Deep neural networks in psychiatry
Abstract Machine and deep learning methods, today's core of artificial intelligence, have
been applied with increasing success and impact in many commercial and research …
been applied with increasing success and impact in many commercial and research …
[HTML][HTML] Digital phenotyping of mental health using multimodal sensing of multiple situations of interest: A systematic literature review
Many studies have used Digital Phenotyping of Mental Health (DPMH) to complement
classic methods of mental health assessment and monitoring. This research area proposes …
classic methods of mental health assessment and monitoring. This research area proposes …
Detecting depression and predicting its onset using longitudinal symptoms captured by passive sensing: a machine learning approach with robust feature selection
We present a machine learning approach that uses data from smartphones and fitness
trackers of 138 college students to identify students that experienced depressive symptoms …
trackers of 138 college students to identify students that experienced depressive symptoms …
Metasense: few-shot adaptation to untrained conditions in deep mobile sensing
Recent improvements in deep learning and hardware support offer a new breakthrough in
mobile sensing; we could enjoy context-aware services and mobile healthcare on a mobile …
mobile sensing; we could enjoy context-aware services and mobile healthcare on a mobile …
Extraction and interpretation of deep autoencoder-based temporal features from wearables for forecasting personalized mood, health, and stress
Continuous wearable sensor data in high resolution contain physiological and behavioral
information that can be utilized to predict human health and wellbeing, establishing the …
information that can be utilized to predict human health and wellbeing, establishing the …
Investigating the relationships between mobility behaviours and indicators of subjective well–being using smartphone–based experience sampling and GPS tracking
People interact with their physical environments every day by visiting different places and
moving between them. Such mobility behaviours likely influence and are influenced by …
moving between them. Such mobility behaviours likely influence and are influenced by …
Semantic gap in predicting mental wellbeing through passive sensing
V Das Swain, V Chen, S Mishra, SM Mattingly… - Proceedings of the …, 2022 - dl.acm.org
When modeling passive data to infer individual mental wellbeing, a common source of
ground truth is self-reports. But these tend to represent the psychological facet of mental …
ground truth is self-reports. But these tend to represent the psychological facet of mental …
[PDF][PDF] Using ML and Data-Mining Techniques in Automatic Vulnerability Software Discovery
Today's age is Machine Learning (ML) and Data-Mining (DM) Techniques, as both
techniques play a significant role in measuring vulnerability prediction accuracy. In the field …
techniques play a significant role in measuring vulnerability prediction accuracy. In the field …
A Reproducible Stress Prediction Pipeline with Mobile Sensor Data
Recent efforts to predict stress in the wild using mobile technology have increased; however,
the field lacks a common pipeline for assessing the impact of factors such as label encoding …
the field lacks a common pipeline for assessing the impact of factors such as label encoding …
[HTML][HTML] Predicting emotional states using behavioral markers derived from passively sensed data: data-driven machine learning approach
Background Mental health disorders affect multiple aspects of patients' lives, including
mood, cognition, and behavior. eHealth and mobile health (mHealth) technologies enable …
mood, cognition, and behavior. eHealth and mobile health (mHealth) technologies enable …