Development and Implementation of the Data Science Learning Platform for Research Physician.

Data analysis and their application are the unavoidable factors in the activities analyses in health care. Unfortunately, the acquisition of data from large available medical databases is a complex process and requires deep knowledge of computer science and especially knowledge of tools for data management. According to the European General Data Protection Regulation, the problem becomes much more complex. Recognizing these problems and difficulties, we have developed a Data Science Learning Platform (DSLP) that primarily targets practitioners and researchers but also the computer science students. Using our proposed tool chain together with the developed graphical user interface, data scientists and research physicians will be able to use available medical databases, apply and analyze different anonymization methods, analyze data according to the patient's risk and quickly formulate new studies to target a disease in a complex data model. This article presents a clinical research discovery toolbox that implements and demonstrates tools for data anonymization, patient data visualization, NLP-tools for guideline search and data science learning tools.

A Visualization and Benchmarking Simulator for Clinical Data.

The availability of Big Data has increased significantly in many areas in recent years. Insights from these data sets lead to optimized processes in many industries, which is why understanding as well as gaining knowledge through analyses of these data sets is becoming increasingly relevant. In the medical field, especially in intensive care units, fast and appropriate treatment is crucial due to the usually critical condition of patients. The patient data recorded here is often very heterogeneous and the resulting database models are very complex, so that accessing and thus using this data requires technical background knowledge. We have focused on the development of a web application that is primarily aimed at clinical staff and researchers. It is an easily accessible visualization and benchmarking tool that provides a graphical interface for the MIMIC-III database. The anonymized datasets contained in MIMIC-III include general information about patients as well as characteristics such as vital signs and laboratory measurements. These datasets are of great interest because they can be used to improve digital decision support systems and clinical processes. Therefore, in addition to visualization, the application can be used by researchers to validate anomaly detection algorithms and by clinical staff to assess disease progression. For this purpose, patient data can be individualized through modifications such as increasing and decreasing vital signs and laboratory parameters so that disease progression can be simulated and subsequently analyzed according to the user's specific needs.