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Value and Acceptability of a Novel Machine Learning Technology for Heart Failure Readmission Reduction: Qualitative Analysis of Clinical Roles and Workflows

Value and Acceptability of a Novel Machine Learning Technology for Heart Failure Readmission Reduction: Qualitative Analysis of Clinical Roles and Workflows

, MAUnited States3Harvard Medical SchoolBoston, MAUnited StatesCorresponding Author: Sunetra Bane sunetrabane@gmail.comJul-Dec20181709201842e118959820182982018©Simone Orlowski, Sunetra Bane, Jaclyn Hirschey, Sujay Kakarmath, Jennifer Felsted, Julie Brown

Simone Orlowski, Sunetra Bane, Jaclyn Hirschey, Sujay Kakarmath, Jennifer Felsted, Julie Brown, Stephen Agboola, Kamal Jethwani

iproc 2018;4(2):e11895

Clinical Relation Extraction Toward Drug Safety Surveillance Using Electronic Health Record Narratives: Classical Learning Versus Deep Learning

Clinical Relation Extraction Toward Drug Safety Surveillance Using Electronic Health Record Narratives: Classical Learning Versus Deep Learning

In particular, we used the Brown clustering model and Word Vector Classes as word clustering features and applied raw word embedding as word vector features.We trained the Brown cluster model [56] on a large collection of biomedical text.

Tsendsuren Munkhdalai, Feifan Liu, Hong Yu

JMIR Public Health Surveill 2018;4(2):e29

YouTube Video Comments on Healthy Eating: Descriptive and Predictive Analysis

YouTube Video Comments on Healthy Eating: Descriptive and Predictive Analysis

One study investigated co-commenting behavior on K-pop videos by analyzing the weighted frequency and weighted sentiment scores of the co-comments.

Shasha Teng, Kok Wei Khong, Saeed Pahlevan Sharif, Amr Ahmed

JMIR Public Health Surveill 2020;6(4):e19618

A New Tool for Nutrition App Quality Evaluation (AQEL): Development, Validation, and Reliability Testing

A New Tool for Nutrition App Quality Evaluation (AQEL): Development, Validation, and Reliability Testing

Spearman-Brown coefficient was used to test split-half reliability. For construct reliability only, items not on a 5-point scale were adjusted to a 5-point scale. These analyses were conducted using the first occasion apps were evaluated.

Kristen Nicole DiFilippo, Wenhao Huang, Karen M. Chapman-Novakofski

JMIR Mhealth Uhealth 2017;5(10):e163

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

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

Computer-Controlled Virtual Humans in Patient-Facing Systems: Systematic Review and Meta-Analysis

Computer-Controlled Virtual Humans in Patient-Facing Systems: Systematic Review and Meta-Analysis

There were more cross-sectional (k=15) [54,68,81,83,87,88,91,92,95] than longitudinal studies (k=11) [58,63,67,69,72,76,78,88]. Longitudinal studies ranged from 1 month to 6 months (see Multimedia Appendix 2).

Debaleena Chattopadhyay, Tengteng Ma, Hasti Sharifi, Pamela Martyn-Nemeth

J Med Internet Res 2020;22(7):e18839

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

Smartphone and Mobile Health Apps for Tinnitus: Systematic Identification, Analysis, and Assessment

Smartphone and Mobile Health Apps for Tinnitus: Systematic Identification, Analysis, and Assessment

evaluate MARS scores from the 4 raters, we calculated the interrater agreement based on Fleiss κ [46], the internal consistency was based on Cronbach α [47], and the interrater reliability was based on Guttman λ6 [48] as well as intraclass correlation—ICC(2,k)

Muntazir Mehdi, Michael Stach, Constanze Riha, Patrick Neff, Albi Dode, Rüdiger Pryss, Winfried Schlee, Manfred Reichert, Franz J Hauck

JMIR Mhealth Uhealth 2020;8(8):e21767