Research output per year
Research output per year
Organization profile
Profile Information
This new Centre operates at the intersection of two ASRIs: Artificial Intelligence and Mental Health. Its scope is to apply AI methods - machine learning, reasoning, knowledge graphs - to various mental health diseases. The initial set of diseases include ADHD, autism and suicide prevention, but is expected to be expanded in the course of the Centre’s operation.
Goals
- To carry out internationally outstanding research in AI for mental health.
- To create a team of research active staff with a common interest in AI for mental health.
- To provide support for existing staff and to build capacity in areas of strategic research importance.
- To produce high quality publications.
- To improve the standard of publications produced by staff and to broaden the scope of publication options.
- To secure high profile research grants.
- To increase international links with world class universities.
- To attract high calibre research students.
- To identify the needs of end users and to develop innovative, multi-disciplinary solutions to those needs.
- To produce research that impacts on policy and practice.
- To disseminate research beyond academia.
Fingerprint
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Network
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Profiles
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George Bargiannis
- Department of Computer Science - Subject Area Leader (CIS)
- School of Computing and Engineering
- Centre for Planning, Autonomy and Representation of Knowledge - Member
- Centre of Artificial Intelligence for Mental Health - Member
Person: Academic
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Sotirios Batsakis
- Department of Computer Science - Senior Research Fellow
- School of Computing and Engineering
- Centre for Planning, Autonomy and Representation of Knowledge - Member
- Centre of Artificial Intelligence for Mental Health - Member
Person: Academic
Research output
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A Decision Tree-initialised Neuro-fuzzy Approach for Clinical Decision Support
Chen, T., Shang, C., Su, P., Keravnou-Papailiou, E., Zhao, Y., Antoniou, G. & Shen, Q., 1 Jan 2021, In: Artificial Intelligence in Medicine. 111, 13 p., 101986.Research output: Contribution to journal › Article › peer-review
Open Access -
An Efficient Hybrid Planning Framework for In-Station Train Dispatching
Cardellini, M., Maratea, M., Vallati, M., Boleto, G. & Oneto, L., 17 Mar 2021, (Accepted/In press) Proceedings of the 2021 International Conference on Computational Science: (ICCS 2021). Springer Nature Switzerland AGResearch output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review
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In-Station Train Dispatching: A PDDL+ Planning Approach
Cardellini, M., Maratea, M., Vallati, M., Boleto, G. & Oneto, L., 16 Feb 2021, (Accepted/In press) Proceedings of the 31st International Conference on Automated Planning and Scheduling: ICAPS 2021. AAAI press, 9 p.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review