Data Science Research Group: Learning from Medical Data Sources

Learning from Medical Data Sources

Description

The key aim is to develop and employ machine learning methods for providing efficient and effective decision support for healthcare and pharmaceutical research. The research group is currently focusing on two concrete problems:

Learning temporal models for predicting and preventing adverse events in healthcare, such as Adverse Drug Events (ADEs)
Understanding heart failure and modeling heart failure patient treatment trajectories.

For the purposes of these two projects, our group has established strong collaboration with Stockholm County Council, Karolinska University Hospital, and Karolinska Institute.

Latest publications

	COVID-19 detection from thermal image and tabular medical data utilizing multi-modal machine learning Alam, Mahbub Ul, Hollmén, Jaakko, and Rahmani, Rahim In 2023 IEEE 36th International Symposium on Computer-Based Medical Systems (CBMS), 2023
	Machine learning models for automated interpretation of 12-lead electrocardiographic signals: a narrative review of techniques, challenges, achievements and clinical relevance Pantelidis, Panteleimon, Bampa, Maria, Oikonomou, Evangelos, and Papapetrou, Panagiotis Journal of Medical Artificial Intelligence, 2023
	Identification of essential genes associated with SARS-CoV-2 infection as potential drug target candidates with machine learning algorithms Taheri, Golnaz, and Habibi, Mahnaz Scientific Reports, 2023
	Early prediction of the risk of ICU mortality with Deep Federated Learning Randl, Korbinian, Armengol, Núria Lladós, Mondrejevski, Lena, and Miliou, Ioanna In 2023 IEEE 36th International Symposium on Computer-Based Medical Systems (CBMS), 2023
	AID4HAI: Automatic Idea Detection for Healthcare-Associated Infections from Twitter, a Framework Based on Active Learning and Transfer Learning Kharazian, Zahra, Rahat, Mahmoud, Gama, Fábio, Mashhadi, Peyman Sheikholharam, Nowaczyk, Sławomir, Lindgren, Tony, and Magnússon, Sindri In Advances in Intelligent Data Analysis XXI: 21st International Symposium on Intelligent Data Analysis, IDA 2023, Louvain-la-Neuve, Belgium, April 12–14, 2023, Proceedings, 2023
	Predicting Drug Treatment for Hospitalized Patients with Heart Failure Zhou, Linyi, and Miliou, Ioanna In Machine Learning and Principles and Practice of Knowledge Discovery in Databases: International Workshops of ECML PKDD 2022, Grenoble, France, September 19–23, 2022, Proceedings, Part II, 2023
	A Workflow for Generating Patient Counterfactuals in Lung Transplant Recipients Rugolon, Franco, Bampa, Maria, and Papapetrou, Panagiotis In Machine Learning and Principles and Practice of Knowledge Discovery in Databases: International Workshops of ECML PKDD 2022, Grenoble, France, September 19–23, 2022, Proceedings, Part II, 2023
	Optimising and validating deep learning approaches for diagnosing atrial fibrillation from few-lead ambulatory electrocardiogram signals Pantelidis, P, Oikonomou, E, Souvaliotis, N, Spartalis, M, Bampa, Maria, Papapetrou, Panagiotis, Siasos, G, and Vavuranakis, M Europace, 2022
	FLICU: A Federated Learning Workflow for Intensive Care Unit Mortality Prediction Mondrejevski, Lena, Miliou, Ioanna, Montanino, Annaclaudia, Pitts, David, Hollmén, Jaakko, and Papapetrou, Panagiotis In 2022 IEEE 35th International Symposium on Computer-Based Medical Systems (CBMS), 2022
	Inside the “brain” of an artificial neural network: an interpretable deep learning approach to paroxysmal atrial fibrillation diagnosis from electrocardiogram signals during sinus rhythm Pantelidis, P, Oikonomou, E, Lampsas, S, Souvaliotis, N, Spartalis, M, Vavuranakis, MA, Bampa, M, Papapetrou, P, Siasos, G, and Vavuranakis, M European Heart Journal, 2022
	Early oxygen levels contribute to brain injury in extremely preterm infants Rantakari, Krista, Rinta-Koski, Olli-Pekka, Metsäranta, Marjo, Hollmén, Jaakko, Särkkä, Simo, Rahkonen, Petri, Lano, Aulikki, Lauronen, Leena, Nevalainen, Päivi, Leskinen, Markus J, and others, Pediatric Research, 2021
	Guest editorial: Special issue on mining for health Spiliopoulou, Myra, and Papapetrou, Panagiotis Data Min. Knowl. Discov., 2021
	Assessing the Clinical Validity of Attention-based and SHAP Temporal Explanations for Adverse Drug Event Predictions Rebane, Jonathan, Samsten, Isak, Pantelidis, Panteleimon, and Papapetrou, Panagiotis In 2021 IEEE 34th International Symposium on Computer-Based Medical Systems (CBMS), 2021
	Detecting Adverse Drug Events from Swedish Electronic Health Records using Text Mining Bampa, Maria, and Dalianis, Hercules In Proceedings of the LREC 2020 Workshop on Multilingual Biomedical Text Processing (MultilingualBIO 2020), 2020
	Mitigating discrimination in clinical machine learning decision support using algorithmic processing techniques Briggs, Emma, and Hollmén, Jaakko In International Conference on Discovery Science, 2020
	Machine learning methods for neonatal mortality and morbidity classification Jaskari, Joel, Myllärinen, Janne, Leskinen, Markus, Rad, Ali Bahrami, Hollmén, Jaakko, Andersson, Sture, and Särkkä, Simo IEEE Access, 2020
	A Clustering Framework for Patient Phenotyping with Application to Adverse Drug Events Bampa, Maria, Papapetrou, Panagiotis, and Hollmén, Jaakko In International Symposium on Computer-Based Medical Systems (CBMS), 2020
	Mining Disproportional Frequent Arrangements of Event Intervals for Investigating Adverse Drug Events Lee, Zed, Rebane, Jonathan, and Papapetrou, Panagiotis In International Symposium on Computer-Based Medical Systems (CBMS), 2020
	Mining and Model Understanding on Medical Data Spiliopoulou, Myra, and Papapetrou, Panagiotis In International Conference on Knowledge Discovery & Data Mining (KDD), 2019
	FISUL: A Framework for Detecting Adverse Drug Events from Heterogeneous Medical Sources Using Feature Importance Allaart, Corinne G., Mondrejevski, Lena, and Papapetrou, Panagiotis In International Conference on Artificial Intelligence Applications and Innovations (AIAI), 2019
	Clustering Diagnostic Profiles of Patients Hollmén, Jaakko, and Papapetrou, Panagiotis In International Conference on Artificial Intelligence Applications and Innovations (AIAI), 2019
	Discovering, selecting and exploiting feature sequence records of study participants for the classification of epidemiological data on hepatic steatosis Hielscher, Tommy, Volzke, Henry, Papapetrou, Panagiotis, and Spiliopoulou, Myra In Symposium on Applied Computing (SAC), 2018