AI could play ‘critical’ role in identifying appropriate treatment for depression

AI could play ‘critical’ role in identifying appropriate treatment for depression

Publication date: Feb 12, 2020

A large-scale trial led by scientists at the University of Texas Southwestern (UT Southwestern) has produced a machine learning algorithm which accurately predicts the efficacy of an antidepressant, based on a patient’s neural activity.

The UT Southwestern researchers hope that this tool could eventually play a critical role in deciding which course of treatment would be best for patients with depression, as well as being part of a new generation of -biology-based, objective strategies” which make use of technologies such as AI to treat psychiatric disorders.

The trial has reaped many studies, the latest of which demonstrates that doctors could use computational tools to guide treatment choices for depression.

Trivedi had previously established in another study that up to two-thirds of patients do not adequately respond to their first antidepressant, motivating him to find a way of identifying much earlier which treatment path is most likely to help the patient before they begin and potentially suffer further through ineffectual treatment.

Concepts Keywords
Algorithm
Antidepressant
Brain
Cortex
Depression
EEG
Electroencephalogram
FMRI
Functional MRI
MEG
Mood Disorders
Nature Biotechnology
Placebo
Psychiatric Disorders
Psychiatrist
Psychiatry
Seasonal Affective Disorder
SSRI
SSRIs
Stanford
Texas

Semantics

Type Source Name
drug DRUGBANK Serotonin
drug DRUGBANK Spinosad
disease MESH seasonal affective disorder
disease MESH mood disorders
drug DRUGBANK Tropicamide
disease MESH psychiatric disorders
disease MESH depression

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