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AWS Health Innovation Podcast
AWS Health Innovation Podcast
AWS Health Innovation Podcast
AWS Health Innovation Podcast
Hear more about Evidation's founding story from Co-Founder and Chief Technology Officer, Alessio Signorini, in this episode of the AWS Health Innovation Podcast.
American Life in Realtime: A benchmark registry of health data for equitable precision health
American Life in Realtime: A benchmark registry of health data for equitable precision health
Ritika R. Chaturvedi, Marco Angrisani, Wendy M. Troxel, Tania Gutsche, Eva Ortega, Monika Jain, Adrien Boch & Arie Kapteyn
American Life in Realtime: A benchmark registry of health data for equitable precision health
Ritika R. Chaturvedi, Marco Angrisani, Wendy M. Troxel, Tania Gutsche, Eva Ortega, Monika Jain, Adrien Boch & Arie Kapteyn
American Life in Realtime: A benchmark registry of health data for equitable precision health
ALiR, funded by a grant from the NIH, is a first-of-its-kind, publicly available benchmark registry and research infrastructure for person-generated health data collected from smartphones and wearables, made possible by Evidation.
Ritika R. Chaturvedi, Marco Angrisani, Wendy M. Troxel, Tania Gutsche, Eva Ortega, Monika Jain, Adrien Boch & Arie Kapteyn
Evidation and Duke Big Ideas Lab partner to boost participation and increase equity in digital health research
Evidation and Duke Big Ideas Lab partner to boost participation and increase equity in digital health research
Evidation and Duke Big Ideas Lab partner to boost participation and increase equity in digital health research
Evidation and Duke Big Ideas Lab partner to boost participation and increase equity in digital health research
Evidation and Duke Big Ideas Lab announce a partnership with the goal of increasing diversity and representation in digital health studies
Predictors of seeking care for influenza-like illness in a novel digital study
Predictors of seeking care for influenza-like illness in a novel digital study
Devika Chawla, Alejandra Benitez, Hao Xu, Victoria Whitehill, Sara Tadesse-Bell, Allison Shapiro, Ernesto Ramirez, Kelly Scherer, Luca Foschini, Faye Drawnel, Barry Clinch, Marco Prunotto, Vincent Ukachukwu
Predictors of seeking care for influenza-like illness in a novel digital study
Devika Chawla, Alejandra Benitez, Hao Xu, Victoria Whitehill, Sara Tadesse-Bell, Allison Shapiro, Ernesto Ramirez, Kelly Scherer, Luca Foschini, Faye Drawnel, Barry Clinch, Marco Prunotto, Vincent Ukachukwu
Predictors of seeking care for influenza-like illness in a novel digital study
Evidation conducted a study that used person-generated health data (PGHD) to identify factors associated with seeking care for ILI.
Devika Chawla, Alejandra Benitez, Hao Xu, Victoria Whitehill, Sara Tadesse-Bell, Allison Shapiro, Ernesto Ramirez, Kelly Scherer, Luca Foschini, Faye Drawnel, Barry Clinch, Marco Prunotto, Vincent Ukachukwu
Evidation launches FluSmart, a direct-to-person digital flu monitoring program, to better understand flu in everyday life
Evidation launches FluSmart, a direct-to-person digital flu monitoring program, to better understand flu in everyday life
Evidation launches FluSmart, a direct-to-person digital flu monitoring program, to better understand flu in everyday life
Evidation launches FluSmart, a direct-to-person digital flu monitoring program, to better understand flu in everyday life
Evidation announces the launch of FluSmart, a direct-to-person digital flu monitoring program hosted on the Evidation platform.
Individuals diagnosed with breast cancer or in remission self-report QoL
Individuals diagnosed with breast cancer or in remission self-report QoL
Individuals diagnosed with breast cancer or in remission self-report QoL
Individuals diagnosed with breast cancer or in remission self-report QoL
Person-generated health data (PGHD) shows promise in detecting cognitive impairment & informing therapies.
COVID-19 real-world evidence primer
COVID-19 real-world evidence primer
Amy Cavet; Claire Cravero, MPH; Aaron Galaznik, MD; Bray Patrick-Lake, MFS; Aniketh Talwai
COVID-19 real-world evidence primer
Amy Cavet; Claire Cravero, MPH; Aaron Galaznik, MD; Bray Patrick-Lake, MFS; Aniketh Talwai
COVID-19 real-world evidence primer
Evidation contributed to this online resource consisting of seven chapters that cover types of RWD, methods in RWE generation, examples of RWE studies, and more.
Amy Cavet; Claire Cravero, MPH; Aaron Galaznik, MD; Bray Patrick-Lake, MFS; Aniketh Talwai
The case for multimodal data capture: Deeper insights and increased prediction accuracy
The case for multimodal data capture: Deeper insights and increased prediction accuracy
The case for multimodal data capture: Deeper insights and increased prediction accuracy
The case for multimodal data capture: Deeper insights and increased prediction accuracy
Evidation participated in an IEEE EMBS workshop that explored how machine learning can convert data into measures for both disease detection and overall quality of life. The key takeaways are presented in this article.
What possibly affects nighttime heart rate? Conclusions from n-of-1 observational data
What possibly affects nighttime heart rate? Conclusions from n-of-1 observational data
Igor Matias, Eric J. Daza, Katarzyna Wac
What possibly affects nighttime heart rate? Conclusions from n-of-1 observational data
Igor Matias, Eric J. Daza, Katarzyna Wac
What possibly affects nighttime heart rate? Conclusions from n-of-1 observational data
Evidation's Lead Biostatistician, Eric J. Daza, worked with the Quality of Life Technology Lab to conduct an n-of-1 study using the MoTR method to evaluate the suggested effects of daily stressors on nighttime heart rate, sleep time, and physical activity in an individualized way.
Igor Matias, Eric J. Daza, Katarzyna Wac
Model-Twin Randomization (MoTR): A Monte Carlo method for estimating the within-individual average treatment effect using wearable sensors
Model-Twin Randomization (MoTR): A Monte Carlo method for estimating the within-individual average treatment effect using wearable sensors
Eric J. Daza, Logan Schneider
Model-Twin Randomization (MoTR): A Monte Carlo method for estimating the within-individual average treatment effect using wearable sensors
Eric J. Daza, Logan Schneider
Model-Twin Randomization (MoTR): A Monte Carlo method for estimating the within-individual average treatment effect using wearable sensors
In this academic preprint, the model twin randomization (MoTR; "motor") method for analyzing possible causes and effects an individual's intensive longitudinal data is introduced.
Eric J. Daza, Logan Schneider
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