I'm pleased to share our new paper "Digital Phenotyping of Social Functioning and Employment in People with Schizophrenia: Pilot Data from an International Sample” developed by @ErlendLane, @LucyGray, myself, @DilipJeste, and @JohnTorous. Employing a sample of 62 individuals with #schizophrenia, our study provides preliminary support for the use of #smartphone-based #digitalphenotyping to index #socialfunctioning and #employment in this population.
Long COVID research must continue! Covid is not over. 🔬
Publication alert from the Deep Digital Phenotyping Research Unit at the Luxembourg Institute of Health. We've published our paper in Frontiers in Public Health on Long COVID profiles using 27,216 #Reddit posts.
https://www.frontiersin.org/articles/10.3389/fpubh.2023.1227807/full
ObjectiveThis work aims to study the profiles of Long COVID from the perspective of the patients spontaneously sharing their experiences and symptoms on Reddit.MethodsWe collected 27,216 posts shared between July 2020 and July 2022 on Long COVID-related Reddit forums. Natural language processing, clustering techniques and a Long COVID symptoms lexicon were used to extract the different symptoms and categories of symptoms and to study the co-occurrences and correlation between them.ResultsMore than 78% of the posts mentioned at least one Long COVID symptom. Fatigue (29.4%), pain (22%), clouded consciousness (19.1%), anxiety (17.7%) and headaches (15.6%) were the most prevalent symptoms. They also highly co-occurred with a variety of other symptoms (e.g., fever, sinonasal congestion). Different categories of symptoms were found: general (45.5%), neurological/ocular (42.9%), mental health/psychological/behavioral (35.2%), body pain/mobility (35.1%) and cardiorespiratory (31.2%). Posts focusing on other concerns of the community such as vaccine, recovery and relapse and, symptom triggers were detected.ConclusionsWe demonstrated the benefits of leveraging large volumes of data from Reddit to characterize the heterogeneity of Long COVID profiles. General symptoms, particularly fatigue, have been reported to be the most prevalent and frequently co-occurred with other symptoms. Other concerns, such as vaccination and relapse following recovery, were also addressed by the Long COVI...
Análisis del fenotipo digital (todos tenemos uno) para predecir gravedad de #adicción y posibilidad de falla de tratamiento
Se llama BERT(ha)
AI-based analysis of social media language predicts addiction treatment dropout at 90 days
#Neuropsychopharmacology #Nature #Addiction #digitalphenotyping
The reoccurrence of use (relapse) and treatment dropout is frequently observed in substance use disorder (SUD) treatment. In the current paper, we evaluated the predictive capability of an AI-based digital phenotype using the social media language of patients receiving treatment for substance use disorders (N = 269). We found that language phenotypes outperformed a standard intake psychometric assessment scale when predicting patients’ 90-day treatment outcomes. We also use a modern deep learning-based AI model, Bidirectional Encoder Representations from Transformers (BERT) to generate risk scores using pre-treatment digital phenotype and intake clinic data to predict dropout probabilities. Nearly all individuals labeled as low-risk remained in treatment while those identified as high-risk dropped out (risk score for dropout AUC = 0.81; p < 0.001). The current study suggests the possibility of utilizing social media digital phenotypes as a new tool for intake risk assessment to identify individuals most at risk of treatment dropout and relapse.
Dr. David Kimhy
Dr Guy Fagherazzi, MSc, PhD
Tim Reierson
Laura Bringmann