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At least 163 records · Page 9Linked to original sources
PubMed2001

Evaluation of biofilm image thresholding methods.

To evaluate biomass distribution in heterogeneous biofilms from their microscope images, it is often necessary to perform image thresholding by converting the gray-scale images to binary images consisting of a foreground of biomass material and a background of interstitial space. The selection of the gray-scale intensity used for thresholding is arbitrary but under the control of the operator, which may produce unacceptable levels of variability among operators. The quality of numerical information extracted from the images is diminished by such variability, and it is desirable to find a method that improves the reproducibility of thresholding operations. Automatic methods of thresholding provide this reproducibility, but often at the expense of accuracy, as they consistently set thresholds that differ significantly from what human operators would choose. The performance of five automatic image thresholding algorithms was tested in this study: (1) local entropy; (2) joint entropy; (3) relative entropy; (4) Renyi's entropy; and (5) iterative selection. Only the iterative selection method was satisfactory in that it was consistently setting the threshold level near that set manually. The extraction of feature information from biofilm images benefits from automatic thresholding and can be extended to other fields, such as medical imaging.

Algorithms↗
PubMedDate not supplied

Velocity imaging by rapid cycle tagging.

The unique abilities of magnetic resonance imaging (MRI) to provide detailed images of blood flow in the body, and without resorting to the injection of contrast agents, has provided the stimulus for the keen interest in this subject. In this paper we discuss a technique aimed at providing quantitative information on the distribution of velocity of blood flow across the lumen of a vessel. The technique is designed to reduce the signal from stationary material allowing for a much more accurate determination of the signal from moving material. A particular feature is that the physical phenomenon sensitized is time-of-flight displacements or "fresh spin inflow," and the technique does not rely upon phase-sensitive modulations. Further this technique produces a steady-state spatial distribution of magnetization, and hence a signal, which reflects both the time at which material enters the selected slice and how long the material remains in the slice. In this way, a single image will provide the information necessary to extract information on the velocities of interest.

Blood Flow Velocity↗
PubMed2004

Short-term treatment with proton-pump inhibitors as a test for gastroesophageal reflux disease: a meta-analysis of diagnostic test characteristics.

BACKGROUND: A response to proton-pump inhibitors (PPIs) is commonly considered to support the diagnosis of gastroesophageal reflux disease (GERD). However, the accuracy of this diagnostic strategy has not been well established. OBJECTIVE: To estimate the diagnostic test characteristics of successful PPI treatment with objective measures of GERD by performing a meta-analysis based on the published literature. DATA SOURCES: English-language studies were identified by searching the Cochrane Clinical Trial Register and MEDLINE from 1 January 1980 through 1 July 2003. STUDY SELECTION: Studies in which the clinical response to a short course (1 to 4 weeks) of normal- or high-dose PPI therapy could be compared with objective measures of GERD (24-hour pH monitoring, endoscopy findings, symptom questionnaires). DATA EXTRACTION: Studies were screened for inclusion by 1 author. Final decisions on exclusion were made by consensus with 2 of the other authors. Two investigators independently extracted the data. Information extracted included patient characteristics, study design, setting, specific type and dose of medication, duration of treatment, and definitions of outcomes. DATA SYNTHESIS: Sensitivity and specificity were determined by comparing a clinical response to PPIs with objective measures for GERD. The summary receiver-operating characteristic curve method was used to summarize test characteristics across studies. Sensitivity and specificity were also combined independently by using a random-effects model. Fifteen studies met the inclusion criteria and provided sufficient data. With 24-hour pH monitoring as the reference standard, the positive likelihood ratio ranged from 1.63 to 1.87, and combined estimates of sensitivity and specificity were 0.78 (95% CI, 0.66 to 0.86) and 0.54 (CI, 0.44 to 0.65), respectively. These values were lower with the other reference standards. LIMITATIONS: Data were insufficient to determine the effect of various doses of PPIs and duration of therapy on test characteristics. CONCLUSION: Successful short-term treatment with a PPI in patients suspected of having GERD does not confidently establish the diagnosis when GERD is defined by currently accepted reference standards.

Adult↗
PubMed2006

Systematic review: impact of health information technology on quality, efficiency, and costs of medical care.

