Biomedical subjects

M Shwe

Publications and source records attributed to M Shwe.

3 recordsLinked to original sources

PubMed1992

The SQLX system: generating explanations for clinical rules encoded in SQL.

We have developed a system to provide case-specific explanations for SQL query results. The explanation facility, called SQLX, is intended to improve user acceptance of clinical rules that are encoded in the SQL query language and are triggered by updates to a relational database. Our design goal is to combine the efficiency and familiarity of the SQL query model with a more intuitive and case-specific display of query results. SQLX defines an augmented SQL syntax that allows the incorporation of explanation text directly into query specifications. A query analyzer determines which retrieved data logically contribute to rule firing, and combines the data with explanation text to generate a case-specific explanation. Although features of the SQL query model limit the use of this method as a general-purpose rule-explanation facility, the current design accommodates a wide range of SQL query formulations and can provide efficient processing and intuitive explanations of many clinical rules.

Anesthesiology↗

PubMed1992

Reuse of knowledge represented in the Arden syntax.

Knowledge Data Systems is building a medical expert system for monitoring clinical events. This system uses the Arden syntax as a knowledge representation. Having encoded may different types of rules in the Arden syntax, we have noticed a number of shortcomings of the syntax. Many of these shortcomings originate from Arden's procedural orientation, from its failure to separate factual medical knowledge from knowledge of how the medical facts should be applied to a particular clinical situation. The absence of this separation leads to redundancy of knowledge and to difficulties in knowledge reuse. We suggest that standards for representing medical logic preserve this separation to engender knowledge reuse. We propose a general framework for representing medical logic which supports both knowledge sharing and reuse.

Diagnosis, Computer-Assisted↗

PubMed1991

An empirical analysis of likelihood-weighting simulation on a large, multiply connected medical belief network.

We are developing a probabilistic reformulation of the Quick Medical Reference (QMR) system. Our current probabilistic model of the QMR knowledge base of internal medicine consists of a two-level, multiply connected, belief network. Because of the size and connectivity of this belief network, most exact algorithms for calculating the posterior marginal probabilities of diseases are not applicable. In this paper, we analyze the convergence properties of an approximation algorithm, called likelihood-weighting simulation, on the QMR-DT belief network. Specifically, on two difficult diagnostic cases, we examine the effects of Markov blanket scoring, importance sampling, and self-importance sampling, demonstrating that the Markov blanket scoring and self-importance sampling significantly improve the convergence of the simulation on our model.

Algorithms↗