Explainable Time Series Tweaking via Irreversible and Reversible Temporal Transformations. - Isak Karlsson; Jonathan Rebane; Panagiotis Papapetrou; Aristides Gionis (ICDM, 2018)

Humans Construct Survey Estimates on the Fly from a Compartmentalised Representation of the Navigated Environment. - Tobias Meilinger; Agnes Henson; Jonathan Rebane; Heinrich H. Bülthoff; Hanspeter A. Mallot (Spatial Cognition, 2018)

An Investigation of Interpretable Deep Learning for Adverse Drug Event Prediction. - Jonathan Rebane; Isak Karlsson; Panagiotis Papapetrou (CBMS, 2019)

A classification framework for exploiting sparse multi-variate temporal features with application to adverse drug event detection in medical records. - Francesco Bagattini; Isak Karlsson; Jonathan Rebane; Panagiotis Papapetrou (BMC Medical Informatics Decis. Mak., 2019)

Mining Disproportional Frequent Arrangements of Event Intervals for Investigating Adverse Drug Events. - Zed Lee; Jonathan Rebane; Panagiotis Papapetrou (CBMS, 2020)

Exploiting complex medical data with interpretable deep learning for adverse drug event prediction. - Jonathan Rebane; Isak Samsten; Panagiotis Papapetrou (Artif. Intell. Medicine, 2020)

Locally and globally explainable time series tweaking. - Isak Karlsson; Jonathan Rebane; Panagiotis Papapetrou; Aristides Gionis (Knowl. Inf. Syst., 2020)

Assessing the Clinical Validity of Attention-based and SHAP Temporal Explanations for Adverse Drug Event Predictions. - Jonathan Rebane; Isak Samsten; Panteleimon Pantelidis; Panagiotis Papapetrou (CBMS, 2021)

SMILE: a feature-based temporal abstraction framework for event-interval sequence classification. - Jonathan Rebane; Isak Karlsson; Leon Bornemann; Panagiotis Papapetrou (Data Min. Knowl. Discov., 2021)

Learning from Complex Medical Data Sources. - Jonathan Rebane (2022)

Achieving a Data-Driven Risk Assessment Methodology for Ethical AI. - Anna Felländer; Jonathan Rebane; Stefan Larsson; Mattias Wiggberg; Fredrik Heintz (Digit. Soc., 2022)