Background: Python and MATLAB both are common tools used for predictive modelling applications, not only in healthcare. In our predictive modelling group, both tools are widely used. None of the two tools is optimal for all tasks along the value chain of predictive modelling in healthcare.
Objectives: The aim of this study was to explore different ways to extend our MATLAB-based toolset with Python functions.
Methods: Pre-existing interfaces between MATLAB and Python have been evaluated and more comprehensive interfaces have been designed to exchange even complex data formats such as MATLAB tables.
Results: The interfaces have successfully been implemented and they were validated in a Natural Language Processing scenario based on free-text notes from a telehealth services for heart failure patients.
Conclusion: Integration of Python modules in our MATLAB toolset is possible. Further improvements especially in terms of performance, are required if large datasets need to be exchanged. A big advantage of our concept is that tabular data can be exchanged between MATLAB and Python without loss and the Python functions are called dynamically via the interface.
Keywords: Data exchange; Interfacing; MATLAB; Machine learning; Predictive Analytics; Python.