Big Data in Medicine: Discover revolutionary ways to analyze data!

Big Data in Medicine: Discover revolutionary ways to analyze data!

The Winter School “Medical Data Analysis for Young Scientists”, which took place in Brandenburg an der Havel, was an important event for the aspiring doctors at the Brandenburg Theodor Fontane Medical School (MHB). Organized by Dennis Wagner, a medical informatics specialist at the MHB, and Prof. Dr. med. Thomas Schrader from the Brandenburg University of Technology, the event lasted five days and offered participants valuable insights into the basics of data analysis.

The five days in total covered a variety of topics. The first day covered the basics of data analysis using Python and R, supported by live coding sessions. The second day focused on exploratory data analysis (EDA), including visualization techniques and statistical methods for pattern recognition. Participants also learned the importance of data quality and its cleansing, which was discussed on day three. The fourth day focused on examining correlations and developing forecast models. Finally, the last day enabled the participants to develop a complete analysis pipeline from data preparation to presentation of results. In addition, all participants received a certificate, which demonstrated the positive response expressed by medical student Jonas Wördemann.

Data analysis in medicine and its challenges

Analyzing medical data today is complex and increasingly complicated by the use of electronic health records (EHR). Dr. Emily Rodriguez and her team at Massachusetts General Hospital are working to analyze this data, which is proving difficult to use. Using Python, they strive to overcome the challenges of using this data. In fact, about 58% of studies have difficulty with the reproducibility of their data processing, which is why pre-processing of data, which takes up about 80% of the work, is crucial for accurate insights.

EHR data includes demographics, clinical observations, laboratory results, treatment histories, and diagnostic procedures. The challenges of analyzing this data are varied, from cleaning up missing and inconsistent entries to transforming the data in preparation for analysis. Methods such as using Python tools such as Pandas, Matplotlib and Seaborn for exploratory data analysis are becoming increasingly important.

Big data in health research

Another focus of the Winter School was the topic of Big Data, which is becoming increasingly important in health research. This includes large, complex data sets that cannot be handled using traditional methods. Big data enables the identification of patterns and trends that can be used for personalized treatment plans. Techniques such as machine learning and statistical analysis play a crucial role here.

Analysis of EHR data can also lead to the development of techniques such as predictive analytics, which uses historical data to predict future events. Important methods of data analysis include both supervised and unsupervised learning methods. Technological advances in data analytics, including NoSQL databases and cloud computing, are helping to increase the efficiency of analyzing large data sets. At the same time, however, data protection and ethical challenges also need to be addressed.

Given the positive response to the Winter School, the organizers are planning a continuation in the form of a Summer School in September. This time the focus will be on machine learning and artificial intelligence in medical data analysis. The exact dates and availability of places will be announced later.