Modelling dynamical comorbidity networks from longitudinal health-care data

PhD Thesis



Patients become increasingly multimorbid with age leading to decreased quality of life, increased need for hospitalizations, health care utilization, mortality, and care costs. However, the typical population-scale disease trajectories along which patients become multimorbid are not yet fully understood.

We use a unique dataset containing 44 million records of almost all inpatient stays from 2003 to 2014 in Austria to investigate these disease trajectories. We developed a new, multilayer disease network approach to quantitatively analyze complex connections between two or more conditions and how they evolve over the life course of patients. Nodes represent diagnoses from the different chapters (cancer, respiratory diseases, etc.) in specific age groups in intervals of ten years. Thus, nodes in the constructed network are 3-digits ICD10 codes + 10 years age group, i.e., E66-20-29 represents obesity in the twenties. Links within a layer encode the tendency of two diagnoses to co-occur. While, directed links across layers mean that a given diagnosis is more likely to occur at a specific age, given that another diagnosis was already present at an earlier age.

We used an unsupervised community detection algorithm for detecting overlapping communities of diagnoses within and across layers of the multilayer comorbidity network to reveal the most common disease trajectories [].

Our multilayer comorbidity network approach shows how to identify critical events that put patients at high risk for different diagnoses decades later, according to different combinations of risk factors. These results could support clinical decision-making and allow a more personalized medicine approach that could be integrated into daily clinical practice.


  • Elma Dervic*, Carola Deischinger*, Nils Haug, Michael Leutner, Alexandra KautzkyWiller, Peter Klimek, et al. The effect of cardiovascular comorbidities on women compared to men: Longitudinal retrospective analysis. JMIR cardio, 5(2):e28015, 2021, DOI: 10.2196/28015
  • Carola Deischinger*, Elma Dervic*, Michael Leutner, Lana Kosi-Trebotic, Peter Klimek, Alexander Kautzky, and Alexandra Kautzky-Willer. Diabetes mellitus is associated with a higher risk for major depressive disorder in women than in men. BMJ Open Diabetes Research and Care, 8(1):e001430, 2020, DOI: 10.1136/bmjdrc-2020-001430
  • Michael Leutner*, Elma Dervic*, Luise Bellach, Peter Klimek, Stefan Thurner, Alexandra Kautzky-Willer, and Kautzky Alexander. Obesity as pleiotropic risk state for metabolic and mental health throughout life. Translational psychiatry, Under revision
  • Elma Dervic, Michael Leutner, Alexander Kautzky, Alexandra Kautzky-Willer, Stefan Thurner, and Peter Klimek. Unraveling cradle-to-grave disease trajectories from multilayer comorbidity networks, Not published
  • Carola Deischinger*, Elma Dervic*, Stephan Nopp, Michaela Kaleta, Peter Klimek, Alexander Kautzky, and Alexandra Kautzky-Willer. Diabetes mellitus is associated with a higher relative risk for venous thromboembolism in females than in males. Diabetes research and clinical practice, Under revision
  • Carola Deischinger, Elma Dervic, Michaela Kaleta, Peter Klimek, and Alexandra Kautzky-Willer. Diabetes mellitus is associated with a higher relative risk for parkinson’s disease in women than in men. Journal of Parkinson’s Disease, 11(2):793–800, 2021, DOI: 10.3233/JPD-202486
  • Michael Leutner, Nils Haug, Luise Bellach, Elma Dervic, Alexander Kautzky, Peter Klimek, and Alexandra Kautzky-Willer. Risk of typical diabetes-associated complications in different clusters of diabetic patients: Analysis of nine risk factors. Journal of personalized medicine, 11(5):328, 2021,DOI: 10.3390/jpm11050328
  • Amelie Desvars-Larrive, Elma Dervic, Nils Haug, Thomas Niederkrotenthaler, Jiaying Chen, Anna Di Natale, Jana Lasser, Diana S Gliga, Alexandra Roux, Johannes Sorger, et al. A structured open dataset of government interventions in response to covid-19. Scientific data, 7(1):1–9, 2020, DOI: 10.1038/s41597-020-00609-9
  • Nils Haug*, Lukas Geyrhofer*, Alessandro Londei*, Elma Dervic, Amelie DesvarsLarrive, Vittorio Loreto, Beate Pinior, Stefan Thurner, and Peter Klimek. Ranking the effectiveness of worldwide covid-19 government interventions. Nature human behaviour, 4(12):1303–1312, 2020, DOI: 10.1038/s41562-020-01009-0
  • Jana Lasser, Johannes Zuber, Johannes Sorger, Elma Dervic, Katharina Ledebur, Simon David Lindner, Elisabeth Klager, Maria Kletečka-Pulker, Harald Willschke, Katrin Stangl, et al. Agent-based simulations for protecting nursing homes with prevention and vaccination strategies. Journal of the Royal Society Interface, 18(185):20210608, 2021, DOI: 10.1098/rsif.2021.0608
  • Michaela Kaleta, Jana Lasser, Elma Dervic, Liuhuaying Yang, Johannes Sorger, Ruggiero Lo Sardo, Stefan Thurner, Alexandra Kautzky-Willer, and Peter Klimek. Stress-testing the resilience of the austrian healthcare system using agent-based simulation. Nature communications, 13(1):1–10, 2022, DOI: 10.1038/s41467-022-31766-7


This study was supported financially by the WWTF “Mathematics and...” Project MA16-045.


Research by Dervic Elma, Carola Deischinger, Leutner Michael, Kautzky Alexander, Johannes Sorger, Kautzky-Willer Alexandra, Klimek Peter.