AI Medical Compendium Topic

Explore the latest research on artificial intelligence and machine learning in medicine.

Depressive Disorder

Showing 21 to 30 of 53 articles

Clear Filters

Internet-Based Management for Depressive Disorder.

Advances in experimental medicine and biology
The advances in the Internet and related technologies may lead to changes in professional roles of psychiatrists and psychotherapists. The application of artificial intelligence (AI) and electronic measurement-based care (eMBC) in the treatment of de...

A deep learning framework for automatic diagnosis of unipolar depression.

International journal of medical informatics
BACKGROUND AND PURPOSE: In recent years, the development of machine learning (ML) frameworks for automatic diagnosis of unipolar depression has escalated to a next level of deep learning frameworks. However, this idea needs further validation. Theref...

Developing algorithms to predict adult onset internalizing disorders: An ensemble learning approach.

Journal of psychiatric research
A growing literature is utilizing machine learning methods to develop psychopathology risk algorithms that can be used to inform preventive intervention. However, efforts to develop algorithms for internalizing disorder onset have been limited. The g...

Personalized prognostic prediction of treatment outcome for depressed patients in a naturalistic psychiatric hospital setting: A comparison of machine learning approaches.

Journal of consulting and clinical psychology
OBJECTIVE: Research on predictors of treatment outcome in depression has largely derived from randomized clinical trials involving strict standardization of treatments, stringent patient exclusion criteria, and careful selection and supervision of st...

Targeted prescription of cognitive-behavioral therapy versus person-centered counseling for depression using a machine learning approach.

Journal of consulting and clinical psychology
OBJECTIVE: Depression is a highly common mental disorder and a major cause of disability worldwide. Several psychological interventions are available, but there is a lack of evidence to decide which treatment works best for whom. This study aimed to ...

Fitting prediction rule ensembles to psychological research data: An introduction and tutorial.

Psychological methods
Prediction rule ensembles (PREs) are a relatively new statistical learning method, which aim to strike a balance between predictive performance and interpretability. Starting from a decision tree ensemble, like a boosted tree ensemble or a random for...

Natural language processing for structuring clinical text data on depression using UK-CRIS.

Evidence-based mental health
BACKGROUND: Utilisation of routinely collected electronic health records from secondary care offers unprecedented possibilities for medical science research but can also present difficulties. One key issue is that medical information is presented as ...

Depression screening using mobile phone usage metadata: a machine learning approach.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: Depression is currently the second most significant contributor to non-fatal disease burdens globally. While it is treatable, depression remains undiagnosed in many cases. As mobile phones have now become an integral part of daily life, th...

Detection of Depression and Scaling of Severity Using Six Channel EEG Data.

Journal of medical systems
Depression is a psychiatric problem which affects the growth of a person, like how a person thinks, feels and behaves. The major reason behind wrong diagnosis of depression is absence of any laboratory test for detection as well as severity scaling o...

Precision psychiatry in clinical practice.

International journal of psychiatry in clinical practice
The treatment of depression represents a major challenge for healthcare systems and choosing among the many available drugs without objective guidance criteria is an error-prone process. Recently, pharmacogenetic biomarkers entered in prescribing gui...