Next: Introduction
Temporal Reasoning for Diagnosis in a Causal Probabilistic Knowledge Base
William Long
MIT Lab for Computer Science
545 Technology
Square, Room 420A
Cambridge, MA 02139, USA
wjl@mit.edu
FAX:
617-258-8682
Phone: 617-253-3508
Reprinted from
Artificial Intelligence in Medicine, 8:193-215, 1996
Abstract:
We have added temporal reasoning to the Heart Disease Program
(HDP) to take advantage of the temporal constraints inherent in
cardiovascular reasoning. Some processes take place over minutes while
others take place over months or years and a strictly probabilistic
formalism can generate hypotheses that are impossible given the temporal
relationships involved. The HDP has temporal constraints on the causal
relations specified in the knowledge base and temporal properties on the
patient input provided by the user. These are used in two ways. First,
they are used to constrain the generation of the pre-computed causal
pathways through the model that speed the generation of hypotheses.
Second, they are used to generate time intervals for the instantiated
nodes in the hypotheses, which are matched and adjusted as nodes are
added to each evolving hypothesis.
This domain offers a number of challenges for temporal
reasoning. Since the nature of diagnostic reasoning is inferring a
causal explanation from the evidence, many of the temporal intervals
have few constraints and the reasoning has to make maximum use of those
that exist. Thus, the HDP uses a temporal interval representation that
includes the earliest and latest beginning and ending specified by the
constraints. Some of the disease states can be corrected but some of
the manifestations may remain. For example, a valve disease such as
aortic stenosis produces hypertrophy that remains long after the valve
has been replaced. This requires multiple time intervals to account for
the existing findings.
This paper discusses the issues and solutions that have been
developed for temporal reasoning integrated with a pseudo-Bayesian
probabilistic network in this challenging domain for diagnosis.
- Keywords:
- temporal reasoning, causality, Bayesian probability
networks, physiologic causality, constraint reasoning, diagnosis, heart
disease
Next: Introduction