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Depression

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Assessing prognosis in depression: comparing perspectives of AI models, mental health professionals and the general public.

Family medicine and community health
BACKGROUND: Artificial intelligence (AI) has rapidly permeated various sectors, including healthcare, highlighting its potential to facilitate mental health assessments. This study explores the underexplored domain of AI's role in evaluating prognosi...

A machine learning model to predict the risk of depression in US adults with obstructive sleep apnea hypopnea syndrome: a cross-sectional study.

Frontiers in public health
OBJECTIVE: Depression is very common and harmful in patients with obstructive sleep apnea hypopnea syndrome (OSAHS). It is necessary to screen OSAHS patients for depression early. However, there are no validated tools to assess the likelihood of depr...

Towards Personalised Mood Prediction and Explanation for Depression from Biophysical Data.

Sensors (Basel, Switzerland)
Digital health applications using Artificial Intelligence (AI) are a promising opportunity to address the widening gap between available resources and mental health needs globally. Increasingly, passively acquired data from wearables are augmented wi...

A Machine Learning Analysis of Big Metabolomics Data for Classifying Depression: Model Development and Validation.

Biological psychiatry
BACKGROUND: Many metabolomics studies of depression have been performed, but these have been limited by their scale. A comprehensive in silico analysis of global metabolite levels in large populations could provide robust insights into the pathologic...

Select or adjust? How information from early treatment stages boosts the prediction of non-response in internet-based depression treatment.

Psychological medicine
BACKGROUND: Internet-based interventions produce comparable effectiveness rates as face-to-face therapy in treating depression. Still, more than half of patients do not respond to treatment. Machine learning (ML) methods could help to overcome these ...

A feasibility study of robot-assisted percutaneous reduction and fixation technique for treating posterolateral depression tibial plateau fractures.

Scientific reports
Posterolateral (PL)-depression fractures of the tibial plateau are difficult to manage. The aim of this study was: (1) to present our experience with a novel technique of robot-assisted percutaneous reduction and fixation and (2) to compare it with t...

Development of depression assessment tools using humanoid robots -Can tele-operated robots talk with depressive persons like humans?

Journal of psychiatric research
BACKGROUND: Depression is a common mental disorder and causes significant social loss. Early intervention for depression is important. Nonetheless, depressed patients tend to conceal their symptoms from others based on shame and stigma, thus hesitate...

Depression Identification Using EEG Signals via a Hybrid of LSTM and Spiking Neural Networks.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Depression severity can be classified into distinct phases based on the Beck depression inventory (BDI) test scores, a subjective questionnaire. However, quantitative assessment of depression may be attained through the examination and categorization...

Evaluation of deep learning-based depression detection using medical claims data.

Artificial intelligence in medicine
Human accuracy in diagnosing psychiatric disorders is still low. Even though digitizing health care leads to more and more data, the successful adoption of AI-based digital decision support (DDSS) is rare. One reason is that AI algorithms are often n...

Deep learning for the prediction of clinical outcomes in internet-delivered CBT for depression and anxiety.

PloS one
In treating depression and anxiety, just over half of all clients respond. Monitoring and obtaining early client feedback can allow for rapidly adapted treatment delivery and improve outcomes. This study seeks to develop a state-of-the-art deep-learn...