Detecting information in electronic health record texts
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Bas van Stein
Mar 21, 2022, 10:49:15 AM3/21/22
to LIACS thesis projects
We have various projects available for structuring information appearing in free text in electronic health records related to
- anonymisation: recognising and masking sensitive patient information (such as patient name) in text data
- negation: recognition of negated symptoms/remarks (e.g. X individual did not display any reaction to medication Y, X is unwell, X is not aggressive, etc.)
- treatment or disease prognosis
We would like to investigate either knowledge-based or deep learning approaches and/or combine and evaluate existing approaches on real world data in Dutch.
The data available include: general practitioners patient notes, EHR from mental health centres and child psychiatric data.
The projects will be in collaboration with LUMC.
- anonymisation: recognising and masking sensitive patient information (such as patient name) in text data
- negation: recognition of negated symptoms/remarks (e.g. X individual did not display any reaction to medication Y, X is unwell, X is not aggressive, etc.)
- treatment or disease prognosis
We would like to investigate either knowledge-based or deep learning approaches and/or combine and evaluate existing approaches on real world data in Dutch.
The data available include: general practitioners patient notes, EHR from mental health centres and child psychiatric data.
The projects will be in collaboration with LUMC.
Supervisors:
dr Kalliopi Zervanou & Prof. Marco Spruit
dr Kalliopi Zervanou & Prof. Marco Spruit
For more information please do not hesitate to contact me at: k.zer...@liacs.leidenuniv.nl
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