Work

The use of an explanation algorithm in a clinical event monitor.

Year: 1999

Type: conference-paper

Abstract: Clinical event monitors (CEMs) seek to improve patient care and reduce its cost by prompting clinicians to take actions that have these effects. To persuade clinicians to act, CEMs have used prewritten-text explanations. However, we encountered limitations of prewritten-text explanations in our CEM. Therefore, we decided to implement an advanced method for explanation (Suermondt's method for belief-network explanation). This method is promising, but whether it is generally applicable to all of clinical event monitoring and whether it is as efficacious as prewritten-text explanations remain areas for future research.

Source: PubMed

Authors: W R Hogan, M M Wagner

Institution: University of Pittsburgh

Language: English

Topic: Explainable Artificial Intelligence (XAI)

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: green

Funder: U.S. National Library of Medicine

Award: BELIEF NETWORK BASED REMINDER SYSTEMS THAT LEARN