Isak Samsten
Citeras av
Citeras av
Generalized random shapelet forests
I Karlsson, P Papapetrou, H Boström
Data mining and knowledge discovery 30 (5), 1053-1085, 2016
Seq2Seq RNNs and ARIMA models for Cryptocurrency Prediction: A Comparative Study
J Rebane, I Karlsson, S Denic, P Papapetrou
Proc. of the ACM KDD, 2018
Predicting adverse drug events by analyzing electronic patient records
I Karlsson, J Zhao, L Asker, H Boström
Conference on Artificial Intelligence in Medicine in Europe, 125-129, 2013
A classification framework for exploiting sparse multi-variate temporal features with application to adverse drug event detection in medical records
F Bagattini, I Karlsson, J Rebane, P Papapetrou
BMC medical informatics and decision making 19 (1), 1-20, 2019
Explainable time series tweaking via irreversible and reversible temporal transformations
I Karlsson, J Rebane, P Papapetrou, A Gionis
2018 IEEE International Conference on Data Mining (ICDM), 207-216, 2018
Forests of randomized shapelet trees
I Karlsson, P Papapetrou, H Boström
International Symposium on Statistical Learning and Data Sciences, 126-136, 2015
Handling sparsity with random forests when predicting adverse drug events from electronic health records
I Karlsson, H Boström
2014 ieee international conference on healthcare informatics, 17-22, 2014
Goldeneye++: A closer look into the black box
A Henelius, K Puolamäki, I Karlsson, J Zhao, L Asker, H Boström, ...
International symposium on statistical learning and data sciences, 96-105, 2015
Conformal prediction using random survival forests
H Bostrom, L Asker, R Gurung, I Karlsson, T Lindgren, P Papapetrou
2017 16th IEEE International Conference on Machine Learning and Applications …, 2017
Locally and globally explainable time series tweaking
I Karlsson, J Rebane, P Papapetrou, A Gionis
Knowledge and Information Systems 62 (5), 1671-1700, 2020
Multi-channel ECG classification using forests of randomized shapelet trees
I Karlsson, P Papapetrou, L Asker
Proceedings of the 8th ACM International Conference on PErvasive …, 2015
An investigation of interpretable deep learning for adverse drug event prediction
J Rebane, I Karlsson, P Papapetrou
2019 IEEE 32nd International Symposium on Computer-Based Medical Systems …, 2019
Predicting adverse drug events using heterogeneous event sequences
I Karlsson, H Boström
2016 IEEE International Conference on Healthcare Informatics (ICHI), 356-362, 2016
Embedding-based subsequence matching with gaps–range–tolerances: a Query-By-Humming application
A Kotsifakos, I Karlsson, P Papapetrou, V Athitsos, D Gunopulos
The VLDB Journal 24 (4), 519-536, 2015
Exploiting complex medical data with interpretable deep learning for adverse drug event prediction
J Rebane, I Samsten, P Papapetrou
Artificial Intelligence in Medicine 109, 101942, 2020
Early random shapelet forest
I Karlsson, P Papapetrou, H Boström
International Conference on Discovery Science, 261-276, 2016
Applying methods for signal detection in spontaneous reports to electronic patient records
J Zhao, I Karlsson, L Asker, H Boström
Proceedings of the ACM KDD, 2013
Mining candidates for adverse drug interactions in electronic patient records
L Asker, H Boström, I Karlsson, P Papapetrou, J Zhao
Proceedings of the 7th International Conference on PErvasive Technologies …, 2014
Prediction of environmental controversies and development of a corporate environmental performance rating methodology
J Svanberg, T Ardeshiri, I Samsten, P Öhman, T Rana, M Danielson
Journal of Cleaner Production 344, 130979, 2022
Order in the random forest
I Karlsson
Department of Computer and Systems Sciences, Stockholm University, 2017
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