PubMed Health⌕ Search

Biomedical subjects

G Hripcsak

Publications and source records attributed to G Hripcsak.

5 recordsLinked to original sources
PubMed1991

The Columbia-Presbyterian Medical Center decision-support system as a model for implementing the Arden Syntax.

Columbia-Presbyterian Medical Center is implementing a decision-support system based on the Arden Syntax for Medical Logic Modules (MLM's). The system uses a compiler-interpreter pair. MLM's are first compiled into pseudo-codes, which are instructions for a virtual machine. The MLM's are then executed using an interpreter that emulates the virtual machine. This design has resulted in increased portability, easier debugging and verification, and more compact compiled MLM's. The time spent interpreting the MLM pseudo-codes has been found to be insignificant compared to database accesses. The compiler, which is written using the tools "lex" and "yacc," optimizes MLM's by minimizing the number of database accesses. The interpreter emulates a stack-oriented machine. A phased implementation of the syntax was used to speed the development of the system.

Artificial Intelligence↗
PubMed1991

Conceptual data model for a central patient database.

This paper presents methods used to develop a conceptual model for a patient database forming the centerpiece of a clinical information system under development. Various modeling techniques are discussed using a simplified fragment of the model. A method for mapping the model onto a relational design optimized for single patient retrievals is described. The results section discusses a number of issues pertaining to the flexibility and usability of this architecture.

Databases, Factual↗
PubMed1990

Using connectionist modules for decision support.

A connectionist model for decision support was constructed out of several back-propagation modules. Manifestations serve as input to the model; they may be real-valued, and the confidence in their measurement may be specified. The model produces as its output the posterior probability of disease. The model was trained on 1,000 cases taken from a simulated underlying population with three conditionally independent manifestations. The first manifestation had a linear relationship between value and posterior probability of disease, the second had a stepped relationship, and the third was normally distributed. An independent test set of 30,000 cases showed that the model was better able to estimate the posterior probability of disease (the standard deviation of residuals was 0.046, with a 95% confidence interval of 0.046-0.047) than a model constructed using logistic regression (with a standard deviation of residuals of 0.062, with a 95% confidence interval of 0.062-0.063). The model fitted the normal and stepped manifestations better than the linear one. It accommodated intermediate levels of confidence well.

Computer Communication Networks↗