SommerUni at AG Araldi

As part of the summer school programme “SommerUni”, from July 7th to July 11th, 2024 a group of upper secondary school students visited the DIASyM PhD students from AG Araldi to gain hands-on experience in the data analysis of clinical studies. The program introduced them to the fundamentals of data analysis, statistics and clinical studies, including a visit to the Gutenberg Health Study (GHS) center as well as the laboratory facilities. The students learned about data management processes and explored the topic of heart failure from a clinical perspective.
A highlight of the week was analyzing a publicly available dataset by G. Jurman and D. Chicco on patient survival in heart failure (original study by Ahmad and colleagues). It provided insights into key parameters involved in the development of heart failure.
The students also received training in creating and presenting scientific posters. Their hard work culminated in an impressive second-place finish in the poster session, reflecting their newly acquired skills and critical understanding of clinical data analysis. The program was a resounding success, offering students a valuable and practical introduction to this fascinating field.

Source:
https://www.unimedizin-mainz.de/sommeruni/start.html

Chicco, D., Jurman, G. Machine learning can predict survival of patients with heart failure from serum creatinine and ejection fraction alone. BMC Med Inform Decis Mak 20, 16 (2020). https://doi.org/10.1186/s12911-020-1023-5

Ahmad T, Lund LH, Rao P, Ghosh R, Warier P, Vaccaro B, Dahlström U, O'Connor CM, Felker GM, Desai NR. Machine Learning Methods Improve Prognostication, Identify Clinically Distinct Phenotypes, and Detect Heterogeneity in Response to Therapy in a Large Cohort of Heart Failure Patients. J Am Heart Assoc. 2018 Apr 12;7(8):e008081. doi: 10.1161/JAHA.117.008081. PMID: 29650709; PMCID: PMC6015420.