American scientists are developing a tool for identifying signs of depression through speech patterns, and they say it could help diagnose the condition more accurately.

Machine learning is an integral part of high stakes decision making in a broad swath of human-computer interactions. You apply for a job. You submit a loan application. Algorithms determine who ...

Key takeaways: Ensemble machine learning models may predict secondary treatment for depression that does not respond to antidepressants initially. Prediction success depends on treatment type.

Researchers have created decision models capable of predicting which patients might need more treatment for their depression than what their primary care provider can offer. The algorithms were ...

Why it matters: The study findings suggest that machine learning technology could serve as a complementary decision-support tool for assessing depression.

Machine-learning algorithms can be applied to data on brain activity in people with depression in order to find such associations.

Algorithms, machine learning and artificial intelligence each play a crucial role in optimizing your hiring processes and keeping you competitive in the talent market.

Machine learning algorithm can analyze a person’s speech patterns to help diagnose the possibility of depression.

But algorithms are rarely designed to optimize for this AI-to-human handover. If they were, the AI system would only defer to its human counterpart if the person could actually make a better decision.