Science

Tech and Mental Health I: Diagnosis [Infographic]

today2020.04.21. 16

Background

We’re kicking off our mini-series on tech & mental health!
In this Infographic we are exploring how Artificial Intelligence can be a useful tool in expanding our knowledge of mental illnesses. Machine Learning has been trending across disciplines in recent years and through its ability to detect patterns in data, AI can also lend a helping hand in diagnosing mental illness. Zoom in on the infographic below and find out how.

 

BRAINSTORMS DIGITAL

We are excited to introduce you the speaker of our next Digital-meetup (this Thursday the 30th of April), Brainstorms Digital #2 – 3D (Bio)printing in Neuroscience: Dr Agnes Dobos.
Dr. Agnes Dobos is a postdoctoral fellow at the University of Gent in the Polymer Chemistry and Biomaterials Group. Her work research focuses on 3D printing, biofabrication and the development of bioinks. With her talk: “3D (Bio)printing in Neuroscience”, she will tell us about the latest 3D printing technologies and she will give us an overview of their current applications in neuroscience.🧠
 
 
Are you still curious? Do you want to learn more about the brain? Stay tuned for the next infographic of our mini-series, in the meantime check out our previous content about Meditation and the brain (https://www.thebrainstorms.io/blog/meditation-infographic)
 
 
Stay tuned for more interesting content – more infographics and articles are coming about artificial intelligence and free-will!

References:

1. Mental Health: Fact Sheet (WHO). Link:http://www.euro.who.int/__data/assets/pdf_file/0004/404851/MNH_FactSheet_ENG.pdf?ua=1
2. Yahata, N. et. al. Computational neuroscience approach to biomarkers and treatments for mental disorders. Psychiatry and Clinical Neurosciences (2017) 
3. Fruehwirt, W. et. al. Bayesian deep neural networks for low-cost neurophysiological markers of Alzheimer’s disease severity. Pre-print (2018) 
4. Schwartenbeck, P. & Friston, K. Computational Phenotyping in Psychiatry: A Worked Example. ENeuro (2016) 
5. Gu, X. et. al. Modeling subjective belief states in computational psychiatry: Interoceptive inference as a candidate framework. Psychopharmacology (2019)
6. Chandler, C. et. al. Using Machine Learning in Psychiatry: The Need to Establish a Framework That Nurtures Trustworthiness. Schizophrenia Bulletin (2020) 
7. Rezaii, N. et. al. A machine learning approach to predicting psychosis using semantic density and latent content analysis. NPJ Schizophrenia (2019)

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