Exploring disease trajectories and outcome prediction using novel methods in network analysis and machine learning

January 1, 2021
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T2DM is the fasting growing chronic disease worldwide and poses a substantial burden to the patient and healthcare system. Thus, the clinical management of T2DM is of global concern.

However, due to the complex nature of the disease progression, the relationship between comorbidities and glycemic control remain poorly understood. The ability to improve our understanding of the common disease trajectories starting from diagnosis would provide new insights into disease phenotypes, risk factors, and provide opportunities to develop personalized treatment plans. Additionally, one clinical area of concern within diabetes management is the risk of fragility fractures. While patients with T2DM often have normal or even increased BMD, studies consistently show these patients have an increased risk of fragility fracture. While a number of studies have examined common fracture risk factors for fracture, observational and animal studies are conflicting. Thus, through the collaboration with the SDSC we aim to explore new methods to capture the complex and dynamic nature of patient trajectories.

To achieve this aim, this collaboration grant will bring together experts in pharmacoepidemiology, real-world data analytics, social network analysis, and machine learning to develop interpretable models that will serve as an important step towards identifying high-risk patients and subsequently prevent adverse health outcomes. In particular, PolyNet has two primary research objectives to solve the above identified gaps in T2DM care:

  1. To explore new methodologies to characterize and visualize common disease and comorbidity trajectories in patients, and
  2. To develop longitudinal models to address important clinical questions in T2DM – predicting glycemic control changes and fragility fracture risk.

All projects will leverage data from the world’s largest primary care database, the UK Clinical Practice Research Datalink, and will include substantial interaction with the SDSC.



SDSC Team:
Anna Susmelj
Ekaterina Krymova
Fernando Perez-Cruz
Guillaume Obozinski
Izabela Moise
Victor Cohen

PI | Partners:

ETH Zurih, Pharmacoepidemiology Group, Institute of Pharmaceutical Sciences:

  • Prof. Andrea Burden
  • Adrian Martinez de la Torre
  • Maria Luísa Marques de Sá Faquetti

More info

ETH Zurch, Social Network Lab:

  • Prof. Christoph Stadtfeld

More info



The goal above all is to address the question: Can we better understand, and ultimately prevent, the development of complex comorbidities and adverse health events in T2DM? To accomplish this overarching goal, we identified two primary goals:

  • to identify common trajectories of TD2M progression, comorbidity development and medication use over time. In particular, the aim is to understand the interactions.
  • to develop machine learning models predicting changes in glycemic control and fragility fracture risk.


The development of these statistical models will help advances towards personalized healthcare treatment. Indeed, the predictions of these models are based on patient characteristics, and it would help the decision-making process regarding potential outcomes. In addition, this potentially unleashes new possibilities in the use of machine learning techniques for disease trajectory understanding.


The SDSC will help to design exploratory analysis to understand the disease trajectory over time, and will propose machine learning techniques to predict changes in glycemic control and fragility fracture risk.




  • Martinez-De la Torre, A., Perez-Cruz, F., Weiler, S., & Burden, A. M. (2022). Comorbidity clusters associated with newly treated type 2 diabetes mellitus: a Bayesian nonparametric analysis. Scientific Reports, 12(1), 20653
  • Faquetti, M. L., la Torre, A. M. D., Burkard, T., Obozinski, G., & Burden, A. M. (2023). Identification of polypharmacy patterns in new‐users of metformin using the Apriori algorithm: A novel framework for investigating concomitant drug utilization through association rule mining. Pharmacoepidemiology and Drug Safety, 32(3), 366-381.

Additional resources


  1. Sonnenberg, F. A., & Beck, J. R. (1993). Markov models in medical decision making: A practical guide.
    Medical Decision Making: An International Journal of the Society for Medical Decision Making,
    13(4), 322–338. https://doi.org/10.1177/0272989X9301300409
  2. Wang, X., Sontag, D., & Wang, F. (2014). Unsupervised Learning of Disease Progression Models. Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 85–94. https://doi.org/10.1145/2623330.2623754
  3. P. Dworzynski, M. Aasbrenn, K. Rostgaard, M. Melbye, T. A. Gerds, H. Hjalgrim, and T. H. Pers. Nationwide prediction of type 2 diabetes comorbidities. Nature Scientific Report, vol. 10, 2019.
  4. M. Ravaut, H. Sadeghi, K. K. Leung, M. Volkovs, K. Kornas, V. Harish, T. Watson, G. F. Lewis, A. Weisman, T. Poutanen, and L. C. Rosella. Predicting adverse outcomes due to diabetes complications with machine learning using administrative health data.npj Digital Medicine, 4:1-12, 2021.


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