BACKGROUND: Experts consider health information technology key to improving efficiency and quality of health care. PURPOSE: To systematically review evidence on the effect of health information technology on quality, efficiency, and costs of health care. DATA SOURCES: The authors systematically searched the English-language literature indexed in MEDLINE (1995 to January 2004), the Cochrane Central Register of Controlled Trials, the Cochrane Database of Abstracts of Reviews of Effects, and the Periodical Abstracts Database. We also added studies identified by experts up to April 2005. STUDY SELECTION: Descriptive and comparative studies and systematic reviews of health information technology. DATA EXTRACTION: Two reviewers independently extracted information on system capabilities, design, effects on quality, system acquisition, implementation context, and costs. DATA SYNTHESIS: 257 studies met the inclusion criteria. Most studies addressed decision support systems or electronic health records. Approximately 25% of the studies were from 4 academic institutions that implemented internally developed systems; only 9 studies evaluated multifunctional, commercially developed systems. Three major benefits on quality were demonstrated: increased adherence to guideline-based care, enhanced surveillance and monitoring, and decreased medication errors. The primary domain of improvement was preventive health. The major efficiency benefit shown was decreased utilization of care. Data on another efficiency measure, time utilization, were mixed. Empirical cost data were limited. LIMITATIONS: Available quantitative research was limited and was done by a small number of institutions. Systems were heterogeneous and sometimes incompletely described. Available financial and contextual data were limited. CONCLUSIONS: Four benchmark institutions have demonstrated the efficacy of health information technologies in improving quality and efficiency. Whether and how other institutions can achieve similar benefits, and at what costs, are unclear.

Biomedical Technology↗
PubMedDate not supplied

Envirometrics. Part I: Modeling of water salinity and air quality data.

Envirometrics utilises advanced mathematical, statistical and information tools to extract information. Two typical environmental data sets are analysed using MVATOB (Multi Variate Analysis TOol Box). The first data set corresponds to the variable river salinity. Least median squares (LMS) detected the outliers whereas linear least squares (LLS) could not detect and remove the outliers. The second data set consists of daily readings of air quality values. Outliers are detected by LMS and unbiased regression coefficients are estimated by multi-linear regression (MLR). As explanatory variables are not independent, principal component regression (PCR) and partial least squares regression (PLSR) are used. Both examples demonstrate the superiority of LMS over LLS.

Air Pollutants↗
PubMed2006

On the analysis of single versus multiple channels of electromagnetic brain signals.

OBJECTIVE: When extracting information from electromagnetic (EM) brain function through recordings such as the electroencephalogram (EEG) it is often assumed that signal processing techniques must be applied to multiple simultaneous recordings in order to obtain useful results. However, sometimes only a single channel of EEG recording is available or desirable. In this paper we objectively assess a novel methodology which exploits only a single measurement channel to extract information of interest relatively independent of channel location (relative to the source of interest). METHODS: The method relies on a combination of a matrix of delay vectors constructed from the single channel measurement, along with constrained independent component analysis, which incorporates prior information into the process. MATERIALS: Here, we use synthetically generated seizure EEG, composed of real, normal multi-channel EEG onto which is superimposed synthetic epileptic "seizure-like" activity, at different signal-to-noise (SNR) levels, through an equivalent current dipole model. RESULTS: We show that the method can extract desired information from single channels with a reasonable accuracy even at very small SNR and from channels distant from the focus of the activity. This provides a powerful technique capable of extracting multiple sources underlying single channel recordings and will be useful in situations where only single channel EM recordings of brain function are desirable, such as would be the case in wearable or implantable recording devices.

Artificial Intelligence↗
PubMed2000

EDGAR: extraction of drugs, genes and relations from the biomedical literature.

EDGAR (Extraction of Drugs, Genes and Relations) is a natural language processing system that extracts information about drugs and genes relevant to cancer from the biomedical literature. This automatically extracted information has remarkable potential to facilitate computational analysis in the molecular biology of cancer, and the technology is straightforwardly generalizable to many areas of biomedicine. This paper reports on the mechanisms for automatically generating such assertions and on a simple application, conceptual clustering of documents. The system uses a stochastic part of speech tagger, generates an underspecified syntactic parse and then uses semantic and pragmatic information to construct its assertions. The system builds on two important existing resources: the MEDLINE database of biomedical citations and abstracts and the Unified Medical Language System, which provides syntactic and semantic information about the terms found in biomedical abstracts.

Algorithms↗
PubMedDate not supplied

Extraction of information from CT scans at different energies.

The image displayed in computed tomography is a scaled representation of attenuation coefficients within the patient's body. A number of authors have presented methods by which additional information (such as electron density, effective atomic number, and extrapolated attenuation coefficients for therapy applications) can be extracted from CT scans carried out at different energies. In the present paper, the dual-energy method described by Rutherford has been used to produce complete images of effective atomic number and electron density of a known phantom (the AAPM phantom) in order to investigate the usefulness of applying this method to current commercial scanners.

