Dataset Information

Automatic SNOMED coding.

ABSTRACT: Medical coding has become an important new industry that has originated from the field of medical informatics. Automatic coding of specimens has emerged as a way of relieving hospitals from the cost of paying professional coders and for achieving uniform coding for all specimens. Unfortunately, automatic coding, like manual coding, has numerous pitfalls. Further, the coding algorithms employed by manufacturers of automatic coders are typically proprietary. We have developed a method for automatic coding of pathology reports. Using this public domain autocoder, we have previously demonstrated that automatic SNOMED coding was superior to manual coding in several measurable categories, including the overall number of codes generated and the number of distinct code entities provided. In this report, we describe an algorithm that executes this strategy in the M-Technology environment.

SUBMITTER: Moore GW

PROVIDER: S-EPMC2247836 | biostudies-other | 1994

REPOSITORIES: biostudies-other

ACCESS DATA

Publications

Automatic SNOMED coding.

Moore G W GW Berman J J JJ

Proceedings. Symposium on Computer Applications in Medical Care 19940101

Medical coding has become an important new industry that has originated from the field of medical informatics. Automatic coding of specimens has emerged as a way of relieving hospitals from the cost of paying professional coders and for achieving uniform coding for all specimens. Unfortunately, automatic coding, like manual coding, has numerous pitfalls. Further, the coding algorithms employed by manufacturers of automatic coders are typically proprietary. We have developed a method for automati ...[more]

PMID: 7949924

Similar Datasets

Project description:BACKGROUND:Mounting evidence suggests several diseases and biological processes target transcription termination to misregulate gene expression. Disruption of transcription termination leads to readthrough transcription past the 3' end of genes, which can result in novel transcripts, changes in epigenetic states and altered 3D genome structure. RESULTS:We developed Automatic Readthrough Transcription Detection (ARTDeco), a tool to detect and analyze multiple features of readthrough transcription from RNA-seq and other next-generation sequencing (NGS) assays that profile transcriptional activity. ARTDeco robustly quantifies the global severity of readthrough phenotypes, and reliably identifies individual genes that fail to terminate (readthrough genes), are aberrantly transcribed due to upstream termination failure (read-in genes), and novel transcripts created as a result of readthrough (downstream of gene or DoG transcripts). We used ARTDeco to characterize readthrough transcription observed during influenza A virus (IAV) infection, validating its specificity and sensitivity by comparing its performance in samples infected with a mutant virus that fails to block transcription termination. We verify ARTDeco's ability to detect readthrough as well as identify read-in genes from different experimental assays across multiple experimental systems with known defects in transcriptional termination, and show how these results can be leveraged to improve the interpretation of gene expression and downstream analysis. Applying ARTDeco to a gene expression data set from IAV-infected monocytes from different donors, we find strong evidence that read-in gene-associated expression quantitative trait loci (eQTLs) likely regulate genes upstream of read-in genes. This indicates that taking readthrough transcription into account is important for the interpretation of eQTLs in systems where transcription termination is blocked. CONCLUSIONS:ARTDeco aids researchers investigating readthrough transcription in a variety of systems and contexts.

Dataset Information

Automatic SNOMED coding.

Publications

Automatic SNOMED coding.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure