Isak Samsten

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Panagiotis PapapetrouProfessor, Stockholm University, SwedenVerified email at dsv.su.se
Henrik BoströmProfessor of computer science - data science systems, KTH Royal Institute of TechnologyVerified email at kth.se
Jonathan RebanePhD in Data Science, Stockholm UniversityVerified email at dsv.su.se
Lars AskerAssociate Professor in Computer and Systems Sciences, Stockholm UniversityVerified email at dsv.su.se
Peter ÖhmanProfessor of Business Administration, Mid Sweden UniversityVerified email at miun.se
Zhendong WangPhD student, Stockholm UniversityVerified email at dsv.su.se
Mats DanielsonProfessor in Computer and Systems Sciences, Stockholm UniversityVerified email at preference.nu
Tarek RanaAssociate Professor, RMIT UniversityVerified email at rmit.edu.au
Jing ZhaoDepartment of Computer and Systems Sciences (DSV), Stockholm UniversityVerified email at dsv.su.se
Aristides GionisKTH Royal Institute of Technology, and Aalto UniversityVerified email at kth.se
Rami MochaourabScaniaVerified email at scania.com
Ioanna MiliouAssistant Professor, Stockholm UniversityVerified email at dsv.su.se
Kai PuolamäkiProfessor, University of Helsinki, FinlandVerified email at helsinki.fi
Francesco BagattiniUniversità di Firenze, PhDVerified email at unifi.it
Natalia SemenovaAssociate Professor of Accounting, School of Business and Economics, Linnaeus UniversityVerified email at lnu.se
Arun VenkitaramanSenior Research Scientist, Sony AIVerified email at sony.com
Cristian R. RojasProfessor, Division of Decision and Control Systems, KTH Royal Institute of TechnologyVerified email at kth.se
András BótaPostdoctoral Research Fellow, Lulea University of TechnologyVerified email at ltu.se
Panteleimon PantelidisNational and Kapodistrian University of AthensVerified email at med.uoa.gr
Dimitrios GunopulosNational and Kapodistrian University of AthensVerified email at di.uoa.gr

Isak Samsten

Stockholm University

Verified email at dsv.su.se - Homepage

Machine learning data mining time series interpretability


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Generalized random shapelet forests I Karlsson, P Papapetrou, H Boström Data mining and knowledge discovery 30, 1053-1085, 2016	203	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	94	2018
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	49	2018
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	46	2019
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	36	2022
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	36	2013
Locally and globally explainable time series tweaking I Karlsson, J Rebane, P Papapetrou, A Gionis Knowledge and Information Systems 62 (5), 1671-1700, 2020	33	2020
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	27	2022
Counterfactual explanations for survival prediction of cardiovascular ICU patients Z Wang, I Samsten, P Papapetrou Artificial Intelligence in Medicine: 19th International Conference on …, 2021	25	2021
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	24	2020
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	22	2014
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	20	2021
Forests of randomized shapelet trees I Karlsson, P Papapetrou, H Boström Statistical Learning and Data Sciences: Third International Symposium, SLDS …, 2015	19	2015
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	17	2022
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	17*	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	16	2015
Predicting adverse drug events using heterogeneous event sequences I Karlsson, H Boström 2016 IEEE International Conference on Healthcare Informatics (ICHI), 356-362, 2016	15	2016
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	14	2019
Surveillance of communicable diseases using social media: A systematic review P Pilipiec, I Samsten, A Bota PLoS One 18 (2), e0282101, 2023	13	2023
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	12	2021

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