An Explainable AI System for the Diagnosis of High-Dimensional Biomedical Data

Author:

Ultsch Alfred1,Hoffmann Jörg2ORCID,Röhnert Maximilian A.3ORCID,von Bonin Malte3,Oelschlägel Uta3,Brendel Cornelia2ORCID,Thrun Michael C.12ORCID

Affiliation:

1. Databionics, Computer Science, Philipps-University Marburg, Hans-Meerwein-Straße 6, 35032 Marburg, Germany

2. Department of Hematology, Oncology and Immunology, Philipps-University Marburg, Baldinger Straße, 35041 Marburg, Germany

3. Department of Internal Medicine I, University Hospital Carl Gustav Carus, 01307 Dresden, Germany

Abstract

Typical state-of-the-art flow cytometry data samples typically consist of measures of 10 to 30 features of more than 100,000 cell “events”. Artificial intelligence (AI) systems are able to diagnose such data with almost the same accuracy as human experts. However, such systems face one central challenge: their decisions have far-reaching consequences for the health and lives of people. Therefore, the decisions of AI systems need to be understandable and justifiable by humans. In this work, we present a novel explainable AI (XAI) method called algorithmic population descriptions (ALPODS), which is able to classify (diagnose) cases based on subpopulations in high-dimensional data. ALPODS is able to explain its decisions in a form that is understandable to 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 with a selection of state-of-the-art XAI systems shows that ALPODS operates efficiently on known benchmark data and on everyday routine case data.

Funder

UKGM (University Clinic Giessen and Marburg) cooperation

Publisher

MDPI AG

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