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Advances in Intelligent Data Analysis XV: 15th International Symposium, IDA 2016, Stockholm, Sweden, October 13-15, 2016, Proceedings

1, 2016

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Guest editorial: Special issue on mining for health

Data mining and knowledge discovery, 2021-07, Vol.35 (4), p.1710-1712

2021

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SMILE: a feature-based temporal abstraction framework for event-interval sequence classification

Data mining and knowledge discovery, 2021, Vol.35 (1), p.372-399

2021

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Locally and globally explainable time series tweaking

Knowledge and information systems, 2020-05, Vol.62 (5), p.1671-1700

2020

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A classification framework for exploiting sparse multi-variate temporal features with application to adverse drug event detection in medical records

BMC medical informatics and decision making, 2019-01, Vol.19 (1), p.7-7, Article 7

2019

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Explainable AI for Data-Driven Feedback and Intelligent Action Recommendations to Support Students Self-Regulation

Frontiers in artificial intelligence, 2021-11, Vol.4, p.723447-723447

2021

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Guiding principles for the use of knowledge bases and real-world data in clinical decision support systems: report by an international expert workshop at Karolinska Institutet

Expert review of clinical pharmacology, 2020-09, Vol.13 (9), p.925-934

2020

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Generalized random shapelet forests

Data mining and knowledge discovery, 2016-09, Vol.30 (5), p.1053-1085

2016

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Z-Time: efficient and effective interpretable multivariate time series classification

Data mining and knowledge discovery, 2024, Vol.38 (1), p.206-236

2024

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Exploiting complex medical data with interpretable deep learning for adverse drug event prediction

Artificial intelligence in medicine, 2020-09, Vol.109, p.101942-101942, Article 101942

2020

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Glacier: guided locally constrained counterfactual explanations for time series classification

Machine learning, 2024

2024

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Learning from heterogeneous temporal data in electronic health records

Journal of biomedical informatics, 2017-01, Vol.65, p.105-119

2017

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Finding the longest common sub-pattern in sequences of temporal intervals

Data mining and knowledge discovery, 2015-09, Vol.29 (5), p.1178-1210

2015

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Random subspace and random projection nearest neighbor ensembles for high dimensional data

Expert systems with applications, 2022-04, Vol.191, p.116078, Article 116078

2022

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On searching and indexing sequences of temporal intervals

Data mining and knowledge discovery, 2017-05, Vol.31 (3), p.809-850

2017

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Style-transfer counterfactual explanations: An application to mortality prevention of ICU patients

Artificial intelligence in medicine, 2023-01, Vol.135, p.102457-102457, Article 102457

2023

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Ijuice: integer JUstIfied counterfactual explanations

Machine learning, 2024

2024

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Cellular Traffic Prediction and Classification: A Comparative Evaluation of LSTM and ARIMA

Discovery Science, 2019, p.129-144

2019

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Finding Local Groupings of Time Series

Machine Learning and Knowledge Discovery in Databases, 2023, p.70-86

2023

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Method in interdisciplinary research: data science for digital art history

International journal for digital art history, 2019 (4)

2019

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A peek into the black box: exploring classifiers by randomization

Data mining and knowledge discovery, 2014-09, Vol.28 (5-6), p.1503-1529

2014

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RTEX: A novel framework for ranking, tagging, and explanatory diagnostic captioning of radiography exams

Journal of the American Medical Informatics Association : JAMIA, 2021, Vol.28 (8), p.1651-1659

2021

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Counterfactual Explanations for Survival Prediction of Cardiovascular ICU Patients

Artificial Intelligence in Medicine, 2021, p.338-348

2021

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Corrigendum to ‘Learning from heterogeneous temporal data in electronic health records’. [J. Biomed. Inform. 65 (2017) 105–119]

Journal of biomedical informatics, 2020-01, Vol.101, p.103352-103352, Article 103352

2020

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An Investigation of Interpretable Deep Learning for Adverse Drug Event Prediction

2019 IEEE 32nd International Symposium on Computer-Based Medical Systems, 2019, p.337-342

2019

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Advances in Intelligent Data Analysis XV: 15th International Symposium, IDA 2016, Stockholm, Sweden, October 13-15, 2016, Proceedings

Guest editorial: Special issue on mining for health

SMILE: a feature-based temporal abstraction framework for event-interval sequence classification

Locally and globally explainable time series tweaking

A classification framework for exploiting sparse multi-variate temporal features with application to adverse drug event detection in medical records

Explainable AI for Data-Driven Feedback and Intelligent Action Recommendations to Support Students Self-Regulation

Guiding principles for the use of knowledge bases and real-world data in clinical decision support systems: report by an international expert workshop at Karolinska Institutet

Generalized random shapelet forests

Z-Time: efficient and effective interpretable multivariate time series classification

Exploiting complex medical data with interpretable deep learning for adverse drug event prediction

Glacier: guided locally constrained counterfactual explanations for time series classification

Learning from heterogeneous temporal data in electronic health records

Finding the longest common sub-pattern in sequences of temporal intervals

Random subspace and random projection nearest neighbor ensembles for high dimensional data

On searching and indexing sequences of temporal intervals

Style-transfer counterfactual explanations: An application to mortality prevention of ICU patients

Ijuice: integer JUstIfied counterfactual explanations

Cellular Traffic Prediction and Classification: A Comparative Evaluation of LSTM and ARIMA

Finding Local Groupings of Time Series

Method in interdisciplinary research: data science for digital art history

A peek into the black box: exploring classifiers by randomization

RTEX: A novel framework for ranking, tagging, and explanatory diagnostic captioning of radiography exams

Counterfactual Explanations for Survival Prediction of Cardiovascular ICU Patients

Corrigendum to ‘Learning from heterogeneous temporal data in electronic health records’. [J. Biomed. Inform. 65 (2017) 105–119]

An Investigation of Interpretable Deep Learning for Adverse Drug Event Prediction

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