103 research outputs found
Intra-individual Variability of the Electrocardiogram: Assessment and exploitation in computerized ECG analysis
Computer interpretation of the electrocardiogram (ECG) was one of the first applications
of computers in health care. The first systems were developed in the
early sixties by Pipberger and Caceres. In the last decades, computerized
ECG analysis has become one of the most widespread computer applications
for decision support in health care. For example, over 50 million ECGs were
analyzed by computer in the United States already in 1988.
Large research efforts have been made to bring the performance of these systems
to a level acceptable for routine use. In an international cooperative study,
called 'Common Standards for Quantitative Electrocardiography' (CSE), most
present-day ECG computer programs were evaluated. This evaluation was
done by comparing measurements and interpretations computed by these systems
with those of a panel of experienced cardiologists on the one hand, and
with a diagnosis based on clinical evidence, not involving the EeG, on the other.
It was concluded that ECG analysis programs can assist clinicians in achieving
lllore unifornl and consistent interpretations of ECGs. However, continued testing
and refinenlent was deelned necessary to enhance the perfonnance of these
systenls.
Despite their good diagnostic performance, ECG analysis programs suffer from
a number of drawbacks that limit their practical utility.
One of the most important shortcomings is the vulnerability for individual ECG
variability. Identical ECG signals will result in identical measurements
and interpretations, but small (and diagnostic inconsequential) differences between
signals may result in an entirely different diagnostic interpretation.
This variability can already be a problem when, e.g., multiple ECGs of the same
patient are interpreted that have been recorded only minutes apart. Instable interpretations
will not only considerably diminish the practical use of ECG computer
prograrlls, users will also lose confidence in their performance. Furthernlore,
variation in interpretations of shnilar ECGs suggests that the accuracy of
programs can still be improved
Rewriting and suppressing UMLS terms for improved biomedical term identification
<p>Abstract</p> <p>Background</p> <p>Identification of terms is essential for biomedical text mining.. We concentrate here on the use of vocabularies for term identification, specifically the Unified Medical Language System (UMLS). To make the UMLS more suitable for biomedical text mining we implemented and evaluated nine term rewrite and eight term suppression rules. The rules rely on UMLS properties that have been identified in previous work by others, together with an additional set of new properties discovered by our group during our work with the UMLS. Our work complements the earlier work in that we measure the impact on the number of terms identified by the different rules on a MEDLINE corpus. The number of uniquely identified terms and their frequency in MEDLINE were computed before and after applying the rules. The 50 most frequently found terms together with a sample of 100 randomly selected terms were evaluated for every rule.</p> <p>Results</p> <p>Five of the nine rewrite rules were found to generate additional synonyms and spelling variants that correctly corresponded to the meaning of the original terms and seven out of the eight suppression rules were found to suppress only undesired terms. Using the five rewrite rules that passed our evaluation, we were able to identify 1,117,772 new occurrences of 14,784 rewritten terms in MEDLINE. Without the rewriting, we recognized 651,268 terms belonging to 397,414 concepts; with rewriting, we recognized 666,053 terms belonging to 410,823 concepts, which is an increase of 2.8% in the number of terms and an increase of 3.4% in the number of concepts recognized. Using the seven suppression rules, a total of 257,118 undesired terms were suppressed in the UMLS, notably decreasing its size. 7,397 terms were suppressed in the corpus.</p> <p>Conclusions</p> <p>We recommend applying the five rewrite rules and seven suppression rules that passed our evaluation when the UMLS is to be used for biomedical term identification in MEDLINE. A software tool to apply these rules to the UMLS is freely available at <url>http://biosemantics.org/casper</url>.</p
Thesaurus-based disambiguation of gene symbols
BACKGROUND: Massive text mining of the biological literature holds great promise of relating disparate information and discovering new knowledge. However, disambiguation of gene symbols is a major bottleneck. RESULTS: We developed a simple thesaurus-based disambiguation algorithm that can operate with very little training data. The thesaurus comprises the information from five human genetic databases and MeSH. The extent of the homonym problem for human gene symbols is shown to be substantial (33% of the genes in our combined thesaurus had one or more ambiguous symbols), not only because one symbol can refer to multiple genes, but also because a gene symbol can have many non-gene meanings. A test set of 52,529 Medline abstracts, containing 690 ambiguous human gene symbols taken from OMIM, was automatically generated. Overall accuracy of the disambiguation algorithm was up to 92.7% on the test set. CONCLUSION: The ambiguity of human gene symbols is substantial, not only because one symbol may denote multiple genes but particularly because many symbols have other, non-gene meanings. The proposed disambiguation approach resolves most ambiguities in our test set with high accuracy, including the important gene/not a gene decisions. The algorithm is fast and scalable, enabling gene-symbol disambiguation in massive text mining applications
Ambiguity of human gene symbols in LocusLink and MEDLINE: creating an inventory and a disambiguation test collection
Genes are discovered almost on a daily basis and new names have to be
found. Although there are guidelines for gene nomenclature, the naming
process is highly creative. Human genes are often named with a gene symbol
and a longer, more descriptive term; the short form is very often an
abbreviation of the long form. Abbreviations in biomedical language are
highly ambiguous, i.e., one gene symbol often refers to more than one
gene.Using an existing abbreviation expansion algorithm,we explore MEDLINE
for the use of human gene symbols derived from LocusLink. It turns out
that just over 40% of these symbols occur in MEDLINE, however, many of
these occurrences are not related to genes. Along the process of making an
inventory, a disambiguation test collection is constructed automatically
Using contextual queries
Search engines generally treat search requests in isolation. The results
for a given query are identical, independent of the user, or the context
in which the user made the request. An approach is demonstrated that
explores implicit contexts as obtained from a document the user is
reading. The approach inserts into an original (web) document
functionality to directly activate context driven queries that yield
related articles obtained from various information sources
TRPC6 single nucleotide polymorphisms and progression of idiopathic membranous nephropathy
Background: Activating mutations in the Transient Receptor Potential channel C6 (TRPC6) cause autosomal dominant focal segmental glomerular sclerosis (FSGS). TRPC6 expression is upregulated in renal biopsies of patients with idiopathic membranous glomerulopathy (iMN) and animal models thereof. In iMN, disease progression is characterized by glomerulosclerosis. In addition, a context-dependent TRPC6 overexpression was recently suggested in complement-mediated podocyte injury in e.g. iMN. Hence, we hypothesized that genetic variants in TRPC6 might affect susceptibility to development or progression of iMN. Methods & Results: Genomic DNA was isolated from blood samples of 101 iMN patients and 292 controls. By direct sequencing of the entire TRPC6 gene, 13 single nucleotide polymorphisms (SNPs) were identified in the iMN cohort, two of which were causing an amino acid substitution (rs3802829; Pro15Ser and rs36111323, Ala404Val). No statistically significant differences in genotypes or allele frequencies between patients and controls were observed. Clinical outcome in patients was determined (remission n = 26, renal failure n = 46, persistent proteinuria n = 29, follow-up median 80 months {range 51-166}). The 13 identified SNPs showed no association with remission or renal failure. There were no differences in genotypes or allele frequencies between patients in remission and progressors. Conclusions: Our data suggest that TRPC6 polymorphisms do not affect susceptibility to iMN, or clinical outcome in iMN
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