Our new article has been accepted in Systems Medicine:
Riemenschneider R, Wienbeck J, Scherag A, Heider D: Data Science for MDx Applications: From Academia to Clinic to Industry. Systems Medicine 2018, in press.
The development of computational approaches for predictive modeling of diseases, e.g., in cancer diagnostics or prognostics, or drug resistance predictions in infectious diseases, has opened a new era in precision medicine. Clinical decision-support-systems have been designed for assistance in molecular diagnostics (MDx) or companion diagnostics (CDx) to enhance therapeutic success. These systems are typically based on statistical or machine learning models, built upon clinical or corporate patient data. However, these software systems are not ready-to-use in MDx or CDx settings — due to regulatory requirements, such as specific ISO standards, which are needed for regulatory approval. As these standards are generally not fulfilled in academic software, there is currently only a limited use of such systems in clinical routine. Due to the fact that academia cannot solve this problem on its own, we propose a scheme where the three key players, i.e., academia, clinics, and industry, work more closely together in software development processes for medical devices.