Filter by input type
Select all
News
Pages
Events
Filter by category
Select all
AI ANALYTICS
Mobile Apps and Internet of Things
Advancement of science
big data
Connected communities
Coronavirus
Courses and training
DIAGNOSIS
Initial Editorial
Editorials
A world in the cloud
Events
Infographics
Artificial Intelligence and Science
IoT Apps
News
Digital platforms
Social networks
Review of scientific publications
Course Summary
Synopsis of essay
Overview of reference frames
Synopsis of recent publications
Use of Digital Platforms
Filter by input type
Select all
News
Pages
Events
Filter by category
Select all
AI ANALYTICS
Mobile Apps and Internet of Things
Advancement of science
big data
Connected communities
Coronavirus
Courses and training
DIAGNOSIS
Initial Editorial
Editorials
A world in the cloud
Events
Infographics
Artificial Intelligence and Science
IoT Apps
News
Digital platforms
Social networks
Review of scientific publications
Course Summary
Synopsis of essay
Overview of reference frames
Synopsis of recent publications
Use of Digital Platforms
Artificial Intelligence could improve access to mental health services

Researchers from MIT and Massachusetts General Hospital (MGH) propose the use of machine learning and Artificial Intelligence (AI) to improve access to mental health care services.

MIT researchers suggest that AI may be able to help make mental health services more accessible to patients. The psychologist and researcher Paola Pedrelli, from the MGH, explained that, from her experience, she has encountered various barriers to accessing mental health, including when and where to seek help.

In this sense, Pedrelli, in collaboration with MIT researcher Rosalind Picard, have worked for the last five years on a project based on machine learning algorithms, which seeks to help diagnose and monitor symptoms in patients with depression.

“We are trying to build sophisticated models that have the ability not only to learn what is common between people, but also to learn categories of what is changing in an individual's life,” Picard explained. In addition, he highlighted that they seek to provide people with the opportunity to access personalized information based on evidence that really makes a difference in their health.

The researchers conducted an initial study of MGH participants with major depressive disorders who had recently changed their treatment. Through the use of wearables, the participants collect information on biometric data such as electrodermal activity, as well as data from the participants' mobile phones such as text messages, phone calls, use of applications, location and a biweekly survey on depression.

“We put all the data we collect from the wearable and the smartphone into our machine learning algorithm, and we try to see how well the machine learning predicts the labels given by doctors,” explains Picard.

However, another of the challenges of the machine learning application is the creation and development of a tool that gives control to users, for example, a new device, a mobile app or other methods.

“What might be effective is a tool that says to an individual, 'The reason you're feeling depressed might be that the data related to your sleep has changed, and the data is related to your social activity, and you haven't had time with your friends, your physical activity has been reduced. The recommendation is that you find a way to increase those things,” Picard explained.

Outstanding news

News by country

Share

Digital Health in the world

  • — Science Brief: Omicron (B.1.1.529) Variant/CDC updates
    See more
  • —Coronavirus resource center/Johns Hopkins
    See more
  • — Epidemiological tracing of COVID-19 contacts / Johns Hopkins Course
    See more
  • — SARS-CoV-2 infection behavior / FCS calculator
    See more
  • — Omicron SARS-CoV-2 variant: a new chapter in the COVID-19 pandemic/ Article The Lancet
    See more
  • —Genomic Epidemiology Tracker/GISAID
    See more
  • — Mexican Genomic Surveillance Consortium
    See more
Secured By miniOrange