PubMed Health⌕ Search
Home/Archive/PubMed

PubMed · 7950022

Turning medical data into decision-support knowledge.

B F Bohren·M Hadzikadic
View original source ↗Export metadata ↓

Abstract

Advances in information collection and analysis are reaching the point of providing physicians with the help of computer-based assistants. These systems will provide rapid second opinions to physicians in a clinical setting as well as assist them in the analysis of large sets of patient descriptions for research purposes. This paper presents INC2.5 as such a decision-support system. INC2.5 extracts information from databases of previously seen patients to build a decision tree which is used to predict the outcome of new patients on a chosen variable. The concept of matching new patients with the most similar previously seen patient, on which INC2.5 is based, can be easily understood by its users. Further adding to INC2.5's ease of use is its flexibility in allowing users to customize decision trees to their liking. In order to convey the uncertainty of the environment, INC2.5 presents all decisions with a confidence factor.

Explore related subjects

ClassificationDatabases, FactualDecision Making, Computer-AssistedDecision Support TechniquesDecision TreesNumerical Analysis, Computer-AssistedJournal Article

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

B F Bohren, M Hadzikadic. 1994. Turning medical data into decision-support knowledge.. https://pubmed.ncbi.nlm.nih.gov/7950022/

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related citations

PubMed2007

Development of an ecologic marine classification in the new zealand region.

We describe here the development of an ecosystem classification designed to underpin the conservation management of marine environments in the New Zealand region. The classification was defined using multivariate classification using explicit environmental layers chosen for their role in driving spatial variation in biologic patterns: depth, mean annual solar radiation, winter sea surface temperature, annual amplitude of sea surface temperature, spatial gradient of sea surface temperature, summer sea surface temperature anomaly, mean wave-induced orbital velocity at the seabed, tidal current velocity, and seabed slope. All variables were derived as gridded data layers at a resolution of 1 km. Variables were selected by assessing their degree of correlation with biologic distributions using separate data sets for demersal fish, benthic invertebrates, and chlorophyll-a. We developed a tuning procedure based on the Mantel test to refine the classification's discrimination of variation in biologic character. This was achieved by increasing the weighting of variables that play a dominant role and/or by transforming variables where this increased their correlation with biologic differences. We assessed the classification's ability to discriminate biologic variation using analysis of similarity. This indicated that the discrimination of biologic differences generally increased with increasing classification detail and varied for different taxonomic groups. Advantages of using a numeric approach compared with geographic-based (regionalisation) approaches include better representation of spatial patterns of variation and the ability to apply the classification at widely varying levels of detail. We expect this classification to provide a useful framework for a range of management applications, including providing frameworks for environmental monitoring and reporting and identifying representative areas for conservation.

Classification↗
PubMed2007

SINEs of progress: Mobile element applications to molecular ecology.

Mobile elements represent a unique and under-utilized set of tools for molecular ecologists. They are essentially homoplasy-free characters with the ability to be genotyped in a simple and efficient manner. Interpretation of the data generated using mobile elements can be simple compared to other genetic markers. They exist in a wide variety of taxa and are useful over a wide selection of temporal ranges within those taxa. Furthermore, their mode of evolution instills them with another advantage over other types of multilocus genotype data: the ability to determine loci applicable to a range of time spans in the history of a taxon. In this review, I discuss the application of mobile element markers, especially short interspersed elements (SINEs), to phylogenetic and population data, with an emphasis on potential applications to molecular ecology.

Classification↗