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Prospective Real-World Performance Evaluation of a Machine Learning Algorithm to Predict 30-Day Readmissions in Patients with Heart Failure Using Electronic Medical Record Data

Prospective Real-World Performance Evaluation of a Machine Learning Algorithm to Predict 30-Day Readmissions in Patients with Heart Failure Using Electronic Medical Record Data

StatesCorresponding Author: Neda Derakhshani snderakhshani@partners.orgJul-Dec20181709201842e118979820182982018©Sujay S Kakarmath, Neda Derakhshani, Sara B Golas, Jennifer Felsted, Takuma Shibahara, Hideo Aoki, Mika Takata, Ken Naono, Joseph Kvedar, Kamal Jethwani, Stephen

Sujay S Kakarmath, Neda Derakhshani, Sara B. Golas, Jennifer Felsted, Takuma Shibahara, Hideo Aoki, Mika Takata, Ken Naono, Joseph Kvedar, Kamal Jethwani, Stephen Agboola

iproc 2018;4(2):e11897

Computerized Adaptive Testing Provides Reliable and Efficient Depression Measurement Using the CES-D Scale

Computerized Adaptive Testing Provides Reliable and Efficient Depression Measurement Using the CES-D Scale

Figure 2 shows the number of items administered by the CES-D CAT as a function of the standardized score of the depressive symptoms construct.Estimates of the level of CES-D trait score provided by the simulated CAT algorithm and the original CES-D trait score

Bao Sheng Loe, David Stillwell, Chris Gibbons

J Med Internet Res 2017;19(9):e302

Authorship Correction: Scalable Passive Sleep Monitoring Using Mobile Phones: Opportunities and Obstacles

Authorship Correction: Scalable Passive Sleep Monitoring Using Mobile Phones: Opportunities and Obstacles

The authors of the paper entitled “Scalable Passive Sleep Monitoring Using Mobile Phones: Opportunities and Obstacles” [J Med Internet Res 2017;19(4):e118] inadvertently omitted Stephen M Schueller, PhD (Center for Behavioral Intervention Technologies, Department

Sohrab Saeb, Thaddeus R Cybulski, Stephen M Schueller, Konrad P Kording, David C Mohr

J Med Internet Res 2017;19(4):e143

Provider and Patient-Related Barriers to and Facilitators of Digital Health Adoption for Hypertension Management: Review

Provider and Patient-Related Barriers to and Facilitators of Digital Health Adoption for Hypertension Management: Review

States2Harvard Medical SchoolBoston, MAUnited States3American Medical AssociationChicago, MAUnited StatesCorresponding Author: Ramya Palacholla RPALACHOLLA@MGH.HARVARD.EDUJul-Dec20181709201842e119049820182982018©Ramya Palacholla, Nils Fischer, Amanda Coleman, Stephen

Ramya Palacholla, Nils Fischer, Amanda Coleman, Stephen Agboola, Jennifer Felsted, Kate Kirley, Chelsea Katz, Stacy Lloyd, Kamal Jethwani

iproc 2018;4(2):e11904

Participant Engagement with a Hyper-Personalized Activity Tracking Smartphone App

Participant Engagement with a Hyper-Personalized Activity Tracking Smartphone App

States2Harvard Medical SchoolBoston, MAUnited States3Massachusetts General HospitalBoston, MAUnited StatesCorresponding Author: Amanda Centi acenti@partners.orgJul-Dec20181709201842e118768820182982018©Amanda Centi, Ramya Palacholla, Sara Golas, Odeta Dyrmishi, Stephen

Amanda Centi, Ramya Palacholla, Sara Golas, Odeta Dyrmishi, Stephen Agboola, Kamal Jethwani, Joseph Kvedar

iproc 2018;4(2):e11876