An open access medical knowledge base for community driven diagnostic decision support system development.

ABSTRACT:

Introduction

While early diagnostic decision support systems were built around knowledge bases, more recent systems employ machine learning to consume large amounts of health data. We argue curated knowledge bases will remain an important component of future diagnostic decision support systems by providing ground truth and facilitating explainable human-computer interaction, but that prototype development is hampered by the lack of freely available computable knowledge bases.

Methods

We constructed an open access knowledge base and evaluated its potential in the context of a prototype decision support system. We developed a modified set-covering algorithm to benchmark the performance of our knowledge base compared to existing platforms. Testing was based on case reports from

SUBMITTER: Muller L 

PROVIDER: S-EPMC6486985 | biostudies-literature | 2019 Apr

REPOSITORIES: biostudies-literature