Building an explanation function for a hypertension decision-support system.
ATHENA DSS is a decision-support system that provides recommendations for managing hypertension in primary care. ATHENA DSS is built on a component-based architecture called EON. User acceptance of a system like this one depends partly on how well the system explains its reasoning and justifies its conclusions. We addressed this issue by adapting WOZ, a declarative explanation framework, to build an explanation function for ATHENA DSS. ATHENA DSS is built based on a component-based architecture called EON. The explanation function obtains its information by tapping into EON's components, as well as into other relevant sources such as the guideline document and medical literature. It uses an argument model to identify the pieces of information that constitute an explanation, and employs a set of visual clients to display that explanation. By incorporating varied information sources, by mirroring naturally occurring medical arguments and by utilizing graphic visualizations, ATHENA DSS's explanation function generates rich, evidence-based explanations.
Shankar
R D
RD
Stanford Medical Informatics, Stanford University School of Medicine, Stanford, California 94305-5479, USA. shankar@smi.stanford.edu
Martins
S B
SB
Tu
S W
SW
Goldstein
M K
MK
Musen
M A
MA
D001185:Artificial Intelligence; D020000:Decision Support Systems, Clinical; D019317:Evidence-Based Medicine; D006801:Humans; D006973:Hypertension / Q000628:therapy*; D016347:Medical Records Systems, Computerized; D017410:Practice Guidelines as Topic; D013813:Therapy, Computer-Assisted