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In order to better diagnose and individually treat patients with asthma and chronic obstructive pulmonary disease (COPD), the influencing factors of the clinical pictures and their interactions should be understood more effectively. Using artificial intelligence and machine learning, multidimensional models will be developed to predict the risk for important clinical outcome parameters.

Asthma and COPD are considered being the most common chronic lung diseases. "Every patient is different and unique". Both disease terms cover different manifestations with

  • ·        different disease-triggering processes in the body
  • ·        differences in measurable parameters such as lung function or values measurable in the blood
  • ·        differences in the response to certain medications and therapies as well as in the long-term course of the disease.

Asthma and COPD - at a first glance, two clearly distinguishable diseases. In recent years, among a more thoroughly understanding of the processes in the body, the influence of external factors, concomitant diseases or characteristics determined in the genetic makeup, it has become increasingly significant that the line between the two cannot always be unequivocal drawn and that some patients can show symptoms of both diseases.

The aim of this project is to make data from healthcare meaningful and usable for further research. The pandemic, triggered by the SARS-CoV-2 pathogen, has demonstarted the importence to obtain a comprehensive picture of the extent, severity, treatment successes (or failures), the influence of preventive measures including vaccination and the associated side effects in a timely manner. This knowledge creates the basis for correct, balanced, informed decisions.

IMPORTANT TO KNOW: CALM-QE combines the expertise of adult and pediatric pneumologists, experts in the field of chronic inflammation, artificial intelligence and modeling, and patients as experts on the literal experience of the disease. The aim is to contribute to better disease control and long-term prognosis and ultimately to an improved quality of life by providing a more accurate picture of the individual patient through more tailored therapy, better identification of individual risk factors and appropriate countermeasures.

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Your contact persons: 

Prof. Dr. med. Harald Renz Email:  
Sabine Feig Email:
Hannah Lemper Email: