Pobiruchin Monika, Zowalla Richard, Kurscheidt Maximilian, Schramm Wendelin
GECKO Institute, Heilbronn University, Heilbronn, Germany.
Center for Machine Learning (ZML), Heilbronn University, Heilbronn, Germany.
Stud Health Technol Inform. 2020 Jun 26;272:151-154. doi: 10.3233/SHTI200516.
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.
决策模型（DM），尤其是马尔可夫模型，在新医疗干预措施的经济评估中起着至关重要的作用。DM生成过程需要医学领域的专业知识，并且是一项耗时的任务。因此，作者提出了一种新的模型生成软件PrositNG，它可以连接到真实世界常规护理数据的数据库系统。该模型的结构借助机器学习算法从数据库系统中的条目中导出。该软件是用Java编程语言实现的。成功利用了两个数据源来证明PrositNG的价值。然而，要使用真实世界数据进行模型生成，深入了解本地文档编制流程和软件至关重要。
