Healthy code, healthy patients
11 Jun 2019Anyone who has ever coded even a simple script has likely experienced the pure excitement of seeing their program run for the first time without errors. For Data Scientists in particular, the satisfaction of successfully building and training a machine learning model is probably unrivalled.
This is particularly true in medical data science: the thrill of data-driven problem solving is exponentially amplified by the awareness that the predictions of the model might actually help doctors make more informed and personalized decisions. However, it is no secret that applying data science in medicine can be intimidating at first. When the lives of patients are on the line, any program built to be used in a clinical setting –whether it is a prediction model or a simpler analytical tool– must be of the highest possible quality. This is a big challenge, especially in diverse teams made up of developers, doctors, and data scientists, all with different coding skills.
In this article, which I wrote for Pacmed’s Medium page, I will share some insight on what it takes to write high quality code for medical applications, specifically keeping in mind the challenges of working in a heterogenous team. I will outline the main steps we take to ensure that the code driving medical data-driven products is of the highest quality, and that the software development process, from inception to end user studies, is smooth and foolproof.
The first part covers repository structure, IDEs, version control, and virtual environments. The second part focuses on how to write good code - from style and syntax to unit and integration tests.
You can find the full article at the following links: