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Addendum of: Fall Detection in Individuals With Lower Limb Amputations Using Mobile Phones: Machine Learning Enhances Robustness for Real-World Applications

Addendum of: Fall Detection in Individuals With Lower Limb Amputations Using Mobile Phones: Machine Learning Enhances Robustness for Real-World Applications

The authors would also like to thank Dr Saninder Kaur, Kelsey Greenoe, Ashley Adamczyk, and Ryan Griesenauer for their assistance during participant recruitment and data collection.

Nicholas Shawen, Luca Lonini, Chaithanya Krishna Mummidisetty, Ilona Shparii, Mark V Albert, Konrad Kording, Arun Jayaraman

JMIR Mhealth Uhealth 2017;5(12):e167

Telemedicine in Neonatal Home Care: Identifying Parental Needs Through Participatory Design

Telemedicine in Neonatal Home Care: Identifying Parental Needs Through Participatory Design

Phase 2), addressing the needs identified in this study, and to implement that solution (Phase 3), which will be the topic of future papers.Setting and ParticipantsParents with preterm infants admitted to two NICUs (see Table 1)—Hvidovre Hospital (HH) and Hans

Kristina Garne, Anne Brødsgaard, Gitte Zachariassen, Jane Clemensen

JMIR Res Protoc 2016;5(3):e100

Quality Management, Certification and Rating of Health Information on the Net with MedCERTAIN:  Using a medPICS/RDF/XML metadata structure for implementing eHealth ethics and creating trust globally

Quality Management, Certification and Rating of Health Information on the Net with MedCERTAIN: Using a medPICS/RDF/XML metadata structure for implementing eHealth ethics and creating trust globally

., Germany), Hans-Joachim Koubenec (Stiftung Warentest, Germany), Michel Labrecque (Université Laval Québec, Canada, Canada), Kristian Lampe (Finnish Office for Health Technology Assessment / MedCERTAIN, Finland), Stephane Lejeune (Swiss Cancer League, Switzerland

Gunther Eysenbach, Gabriel Yihune, Kristian Lampe, Phil Cross, Dan Brickley

J Med Internet Res 2000;2(suppl2):e1

Evaluating Identity Disclosure Risk in Fully Synthetic Health Data: Model Development and Validation

Evaluating Identity Disclosure Risk in Fully Synthetic Health Data: Model Development and Validation

The former is referred to as a sample-to-population match, and the latter as a population-to-sample match.In our hypothetical example, an adversary may know Hans in the population and can match that with the European record in the synthetic sample through the

Khaled El Emam, Lucy Mosquera, Jason Bass

J Med Internet Res 2020;22(11):e23139