Data acquisition and the implications of machine learning in the development of a Clinical Decision Support system

Abstract

The abundance of healthcare data, with the collection of population-wide information in Electronical Medical Records, would be promising for the implementation of products using artificial intelligence and machine learning. This enables development of new advanced software applications for the clinical practice, especially for the large vendors with years long experience in developing medical software application. Nevertheless, the introduction of artificial intelligence and machine learning to the product development process makes the daily life of software engineers more challenging and brings new factors to consider during the development of a product that must meet the high standards of clinical world. This paper describes experience with the software development of a Clinical Decision Support system at Siemens Healthineers. The intention of the project is to build a software platform for the handling of patient longitudinal data and to provide supportive functionalities to the clinician, with application of Machine Learning and Artificial Intelligence methods to deliver relevant information to the user.