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Isak Samsten
Titel
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År
Generalized random shapelet forests
I Karlsson, P Papapetrou, H Boström
Data mining and knowledge discovery 30, 1053-1085, 2016
2022016
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
942018
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
492018
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, 1-20, 2019
462019
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
362022
Predicting adverse drug events by analyzing electronic patient records
I Karlsson, J Zhao, L Asker, H Boström
Artificial Intelligence in Medicine: 14th Conference on Artificial …, 2013
362013
Locally and globally explainable time series tweaking
I Karlsson, J Rebane, P Papapetrou, A Gionis
Knowledge and Information Systems 62 (5), 1671-1700, 2020
322020
Corporate governance performance ratings with machine learning
J Svanberg, T Ardeshiri, I Samsten, P Öhman, PE Neidermeyer, T Rana, ...
Intelligent Systems in Accounting, Finance and Management 29 (1), 50-68, 2022
272022
Counterfactual explanations for survival prediction of cardiovascular ICU patients
Z Wang, I Samsten, P Papapetrou
Artificial Intelligence in Medicine: 19th International Conference on …, 2021
242021
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
242020
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
222014
Forests of randomized shapelet trees
I Karlsson, P Papapetrou, H Boström
Statistical Learning and Data Sciences: Third International Symposium, SLDS …, 2015
192015
Learning time series counterfactuals via latent space representations
Z Wang, I Samsten, R Mochaourab, P Papapetrou
Discovery Science: 24th International Conference, DS 2021, Halifax, NS …, 2021
172021
Post hoc explainability for time series classification: Toward a signal processing perspective
R Mochaourab, A Venkitaraman, I Samsten, P Papapetrou, CR Rojas
IEEE signal processing magazine 39 (4), 119-129, 2022
162022
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
16*2017
Goldeneye++: A closer look into the black box
A Henelius, K Puolamäki, I Karlsson, J Zhao, L Asker, H Boström, ...
Statistical Learning and Data Sciences: Third International Symposium, SLDS …, 2015
162015
Predicting adverse drug events using heterogeneous event sequences
I Karlsson, H Boström
2016 IEEE International Conference on Healthcare Informatics (ICHI), 356-362, 2016
152016
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
142019
Surveillance of communicable diseases using social media: A systematic review
P Pilipiec, I Samsten, A Bota
PLoS One 18 (2), e0282101, 2023
132023
Assessing the clinical validity of attention-based and SHAP temporal explanations for adverse drug event predictions
J Rebane, I Samsten, P Pantelidis, P Papapetrou
2021 IEEE 34th International Symposium on Computer-Based Medical Systems …, 2021
122021
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Artiklar 1–20