Tomography, X-Ray Computed↗
PubMed2003

Inferring higher functional information for RIKEN mouse full-length cDNA clones with FACTS.

FACTS (Functional Association/Annotation of cDNA Clones from Text/Sequence Sources) is a semiautomated knowledge discovery and annotation system that integrates molecular function information derived from sequence analysis results (sequence inferred) with functional information extracted from text. Text-inferred information was extracted from keyword-based retrievals of MEDLINE abstracts and by matching of gene or protein names to OMIM, BIND, and DIP database entries. Using FACTS, we found that 47.5% of the 60,770 RIKEN mouse cDNA FANTOM2 clone annotations were informative for text searches. MEDLINE queries yielded molecular interaction-containing sentences for 23.1% of the clones. When disease MeSH and GO terms were matched with retrieved abstracts, 22.7% of clones were associated with potential diseases, and 32.5% with GO identifiers. A significant number (23.5%) of disease MeSH-associated clones were also found to have a hereditary disease association (OMIM Morbidmap). Inferred neoplastic and nervous system disease represented 49.6% and 36.0% of disease MeSH-associated clones, respectively. A comparison of sequence-based GO assignments with informative text-based GO assignments revealed that for 78.2% of clones, identical GO assignments were provided for that clone by either method, whereas for 21.8% of clones, the assignments differed. In contrast, for OMIM assignments, only 28.5% of clones had identical sequence-based and text-based OMIM assignments. Sequence, sentence, and term-based functional associations are included in the FACTS database (http://facts.gsc.riken.go.jp/), which permits results to be annotated and explored through web-accessible keyword and sequence search interfaces. The FACTS database will be a critical tool for investigating the functional complexity of the mouse transcriptome, cDNA-inferred interactome (molecular interactions), and pathome (pathologies).

Animals↗
PubMed2006

[The algorithm of extracting respiratory information from single channel electrocardiogram].

Because the factors of interference in R wave of normal QRS are few except for respiration, the algorithm uses supra ventricular beats in the ECG to extract respiratory signal (EDR) (ECG-derived respiratory signal). The method used here firstly transfers the original single lead ECG signal into the wavelet, then prolongs R amplitudes of supra ventricular beats in the proper scale approximation of wavelet, then the prolonged signal passes a low pass filter, then reduces sample rate to 5 Hz, finally through a band pass filter (0.1-0.4 Hz), the output of the band pass filter is the EDR.

Algorithms↗
PubMed2003

To see or not to see: does previewing a future opponent affect the contest behavior of green swordtail males ( Xiphophorus helleri)?

Animals assess the fighting ability of conspecifics either by engaging in aggressive interactions or observing contests between others. However, whether individuals assess physical prowess outside the context of aggressive interactions remains unknown. We examined whether male green swordtails ( Xiphophorus helleri) extract information about the fighting ability of solitary individuals via observation and whether acquiring such information elicits behavioral modifications. Contests preceded by mutual visual assessment were significantly shorter than fights where only one or neither of the two individuals was informed in advance. Focal animals initiated aggressive behavior more often against larger opponents only after previewing their adversary, indicating that swordtails can extract information about relative body size from watching solitary conspecifics. When a fighting disadvantage is perceived, observers adopt tactics that increase their probability of winning the contest.

Aggression↗
PubMed2002

Text-based knowledge discovery: search and mining of life-sciences documents.

Text literature is playing an increasingly important role in biomedical discovery. The challenge is to manage the increasing volume, complexity and specialization of knowledge expressed in this literature. Although information retrieval or text searching is useful, it is not sufficient to find specific facts and relations. Information extraction methods are evolving to extract automatically specific, fine-grained terms corresponding to the names of entities referred to in the text, and the relationships that connect these terms. Information extraction is, in turn, a means to an end, and knowledge discovery methods are evolving for the discovery of still more-complex structures and connections among facts. These methods provide an interpretive context for understanding the meaning of biological data.

Biological Science Disciplines↗
PubMed2006

Natural language processing and visualization in the molecular imaging domain.

