| Learning from heterogeneous temporal data in electronic health records J Zhao, P Papapetrou, L Asker, H Boström Journal of Biomedical Informatics, 2016 | 102 | 2016 |
| Multicriteria benefit–risk assessment using network meta-analysis G van Valkenhoef, T Tervonen, J Zhao, B de Brock, HL Hillege, ... Journal of clinical epidemiology 65 (4), 394-403, 2012 | 78 | 2012 |
| Predictive modeling of structured electronic health records for adverse drug event detection J Zhao, A Henriksson, L Asker, H Boström BMC Medical Informatics and Decision Making 15 (Suppl 4), S1, 2015 | 77 | 2015 |
| Ensembles of randomized trees using diverse distributed representations of clinical events A Henriksson, J Zhao, H Dalianis, H Boström BMC medical informatics and decision making 16 (2), 85-95, 2016 | 37 | 2016 |
| Detecting adverse drug events with multiple representations of clinical measurements J Zhao, A Henriksson, L Asker, H Boström 2014 IEEE International Conference on Bioinformatics and Biomedicine (BIBM …, 2014 | 35 | 2014 |
| 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 | 35 | 2013 |
| Modeling electronic health records in ensembles of semantic spaces for adverse drug event detection A Henriksson, J Zhao, H Boström, H Dalianis 2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM …, 2015 | 30 | 2015 |
| Handling temporality of clinical events for drug safety surveillance J Zhao, A Henriksson, M Kvist, L Asker, H Boström AMIA Annual Symposium Proceedings 2015, 1371, 2015 | 30 | 2015 |
| Detecting adverse drug events using concept hierarchies of clinical codes J Zhao, A Henriksson, H Boström 2014 IEEE International Conference on Healthcare Informatics, 285-293, 2014 | 25 | 2014 |
| Temporal weighting of clinical events in electronic health records for pharmacovigilance J Zhao 2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM …, 2015 | 20 | 2015 |
| Modeling heterogeneous clinical sequence data in semantic space for adverse drug event detection A Henriksson, J Zhao, H Boström, H Dalianis 2015 IEEE International Conference on Data Science and Advanced Analytics …, 2015 | 20 | 2015 |
| Learning temporal weights of clinical events using variable importance J Zhao, A Henriksson BMC medical informatics and decision making 16 (2), 111-121, 2016 | 19 | 2016 |
| 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 |
| Cascading adverse drug event detection in electronic health records J Zhao, A Henriksson, H Boström 2015 IEEE International Conference on Data Science and Advanced Analytics …, 2015 | 12 | 2015 |
| Learning predictive models from electronic health records J Zhao Department of Computer and Systems Sciences, Stockholm University, 2017 | 9 | 2017 |
| 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 | 6 | 2014 |
| 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 | 6 | 2013 |
| Dimensionality reduction with random indexing: An application on adverse drug event detection using electronic health records I Karlsson, J Zhao 2014 IEEE 27th International Symposium on Computer-Based Medical Systems …, 2014 | 5 | 2014 |
| Predicting Adverse Drug Events with Confidence T Löfström, J Zhao, H Linusson, K Jansson Thirteenth Scandinavian Conference on Artificial Intelligence: SCAI 2015 278 …, 2015 | 2 | 2015 |
| Learning from heterogeneous temporal data in electronic health records (vol 65, pg 105, 2017) J Zhao, P Papapetrou, L Asker, H Bostrom JOURNAL OF BIOMEDICAL INFORMATICS 101, 2020 | | 2020 |
Henrik BoströmProfessor of computer science - data science systems, KTH Royal Institute of TechnologyVerifierad e-postadress på kth.se
