Aifred Health, a Deep Learning Powered Clinical Decision Support System for Mental Health

David David Benrimoh, Robert Fratila, Sonia Israel, Kelly Perlman, Nykan Mirchi, Sneha Desai, Ariel Rosenfeld, Sabrina Knappe, Jason Behrmann, Colleen Rollins, Raymond Penh You

Research output: Chapter in Book/Report/Conference proceedingChapter


Aifred Health, one of the top two teams in the first round of the IBM Watson AI XPRIZE competition, is using deep learning to solve the problem of treatment selection and prognosis prediction in mental health, starting with depression. Globally, depression affects over 300 million people and is the leading cause of disability. While a range of effective treatments do exist, patients’ responses to treatments vary to a large degree. Some patients spend years going through a frustrating ‘trial-and-error’ process in order to find an effective treatment. The Aifred Health solution is a deep learning-powered Clinical Decision Support System (CDSS) aimed at helping clinicians select the most effective treatment plans for depression in collaboration with their patients. In this chapter, we discuss problem of treatment selection in depression and explore the technical, clinical, and ethical dimensions of building a CDSS for mental health based on deep learning technology.
Original languageEnglish
Title of host publicationThe NIPS '17 Competition
Subtitle of host publicationBuilding Intelligent Systems
EditorsSergio Escalera, Markus Weimer
PublisherSpringer Verlag
Number of pages37
ISBN (Electronic)978-3-319-94042-7
ISBN (Print)978-3-319-94041-0
StatePublished Online - 28 Sep 2018

Publication series

NameThe Springer Series on Challenges in Machine Learning
ISSN (Print)2520-1328


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