalia Wilson, Joseph P DrozdaAbstract:In recent years, high-profile cases of medical Device failure resulting in patient harm�such as implantable cardioverter-defibrillator leads and metal-on-metal hip implants�have received substantial attention both in the medical literature and popular press.1- 2 These examples illustrate the need for a more effective system of monitoring Device performance and protecting patient safety.
Natalia Wilson - One of the best experts on this subject based on the ideXlab platform.
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advancing medical Device innovation through collaboration and coordination of structured data capture pilots report from the medical Device epidemiology network mdepinet specific measurable achievable results oriented time bound smart think tank
Healthcare, 2017Co-Authors: Terrie L Reed, Danica Marinacdabic, Natalia Wilson, Joseph P Drozda, Kevin M Baskin, James E Tcheng, Karen Conway, Theodore Heise, Mitchell W KrucoffAbstract:The Medical Device Epidemiology Network (MDEpiNet) is a public private partnership (PPP) that provides a platform for collaboration on medical Device evaluation and depth of expertise for supporting pilots to capture, exchange and use Device information for improving Device safety and protecting public health. The MDEpiNet SMART Think Tank, held in February, 2013, sought to engage expert stakeholders who were committed to improving the capture of Device data, including Unique Device Identification (UDI), in key electronic health information. Prior to the Think Tank there was limited collaboration among stakeholders beyond a few single health care organizations engaged in electronic capture and exchange of Device data. The Think Tank resulted in what has become two sustainable multi-stakeholder Device data capture initiatives, BUILD and VANGUARD. These initiatives continue to mature within the MDEpiNet PPP structure and are well aligned with the goals outlined in recent FDA-initiated National Medical Device Planning Board and Medical Device Registry Task Force white papers as well as the vision for the National Evaluation System for health Technology.%.
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national projections of time cost and failure in implantable Device Identification consideration of unique Device Identification use
Healthcare, 2015Co-Authors: Natalia Wilson, Jennifer Broatch, Megan Jehn, Charles M DavisAbstract:Abstract Background U.S. health care is responding to significant regulation and meaningful incentives for higher quality care, patient safety, electronic documentation and data exchange. FDA’s Unique Device Identification (UDI) Rule, a relatively new regulation aligned with these goals, requires standard labeling of medical Devices by manufacturers. This lays the foundation for UDI scanning and documentation in the electronic health record, expected to change the landscape of medical Device Identification and postmarket surveillance. Methods We developed national projections for time, cost and failure in implant Identification prior to revision total hip and knee arthroplasty (THA/TKA) using American Association of Hip and Knee Surgeons 2012 membership survey data, Nationwide Inpatient Sample 2011 data and THA/TKA demand projection data. Results Our projections suggest that cumulative surgeon time spent identifying failed implants could reach 133,000 h in 2030, representing opportunity to perform over 500,000 15 min established patient office visits. Staff time could reach 220,000 h with a cost of $3.3 m. Failed implants that cannot be identified may be greater than 50,000 preoperatively and 25,000 intraoperatively in 2030. Conclusion Study projections indicate significant time, cost and inability to identify failed implants, supporting need for improvement of implant documentation. FDA’s UDI Rule sets the foundation for UDI scanning and documentation in the electronic health record, a process poised to serve as the standard system for Device documentation.
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revision total hip and knee arthroplasty implant Identification implications for use of unique Device Identification 2012 aahks member survey results
Journal of Arthroplasty, 2014Co-Authors: Natalia Wilson, Megan Jehn, Sally York, Charles M DavisAbstract:FDA's Unique Device Identification (UDI) Rule will mandate manufacturers to assign unique identifiers to their marketed Devices. UDI use is expected to improve implant documentation and Identification. A 2012 American Association of Hip and Knee Surgeons membership survey explored revision total hip and knee arthroplasty implant Identification processes. 87% of surgeons reported regularly using at least 3 methods to identify failed implants pre-operatively. Median surgeon Identification time was 20 min; median staff time was 30 min. 10% of implants could not be identified pre-operatively. 2% could not be identified intra-operatively. UDI in TJA registry and UDI in EMR were indicated practices to best support implant Identification and save time. FDA's UDI rule sets the foundation for UDI use in patient care settings as standard practice for implant documentation.
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value of unique Device Identification in the digital health infrastructure
JAMA, 2013Co-Authors: Natalia Wilson, Joseph P DrozdaAbstract:In recent years, high-profile cases of medical Device failure resulting in patient harm�such as implantable cardioverter-defibrillator leads and metal-on-metal hip implants�have received substantial attention both in the medical literature and popular press.1- 2 These examples illustrate the need for a more effective system of monitoring Device performance and protecting patient safety.