Computer programs in biomedicine最新文献

A program has been written to determine T-score profile patterns, thus allowing the immediate assessment of morphological similarities and differences between taxa. For a given data set, the program will produce T-scores, T-score correlation coefficients and associated significance statistics, weighted means and standard deviations for all input variables, and a graphic display of the T-score profile pattern(s). This program should prove to be of great interest to physical anthropologists, zoologists and other researchers in the life sciences with an interest in taxonomy, systematics and phylogeny.

It has been argued that the problem of medical diagnosis is fundamentally ill-structured, particularly during the early stages when the number of possible explanations for presenting complaints can be immense. This paper discusses the process of clinical hypothesis evocation, contrasts it with the structured decision making approaches used in traditional computer-based diagnostic systems, and briefly surveys the more open-ended reasoning methods that have been used in medical artificial intelligence (AI) programs. The additional complexity introduced when an advice system is designed to suggest management instead of (or in addition to) diagnosis is also emphasized. Example systems are discussed to illustrate the key concepts.

A calculator program that performs a nonlinear least-squares fit to data conforming to the one-compartment model with zero-order input is described. The program, which is designed for the Hewlett-Packard HP-41 CV calculator, is based on the Gauss-Newton iterative algorithm as modified by Hartley. A subroutine for calculation of initial parameter estimates is incorporated into the program. Plasma concentration data relative to a single oral dose of a sustained-release theophylline formulation are used to demonstrate the practical application of the program.

A new program ELSMOS has been developed for curve fitting of pharmacokinetic and pharmacodynamic data. The form is sufficiently general, with user-defined model in a separate FORTRAN subroutine, to permit application in many other areas. The principal features are the use of a user-defined error model for statistically rigorous automatic weighting of data, and the containment of program size to a level that enables mini- or micro-computer implementation.

A computer program package for parametric and nonparametric linear system identification of both static and dynamic biological data, written for an LSI-11 minicomputer with 28 K of memory, is described. The program has 11 possible commands including an instructional help command. A user can perform nonparametric spectral analysis and estimation of autocorrelation and partial autocorrelation functions of univariate data and estimate nonparametrically the transfer function and possibly an associated noise series of bivariate data. In addition, the commands provide the user the means to derive a parametric autoregressive moving average model for univariate data, to derive a parametric transfer function and noise model for bivariate data, and to perform several model evaluation tests such as pole-zero cancellation, examination of residual whiteness and uncorrelatedness with the input. The program, consisting of a main program and driver subroutine as well as six overlay segments, may be run interactively or automatically.

The traditional approach to computer-assisted medical decision-making involves designing a computer system which simulates a physician's decision-making process. This paper describes a different approach: Medical Plan-Analysis (MPA). Instead of trying to tell a physician how best to manage a patient, an MPA system critiques a physician's management plan. It is anticipated that this approach may have a number of potential social, medical, and medicolegal advantages. The MPA approach has been implemented in ATTENDING, a system designed using Artificial Intelligence techniques to critique a physician's plan for anesthetic management.