Pobiruchin M, Zowalla R, Kurscheidt M, Schramm W. PrositNG - A Machine Learning Supported Disease Model Generation Software. Stud Health Technol Inform. 2020 Jun 26;272:151-154. doi: 10.3233/SHTI200516. PMID: 32604623
Decision models (DM), especially Markov Models, play an essential role in the economic evaluation of new medical interventions. The process of DM generation requires expert knowledge of the medical domain and is a time-consuming task. Therefore, the authors propose a new model generation software PrositNG that is connectable to database systems of real-world routine care data. The structure of the model is derived from the entries in a database system by the help of Machine Learning algorithms. The software was implemented with the programming language Java. Two data sources were successfully utilized to demonstrate the value of PrositNG. However, a good understanding of the local documentation routine and software is paramount to use real-world data for model generation.
Keywords: electronic health records; machine learning; markov processes; medical economics; real-world data.