Search PubMed⌕ Search

PubMed · 9929360

Data mining by clinicians.

Abstract

Clinical databases are becoming commonplace in healthcare environments. However, clinicians have been unable to readily explore these information sources, because currently available data retrieval tools require substantial technical skill, as well as knowledge of the underlying database structures. To address this, we have defined a group of "atomic" queries, including both population-based and temporal predicates, to enable the extraction of clinically meaningful information form these databases. DXtractor is an application that incorporates this functionality, and allows clinicians to simply combine these atomic queries. In doing so, arbitrarily complex data retrieval and exploration becomes possible for the non-programming clinician.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

D J Nigrin, I S Kohane. 1998. Data mining by clinicians.. https://pubmed.ncbi.nlm.nih.gov/9929360/

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

KEEP EXPLORING

Related citations

Low-density lipoprotein receptor gene (LDLR) world-wide website in familial hypercholesterolaemia: update, new features and mutation analysis.

Mutations in the low density lipoprotein receptor gene (LDLR) cause familial hypercholesterolaemia (FH). The FH website (http://www.ucl. ac.uk/fh) has been updated to provide various functions enabling the analysis of the large number of LDLR mutations. To date, 683 LDLR mutations have been reported; of these 58.9% are missense mutations, 21.1% minor rearrangements, 13.5% major rearrangements and 6.6% splice site mutations. Of the 402 missense mutations, only 11.4% occurred at CpG sites. The majority of mutations were found in two functional domains, the ligand binding domain (42%) and the epidermal growth factor (EGF) precursor-like domain (47%). This report describes new features of the FH website and assesses the spectrum of mutations reported to date.

Databases as Topic↗

Comparison of the Escherichia coli K-12 genome with sampled genomes of a Klebsiella pneumoniae and three salmonella enterica serovars, Typhimurium, Typhi and Paratyphi.

The Escherichia coli K-12 genome (ECO) was compared with the sampled genomes of the sibling species Salmonella enterica serovars Typhimurium, Typhi and Paratyphi A (collectively referred to as SAL) and the genome of the close outgroup Klebsiella pneumoniae (KPN). There are at least 160 locations where sequences of >400 bp are absent from ECO but present in the genomes of all three SAL and 394 locations where sequences are present in ECO but close homologs are absent in all SAL genomes. The 394 sequences in ECO that do not occur in SAL contain 1350 (30.6%) of the 4405 ECO genes. Of these, 1165 are missing from both SAL and KPN. Most of the 1165 genes are concentrated within 28 regions of 10-40 kb, which consist almost exclusively of such genes. Among these regions were six that included previously identified cryptic phage. A hypothetical ancestral state of genomic regions that differ between ECO and SAL can be inferred in some cases by reference to the genome structure in KPN and the more distant relative Yersinia pestis. However, many changes between ECO and SAL are concentrated in regions where all four genera have a different structure. The rate of gene insertion and deletion is sufficiently high in these regions that the ancestral state of the ECO/SAL lineage cannot be inferred from the present data. The sequencing of other closely related genomes, such as S.bongori or Citrobacter, may help in this regard.

Databases as Topic↗

PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals.

The newly inaugurated Research Resource for Complex Physiologic Signals, which was created under the auspices of the National Center for Research Resources of the National Institutes of Health, is intended to stimulate current research and new investigations in the study of cardiovascular and other complex biomedical signals. The resource has 3 interdependent components. PhysioBank is a large and growing archive of well-characterized digital recordings of physiological signals and related data for use by the biomedical research community. It currently includes databases of multiparameter cardiopulmonary, neural, and other biomedical signals from healthy subjects and from patients with a variety of conditions with major public health implications, including life-threatening arrhythmias, congestive heart failure, sleep apnea, neurological disorders, and aging. PhysioToolkit is a library of open-source software for physiological signal processing and analysis, the detection of physiologically significant events using both classic techniques and novel methods based on statistical physics and nonlinear dynamics, the interactive display and characterization of signals, the creation of new databases, the simulation of physiological and other signals, the quantitative evaluation and comparison of analysis methods, and the analysis of nonstationary processes. PhysioNet is an on-line forum for the dissemination and exchange of recorded biomedical signals and open-source software for analyzing them. It provides facilities for the cooperative analysis of data and the evaluation of proposed new algorithms. In addition to providing free electronic access to PhysioBank data and PhysioToolkit software via the World Wide Web (http://www.physionet. org), PhysioNet offers services and training via on-line tutorials to assist users with varying levels of expertise.

Databases as Topic↗