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Using a Web-Based App to Improve Hand Hygiene Compliance Rates

HospitalBoston, MA,United Stateshzhang37@mgh.harvard.eduLandmanAdamMD11Brigham and Women's HospitalBoston, MAUnited StatesCorresponding Author: Haipeng Zhang hzhang37@mgh.harvard.eduJul-Dec20181709201842e118122820182982018©Benjamin Rome, Haipeng Zhang, Adam

Benjamin Rome, Haipeng Zhang, Adam Landman

iproc 2018;4(2):e11812

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The Impact of Providing a Tool Kit for Innovators in an Academic Medical Center to Scale Digital Health Innovation

HospitalBrigham Digital Innovation HubBoston, MAUnited States2Brandeis UniversityWaltham, MAUnited StatesCorresponding Author: Ritika Saxena rsaxena@brandeis.eduJul-Dec20181709201842e118082820182982018©Ritika Saxena, Josephine Elias, Chenzhe Cao, Haipeng Zhang, Adam

Ritika Saxena, Josephine Elias, Chenzhe Cao, Haipeng Zhang, Adam Landman

iproc 2018;4(2):e11808

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A Novel Information Retrieval Tool to Find Hospital Care Team Members: Development and Usability Study

Kyle Morawski, Craig Monsen, Sukhjit Takhar, Adam Landman

JMIR Hum Factors 2018;5(2):e14

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Interrater Reliability of mHealth App Rating Measures: Analysis of Top Depression and Smoking Cessation Apps

Adam C Powell, John Torous, Steven Chan, Geoffrey Stephen Raynor, Erik Shwarts, Meghan Shanahan, Adam B Landman

JMIR Mhealth Uhealth 2016;4(1):e15

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The Development of an Automated Device for Asthma Monitoring for Adolescents: Methodologic Approach and User Acceptability

Figure 5 illustrates the schematic overview of the audio data processing by ADAM. Although our initial intention was to detect both coughing and wheezing, we were only able to successfully apply our automated techniques to coughs.

Hyekyun Rhee, Sarah Miner, Mark Sterling, Jill S. Halterman, Eileen Fairbanks

JMIR Mhealth Uhealth 2014;2(2):e27

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Evaluating the Validity of an Automated Device for Asthma Monitoring for Adolescents: Correlational Design

These findings are useful for an initial understanding of the validity of ADAM and for providing direction for further studies.

Hyekyun Rhee, Michael J Belyea, Mark Sterling, Mark F Bocko

J Med Internet Res 2015;17(10):e234

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Machine Learning Classifiers for Twitter Surveillance of Vaping: Comparative Machine Learning Study

, epochs: 10, batch_size: 16LSTM-CNNRelevancemax_features: 166,395, embed_size: 300, max_len: 75, optimizer: adam, filters: 50, kernel_size: 2, epochs: 10, batch_size: 16BiLSTMcRelevancemax_features: 166,395, embed_size: 300, max_len: 75, optimizer: adam, epochs

Shyam Visweswaran, Jason B Colditz, Patrick O’Halloran, Na-Rae Han, Sanya B Taneja, Joel Welling, Kar-Hai Chu, Jaime E Sidani, Brian A Primack

J Med Internet Res 2020;22(8):e17478

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Comparing Characteristics of Patients Who Connect Their iPhones to an Electronic Health Records System Versus Patients Who Connect Without Personal Devices: Cohort Study

William J Gordon, David W Bates, Daniel Fuchs, John Pappas, Sara Silacci, Adam Landman

J Med Internet Res 2019;21(8):e14871

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Can Google Searches Predict the Popularity and Harm of Psychoactive Agents?

Wojciech Jankowski, Marcin Hoffmann

J Med Internet Res 2016;18(2):e38

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Implementation of a RFID Tracking System to Capture Trauma Attending Arrival Times in the Emergency Department

Nicholas Faoro, Meghan McDonald, Heather Lyu, Lorraine Fenelon, Patricia Lydon, Kevin Kreitzman, Michael Fraai, Ali Salim, Adam Landman

iproc 2017;3(1):e31

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