Abstract
This paper introduces the Greg, ML platform, a machine-learning engine and toolset conceived to generate automatic diagnostic suggestions based on patient profiles. Greg, ML departs from many other experiences in machine learning for healthcare in the fact that it was designed to handle a large number of different diagnoses, in the order of the hundreds. We discuss the architecture that stands at the core of Greg, ML, designed to handle the complex challenges posed by this ambitious goal, and confirm its effectiveness with experimental results based on the working prototype we have developed. Finally, we discuss challenges and opportunities related to the use of this kind of tools in medicine, and some important lessons learned while developing the tool. In this respect, we underline that Greg, ML should be conceived primarily as a support for expert doctors in their diagnostic decisions, and can hardly replace humans in their judgment.
Highlights
The push for the widespread adoption of digital records and digital reports in medicine [11, 17] is paving the ground for new applications that would not be conceivable a few years ago.This paper presents one of these applications, called Greg, ML
The main novelty of the platform we propose in this paper, and its main contribution, is the fact that it has been conceived to address automatic diagnostic suggestions on a large scale
We believe that Greg, ML can be a valid and useful tool to assist doctors in the diagnostic process
Summary
The push for the widespread adoption of digital records and digital reports in medicine [11, 17] is paving the ground for new applications that would not be conceivable a few years ago. A spin-off of the data-management group at University of Basilicata It is a machine-learning-based platform for generating automatic diagnostic suggestions based on patient profiles. All of the existing tools concentrate on rather specific learning tasks, for example identifying a single pathology – like heart disease [25, 30], or pneumonia [26], or cancer. For these very focused efforts, results of remarkable quality have been reported [31]. In the remainder of this section, we will describe the Greg, ML’s architecture more in details
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