[HTML][HTML] A systematic literature review and analysis of deep learning algorithms in mental disorders
G Arji, L Erfannia, M Hemmat - Informatics in medicine unlocked, 2023 - Elsevier
Introduction Mental disorders are the main cause of mortality and morbidity worldwide. Deep
learning offers a considerable promise for mental health diagnosis and risk assessment. The …
learning offers a considerable promise for mental health diagnosis and risk assessment. The …
[HTML][HTML] Machine learning techniques in diagnostics and prediction of the clinical features of schizophrenia: a narrative review
V Gashkarimov, R Sultanova, I Efremov… - Consortium …, 2023 - cyberleninka.ru
BACKGROUND: Schizophrenia is a severe psychiatric disorder associated with a significant
negative impact. Early diagnosis and treatment of schizophrenia has a favorable effect on …
negative impact. Early diagnosis and treatment of schizophrenia has a favorable effect on …
Automated mood disorder symptoms monitoring from multivariate time-series sensory data: getting the full picture beyond a single number
Mood disorders (MDs) are among the leading causes of disease burden worldwide. Limited
specialized care availability remains a major bottleneck thus hindering pre-emptive …
specialized care availability remains a major bottleneck thus hindering pre-emptive …
Utilizing deep convolutional neural architecture with attention mechanism for objective diagnosis of schizophrenia using wearable IoMT devices
MM Misgar, MPS Bhatia - Multimedia Tools and Applications, 2024 - Springer
Mental health diagnosis often relies on subjective evaluations, which can be intrusive and
lack objectivity. With the current global situation brought about by the COVID-19 pandemic …
lack objectivity. With the current global situation brought about by the COVID-19 pandemic …
Leveraging Machine Learning for Disease Diagnoses based on Wearable Devices: A Survey
Z Jiang, V Van Zoest, W Deng… - IEEE Internet of Things …, 2023 - ieeexplore.ieee.org
Many countries around the world are facing a shortage of healthcare resources, especially
during the post-epidemic era, leading to a dramatic increase in the need for self-detection …
during the post-epidemic era, leading to a dramatic increase in the need for self-detection …
Hopping-mean: an augmentation method for motor activity data towards real-time depression diagnosis using machine learning
MM Misgar, MPS Bhatia - Multimedia Tools and Applications, 2024 - Springer
The advances from the last few decades in the fields of ML (Machine Learning), DL (Deep
Learning), and semantic computing are now changing the shape of the healthcare system …
Learning), and semantic computing are now changing the shape of the healthcare system …
Identifying digital biomarkers of illness activity and treatment response in bipolar disorder with a novel wearable device (TIMEBASE): Protocol for a pragmatic …
BackgroundBipolar disorder is highly prevalent and consists of biphasic recurrent mood
episodes of mania and depression, which translate into altered mood, sleep and activity …
episodes of mania and depression, which translate into altered mood, sleep and activity …
[HTML][HTML] Application of deep learning in wound size measurement using fingernail as the reference
DH Chang, DK Nguyen, TN Nguyen… - … and Decision Making, 2024 - pmc.ncbi.nlm.nih.gov
Objective Most current wound size measurement devices or applications require manual
wound tracing and reference markers. Chronic wound care usually relies on patients or …
wound tracing and reference markers. Chronic wound care usually relies on patients or …
Advancing ADHD diagnosis: using machine learning for unveiling ADHD patterns through dimensionality reduction on IoMT actigraphy signals
MM Misgar, MPS Bhatia - International Journal of Information Technology, 2024 - Springer
Mental health is an integral component of overall well-being, profoundly influencing the lives
of individuals, families, and communities worldwide. As our understanding of mental health …
of individuals, families, and communities worldwide. As our understanding of mental health …
[HTML][HTML] Wearable Data From Subjects Playing Super Mario, Taking University Exams, or Performing Physical Exercise Help Detect Acute Mood Disorder Episodes via …
Background Personal sensing, leveraging data passively and near-continuously collected
with wearables from patients in their ecological environment, is a promising paradigm to …
with wearables from patients in their ecological environment, is a promising paradigm to …