1 research outputs found
An Explainable AI System for the Diagnosis of High Dimensional Biomedical Data
Typical state of the art flow cytometry data samples consists of measures of
more than 100.000 cells in 10 or more features. AI systems are able to diagnose
such data with almost the same accuracy as human experts. However, there is one
central challenge in such systems: their decisions have far-reaching
consequences for the health and life of people, and therefore, the decisions of
AI systems need to be understandable and justifiable by humans. In this work,
we present a novel explainable AI method, called ALPODS, which is able to
classify (diagnose) cases based on clusters, i.e., subpopulations, in the
high-dimensional data. ALPODS is able to explain its decisions in a form that
is understandable for human experts. For the identified subpopulations, fuzzy
reasoning rules expressed in the typical language of domain experts are
generated. A visualization method based on these rules allows human experts to
understand the reasoning used by the AI system. A comparison to a selection of
state of the art explainable AI systems shows that ALPODS operates efficiently
on known benchmark data and also on everyday routine case data.Comment: 22 pages, 1 figure, 5 table