We propose a method to support diagnosis in domains with time series data.
Temporal abstraction is used to capture relevant domain concepts in data.
A frequent pattern discovery technique is used to extract patient characteristics.
Patterns are used to characterize population groups by comparison with a control group.
Our method uses domain knowledge to improve the interpretability and acceptability of results.