Towards Personalised Mood Prediction and Explanation for Depression from Biophysical Data

About this item

Title

Towards Personalised Mood Prediction and Explanation for Depression from Biophysical Data

Content partner

The University of Auckland Library

Collection

ResearchSpace@Auckland

Description

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 with carefully selected active data from depressed individuals to develop Machine Learning (ML) models of depression based on mood scores. However, most ML models are black box in nature, and hence the outputs are not explainable. Depression is also...

Read more

Format

Research paper

Research format

Journal article

Date created

2023-12

Creator

Chatterjee, Sobhan / Mishra, Jyoti / Sundram, Frederick / Roop, Partha

URL

https://hdl.handle.net/2292/67392

Related subjects

deep learning / depressive-mood prediction / explainable AI / explainable model / model optimisation / mood prediction / mood score / mood-state classification / wearable data / 46 Information and Computing Sciences / 4608 Human-Centred Computing / Mental Health / Depression / Brain Disorders / Behavioral and Social Science / 3 Good Health and Well Being / Humans / Artificial Intelligence / Affect / Algorithms / Biological Evolution / 0301 Analytical Chemistry / 0502 Environmental Science and Management / 0602 Ecology / 0805 Distributed Computing / 0906 Electrical and Electronic Engineering / 3103 Ecology / 4008 Electrical engineering / 4009 Electronics, sensors and digital hardware / 4104 Environmental management / 4606 Distributed computing and systems software

What is the copyright status of this item?

Share, Modify, Use commercially

See below for specifics about how you may use this item.

More Information

The University of Auckland Library has this to say about the rights status of this item:

Items in ResearchSpace are protected by copyright, with all rights reserved, unless otherwise indicated. Previously published items are made available in accordance with the copyright policy of the publisher.

You can learn more about the rights status of this item at:

• https://researchspace.auckland.ac.nz/docs/uoa-docs/rights.htm
• https://creativecommons.org/licenses/by/4.0/
• http://purl.org/eprint/accessRights/OpenAccess

What can I do with this item?

You must always check with The University of Auckland Library to confirm the specific terms of use, but this is our understanding:

Non-infringing use

NZ Copyright law does not prevent every use of a copyright work. You should consider what you can and cannot do with a copyright work.

Share it

This item is suitable for copying and sharing with others, without further permission.

Modify it

This item is suitable for modifying, remixing and building upon, without further permission.

Use it commercially

This item is suitable for commercial use, without further permission.