The PSYCOP project aims at using the wealth of information in Electronic Health Records to improve patient care and treatment of those with mental illness.

To do this, we develop machine learning models with a specific focus on evaluation and clinical applicability.

We are currently developing tools for thoroughly validation our data and models, and quickly iterating on classical machine learning models. Check out our Github repositories for feature generation and data validation and model training and evaluation.

Ongoing and planned projects include:

  • Representation learning on the multi-modal EHR data
  • Deep sequential models for EHR time-series

For more information, see our paper which outlines the cohort and research directions, or reach out!



PhD students:

Lasse Hansen
Lasse Hansen
PhD Student in Machine Learning for Healthcare

I am PhD student at the Department of Clinical Medicine at Aarhus University. I study how to use natural language processing and machine learning to improve patient outcomes in psychiatry. I am broadly interested in applying machine learning to solve real world problems, and in advancing open-source software.