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View article: Racial disparities in continuous glucose monitoring-based 60-min glucose predictions among people with type 1 diabetes
Racial disparities in continuous glucose monitoring-based 60-min glucose predictions among people with type 1 diabetes Open
Non-Hispanic white (White) populations are overrepresented in medical studies. Potential healthcare disparities can happen when machine learning models, used in diabetes technologies, are trained on data from primarily White patients. We a…
View article: Racial disparities in continuous glucose monitoring-based 60-min glucose predictions among people with type 1 diabetes
Racial disparities in continuous glucose monitoring-based 60-min glucose predictions among people with type 1 diabetes Open
Non-Hispanic white (White) populations are overrepresented in medical studies. Potential healthcare disparities can happen when machine learning models, used in diabetes technologies, are trained on data from primarily White patients. We a…
View article: ALTRUIST: A Python Package to Emulate a Virtual Digital Cohort Study Using Social Media Data
ALTRUIST: A Python Package to Emulate a Virtual Digital Cohort Study Using Social Media Data Open
Epidemiological cohort studies play a crucial role in identifying risk factors for various outcomes among participants. These studies are often time-consuming and costly due to recruitment and long-term follow-up. Social media (SM) data ha…
View article: The Long COVID experience from a patient's perspective: a clustering analysis of 27,216 Reddit posts
The Long COVID experience from a patient's perspective: a clustering analysis of 27,216 Reddit posts Open
Objective This work aims to study the profiles of Long COVID from the perspective of the patients spontaneously sharing their experiences and symptoms on Reddit. Methods We collected 27,216 posts shared between July 2020 and July 2022 on L…
View article: Global diabetes burden: analysis of regional differences to improve diabetes care
Global diabetes burden: analysis of regional differences to improve diabetes care Open
Introduction The current evaluation processes of the burden of diabetes are incomplete and subject to bias. This study aimed to identify regional differences in the diabetes burden on a universal level from the perspective of people with d…
View article: Extraction of Explicit and Implicit Cause-Effect Relationships in Patient-Reported Diabetes-Related Tweets From 2017 to 2021: Deep Learning Approach
Extraction of Explicit and Implicit Cause-Effect Relationships in Patient-Reported Diabetes-Related Tweets From 2017 to 2021: Deep Learning Approach Open
Background Intervening in and preventing diabetes distress requires an understanding of its causes and, in particular, from a patient’s perspective. Social media data provide direct access to how patients see and understand their disease a…
View article: Emulating a virtual digital cohort study based on social media data as a complementary approach to traditional epidemiology: When, what for, and how?
Emulating a virtual digital cohort study based on social media data as a complementary approach to traditional epidemiology: When, what for, and how? Open
View article: Extraction of Explicit and Implicit Cause-Effect Relationships in Patient-Reported Diabetes-Related Tweets From 2017 to 2021: Deep Learning Approach (Preprint)
Extraction of Explicit and Implicit Cause-Effect Relationships in Patient-Reported Diabetes-Related Tweets From 2017 to 2021: Deep Learning Approach (Preprint) Open
BACKGROUND Intervening in and preventing diabetes distress requires an understanding of its causes and, in particular, from a patient’s perspective. Social media data provide direct access to how patients see and understand their disease …
View article: Global Diabetes Burden: Analysis of Regional Differences to Improve Diabetes Care
Global Diabetes Burden: Analysis of Regional Differences to Improve Diabetes Care Open
View article: Identifying causal relations in tweets using deep learning: Use case on diabetes-related tweets from 2017-2021
Identifying causal relations in tweets using deep learning: Use case on diabetes-related tweets from 2017-2021 Open
Objective: Leveraging machine learning methods, we aim to extract both explicit and implicit cause-effect associations in patient-reported, diabetes-related tweets and provide a tool to better understand opinion, feelings and observations …
View article: The Use of Social Media for Health Research Purposes: Scoping Review
The Use of Social Media for Health Research Purposes: Scoping Review Open
Background As social media are increasingly used worldwide, more and more scientists are relying on them for their health-related projects. However, social media features, methodologies, and ethical issues are unclear so far because, to ou…
View article: Scoping review protocol on the use of social media for health research purposes
Scoping review protocol on the use of social media for health research purposes Open
Introduction More than one-third of the world population uses at least one form of social media. Since their advent in 2005, health-oriented research based on social media data has largely increased as discussions about health issues are b…
View article: The Use of Social Media for Health Research Purposes: Scoping Review (Preprint)
The Use of Social Media for Health Research Purposes: Scoping Review (Preprint) Open
BACKGROUND As social media are increasingly used worldwide, more and more scientists are relying on them for their health-related projects. However, social media features, methodologies, and ethical issues are unclear so far because, to o…