Teaching
Data-driven Health
I am the Program Director of the Master’s Program in Data-driven Health at KTH Royal Institute of Technology. The program started in Fall 2025, is highly competetive with an acceptance rate of <10%.
Taught Courses
- Healthcare Ethics, Legal and Social Impact. Given for the first time in fall 2025. For a description of the course, and the reading material, see here.
- Applied Machine Learning and Data Mining CM1001. Given every spring since 2020.
- Applied Machine Learning and Data Mining for Performance Analysis CM2007. Given every spring since 2020.
- Applied Machine Learning and Artificial Intelligence CM2011. Given every spring since 2021.
PhD Students
Current Students
- Andrew Dwi Permana
- Mikaela Hellstrand
- Gibril Jarzue
- Merja Hietanen
Past Students
Arsineh Boodaghian Asl. (2025). Network-Agnostic Computational Approaches for Modelling and Validating Evolving Complex Systems [KTH Royal Institute of Technology].
Luca Marzano. (2024). Perspectives on designing data-driven approaches in healthcare based on real-world evidence [KTH Royal Institute of Technology].
Master’s Thesis Students
Karwacki, J. (2025). Development of a Word Embedding adapted to Swedish Medical Terms.
Eivinsson, T. (2024). Utilizing Primary Health Care Data for Early Detection of Colorectal Cancer: A Machine Learning Approach.
Sadegh Bozorgi, S. (2024). Natural Language Processing of Primary Care Data for Early Detection of Colorectal Cancer.
Costa, R. A. S. (2024). Exploring Innovative Ways to enhance Multiple Criteria Decision Analysis and MACBETH in Health Settings with Big Data and Data Intelligence.
Carrera Jeri, P. (2023). Risk Stratification of Endometriosis through Machine Learning using Lifestyle Data: An Extensive Analysis on Lifestyle Data to Reveal Patterns in People with Endometriosis.
Jefford-Baker, B. (2022). Autonomous Patient Monitoring in the Intermediate Care Unit by Live Video Analysis.
Lindberg, T. (2022). Early Detection and Differentiation of Circulatory Shock in the Intensive Care Unit using Machine Learning.
Malm, E. (2022). Machine Learning for Early Prediction of Pneumothorax in the Intensive Care Unit.
Rosamilia, U. (2022). Applying Nonlinear Mixed-Effects Modeling to Model Patient Flow in the Emergency Department: Evaluation of the Impact of Patient Characteristics on Emergency Department Logistics.
Wadhwa, R. (2019). Systems mapping ofwork-stress mental health inStockholm to inform policydecision making.
Dizdarevic, S., & Hämäläinen, A. (2018). Developing a simulation model for decision making in a further digitized Swedish healthcare system.
Skoglund, P., & Peterson, T. (2018). Development of a Simulation Platform Addressing the Digitalization of the Stockholm Healthcare System.
Nilsson Hall, R., & Jerjas, A. (2017). Specifying an ontology framework to model processes in hospitals.
Bachelor’s Thesis Students
Ojanne, B., & Springer, S. (2025). Efterkorrigering av OCR-genererad text från svenska historiska dokument med hjälp av språkmodeller: En utvärderande studie: Analys av svenska historiska texter med hjälp av OCR, NLP och LLM i samverkan: En modern metod för att tolka det förflutna.
Stylbäck, J., & Villför, E. (2023). Endometriosis and Its Correlation with Lifestyle Factors and Health Indicators: A Data Mining Approach Using R and Python.
Balachandran, S., & Perez Legrand, D. (2023). Evaluating machine learning models for time series forecasting in smart buildings.
Höstklint, N., & Larsson, J. (2021). Dynamic Test Case Selection using Machine Learning.
Ingemarsson, M., & Henningsson, J. (2022). Evaluating the effects of data augmentations for specific latent features: Using self-supervised learning.
Nordlund, A., & Ålander, N. (2019). Forecast Modelling of Future Events that Affect the Repayment Capacity of Mortgages.