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Machine learning for multimodal mental health detection: a systematic review of passive sensing approaches
As mental health (MH) disorders become increasingly prevalent, their multifaceted
symptoms and comorbidities with other conditions introduce complexity to diagnosis, posing …
symptoms and comorbidities with other conditions introduce complexity to diagnosis, posing …
[HTML][HTML] Correlations between objective behavioral features collected from mobile and wearable devices and depressive mood symptoms in patients with affective …
Background: Several studies have recently reported on the correlation between objective
behavioral features collected via mobile and wearable devices and depressive mood …
behavioral features collected via mobile and wearable devices and depressive mood …
Predicting symptoms of depression and anxiety using smartphone and wearable data
Background: Depression and anxiety are leading causes of disability worldwide but often
remain undetected and untreated. Smartphone and wearable devices may offer a unique …
remain undetected and untreated. Smartphone and wearable devices may offer a unique …
Tracking depression dynamics in college students using mobile phone and wearable sensing
There are rising rates of depression on college campuses. Mental health services on our
campuses are working at full stretch. In response researchers have proposed using mobile …
campuses are working at full stretch. In response researchers have proposed using mobile …
Algorithms that remember: model inversion attacks and data protection law
Many individuals are concerned about the governance of machine learning systems and the
prevention of algorithmic harms. The EU's recent General Data Protection Regulation …
prevention of algorithmic harms. The EU's recent General Data Protection Regulation …
Generalization and personalization of mobile sensing-based mood inference models: an analysis of college students in eight countries
Mood inference with mobile sensing data has been studied in ubicomp literature over the
last decade. This inference enables context-aware and personalized user experiences in …
last decade. This inference enables context-aware and personalized user experiences in …
GLOBEM dataset: multi-year datasets for longitudinal human behavior modeling generalization
Recent research has demonstrated the capability of behavior signals captured by
smartphones and wearables for longitudinal behavior modeling. However, there is a lack of …
smartphones and wearables for longitudinal behavior modeling. However, there is a lack of …
Deepmood: Forecasting depressed mood based on self-reported histories via recurrent neural networks
Depression is a prevailing issue and is an increasing problem in many people's lives.
Without observable diagnostic criteria, the signs of depression may go unnoticed, resulting …
Without observable diagnostic criteria, the signs of depression may go unnoticed, resulting …
[HTML][HTML] Mood ratings and digital biomarkers from smartphone and wearable data differentiates and predicts depression status: A longitudinal data analysis
Depression is a prevalent mental disorder. Current clinical and self-reported assessment
methods of depression are laborious and incur recall bias. Their sporadic nature often …
methods of depression are laborious and incur recall bias. Their sporadic nature often …
An insight into diagnosis of depression using machine learning techniques: a systematic review
Background In this modern era, depression is one of the most prevalent mental disorders
from which millions of individuals are affected today. The symptoms of depression are …
from which millions of individuals are affected today. The symptoms of depression are …