Evaluating the efficacy of government interventions used to mitigate the spread of COVID-19 has been challenging. Mobility data from phones can be used as a low-cost and standardised mechanism to observe the change in the aggregate movement of populations in response to interventions stimuli.
Do seasonal variations in day length and temperature influence sleep in a world of climate control and electricity? Our study combined a year of wearable sleep data with meteorological data. We found that seasons, day length, and temperature influence when and how long individuals sleep.
Our systematic review and meta-analysis provides a critical assessment of the deep learning literature to date and proposes recommendations to improve the quality of deep learning research in the future.
Our recent publication, Machine Learning for Patient Risk Stratification: Standing on, or looking over, the shoulders of clinicians?, in npj Digital Medicine examines the question of whether clinical machine learning models truly extend beyond what clinicians already suspect.
The COVID-19 pandemic has disrupted people daily life, digital health solutions empowered with AI could become critical and conducted in a safe way. To develop robust and reliable clinical tools capturing large variety and heterogeneity, a privacy-preserving learning strategy is worthwhile to study.
In spite of dozens of existing treatment options, children with Attention Deficit Hyperactivity Disorder (ADHD) still fare poorly across many domains. We explore a new frontier in which digital therapeutics could help address these challenges for ADHD specifically and mental health more broadly.
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