Molecular imaging is at the crossroads of genomic sciences and medical imaging. Information within the molecular imaging literature could be used to link to genomic and imaging information resources and to organize and index images in a way that is potentially useful to researchers. A number of natural language processing (NLP) systems are available to automatically extract information from genomic literature. One existing NLP system, known as BioMedLEE, automatically extracts biological information consisting of biomolecular substances and phenotypic data. This paper focuses on the adaptation, evaluation, and application of BioMedLEE to the molecular imaging domain. In order to adapt BioMedLEE for this domain, we extend an existing molecular imaging terminology and incorporate it into BioMedLEE. BioMedLEE's performance is assessed with a formal evaluation study. The system's performance, measured as recall and precision, is 0.74 (95% CI: [.70-.76]) and 0.70 (95% CI [.63-.76]), respectively. We adapt a JAVA viewer known as PGviewer for the simultaneous visualization of images with NLP extracted information.

Animals↗
PubMed2001

The extraction of useful information from the biomedical literature.

Modern information technology provides the biomedical professional with powerful tools and processes for extracting useful information from large volumes of text. Presently, little use is made of the full capabilities of these tools to supplement research and teaching. This article gives an overview of these tools and processes, and shows the diversity of ways they can be applied to enhance the capabilities of biomedical professionals. The article defines information technology terms, presents the requirements for extracting high-quality information, describes some available techniques for extracting information, and summarizes myriad information-extraction applications. While the biomedical researcher or teacher can gain substantial benefits by using effective information-extraction techniques, substantial time and effort and technical expertise are required to generate a credible high-quality product, that is, the information needed.

Humans↗
PubMed2005

Automatically annotating documents with normalized gene lists.

BACKGROUND: Document gene normalization is the problem of creating a list of unique identifiers for genes that are mentioned within a document. Automating this process has many potential applications in both information extraction and database curation systems. Here we present two separate solutions to this problem. The first is primarily based on standard pattern matching and information extraction techniques. The second and more novel solution uses a statistical classifier to recognize valid gene matches from a list of known gene synonyms. RESULTS: We compare the results of the two systems, analyze their merits and argue that the classification based system is preferable for many reasons including performance, simplicity and robustness. Our best systems attain a balanced precision and recall in the range of 74%-92%, depending on the organism.

Animals↗
PubMed2005

Gene/protein name recognition based on support vector machine using dictionary as features.

BACKGROUND: Automated information extraction from biomedical literature is important because a vast amount of biomedical literature has been published. Recognition of the biomedical named entities is the first step in information extraction. We developed an automated recognition system based on the SVM algorithm and evaluated it in Task 1.A of BioCreAtIvE, a competition for automated gene/protein name recognition. RESULTS: In the work presented here, our recognition system uses the feature set of the word, the part-of-speech (POS), the orthography, the prefix, the suffix, and the preceding class. We call these features "internal resource features", i.e., features that can be found in the training data. Additionally, we consider the features of matching against dictionaries to be external resource features. We investigated and evaluated the effect of these features as well as the effect of tuning the parameters of the SVM algorithm. We found that the dictionary matching features contributed slightly to the improvement in the performance of the f-score. We attribute this to the possibility that the dictionary matching features might overlap with other features in the current multiple feature setting. CONCLUSION: During SVM learning, each feature alone had a marginally positive effect on system performance. This supports the fact that the SVM algorithm is robust on the high dimensionality of the feature vector space and means that feature selection is not required.

Algorithms↗
PubMed2002

The limit fold change model: a practical approach for selecting differentially expressed genes from microarray data.

BACKGROUND: The biomedical community is developing new methods of data analysis to more efficiently process the massive data sets produced by microarray experiments. Systematic and global mathematical approaches that can be readily applied to a large number of experimental designs become fundamental to correctly handle the otherwise overwhelming data sets. RESULTS: The gene selection model presented herein is based on the observation that: (1) variance of gene expression is a function of absolute expression; (2) one can model this relationship in order to set an appropriate lower fold change limit of significance; and (3) this relationship defines a function that can be used to select differentially expressed genes. The model first evaluates fold change (FC) across the entire range of absolute expression levels for any number of experimental conditions. Genes are systematically binned, and those genes within the top X% of highest FCs for each bin are evaluated both with and without the use of replicates. A function is fitted through the top X% of each bin, thereby defining a limit fold change. All genes selected by the 5% FC model lie above measurement variability using a within standard deviation (SDwithin) confidence level of 99.9%. Real time-PCR (RT-PCR) analysis demonstrated 85.7% concordance with microarray data selected by the limit function. CONCLUSION: The FC model can confidently select differentially expressed genes as corroborated by variance data and RT-PCR. The simplicity of the overall process permits selecting model limits that best describe experimental data by extracting information on gene expression patterns across the range of expression levels. Genes selected by this process can be consistently compared between experiments and enables the user to globally extract information with a high degree of confidence.

Animals↗