Search

Classifying biological data is a common task in the biomedical context. Predicting the class of new, unknown information allows researchers to gain insight and make decisions based on the available data. Also, using classification methods often implies choosing the best parameters to obtain optimal class separation, and the number of parameters might be large in biological datasets. Support Vector Machines provide a well-established and powerful classification method to analyse data and find the minimal-risk separation between different classes. Finding that separation strongly depends on the available feature set and the tuning of hyper-parameters. Techniques for feature selection and SVM parameters optimization are known to improve classification accuracy, and its literature is extensive. In this paper we review the strategies that are used to improve the classification performance of SVMs and perform our own experimentation to study the influence of features and hyper-parameters in the optimization process, using several known kernels.

Background: The ability to identify patients with colorectal cancer (CRC) liver metastasis (LM) using administrative data is unknown. The goals of this study were to evaluate whether administrative data can accurately identify patients with CRCLM and to develop a diagnostic algorithm capable of identifying such patients., Materials and Methods: A retrospective cohort study was conducted to validate the diagnostic and procedural codes found in administrative databases of the Veterans Administration (VA) system. CRC patients evaluated at a major VA center were identified (1997-2008, n = 1671) and classified as having liver-specific ICD-9 and/or CPT codes. The presence of CRCLM was verified by primary chart abstraction in the study sample. Contingency tables were created and the positive predictive value (PPV) for CRCLM was calculated for each candidate administrative code. A multivariate logistic-regression model was used to identify independent predictors (codes) of CRCLM, which were used to develop a diagnostic algorithm. Validity of the algorithm was determined by discrimination (c-statistic) of the model and PPV of the algorithm., Results: Multivariate logistic regression identified ICD-9 diagnosis codes 155.2 (OR 9.7 [95% CI 2.5-38.4]) and 197.7 (84.6 [52.9-135.3]), and procedure code 50.22 (5.9 [1.3-25.5]) as independent predictors of CRCLM diagnosis. The model's discrimination was 0.89. The diagnostic algorithm, defined as the presence of any of these codes, had a PPV of 87%., Conclusions: VA administrative databases reliably identify patients with CRCLM. This diagnostic algorithm is highly predictive of CRCLM diagnosis and can be used for research studies evaluating population-level features of this disease within the VA system., (Published by Elsevier Inc.)

Research relies on ever larger amounts of data from experiments, automated production equipment, questionnaries, times series such as weather records, and so on. A major task in science is to combine, process and analyse such data to obtain evidence of patterns and correlations.Most research data are on digital form, which in principle ensures easy processing and analysis, easy long-term preservation, and easy reuse in future research, perhaps in entirely unanticipated ways. However, in practice, obstacles such as incompatible or undocumented data formats, poor data quality and lack of familiarity with current technology prevent researchers from making full use of available data.This paper argues that relational databases are excellent tools for veterinary research and animal production; provides a small example to introduce basic database concepts; and points out some concerns that must be addressed when organizing data for research purposes.

Background: CISMeF is a French quality-controlled health gateway that uses the MeSH thesaurus. We introduced two new concepts, metaterms (medical specialty which has semantic links with one or more MeSH terms, subheadings and resource types) and resource types., Objective: Evaluate precision and recall of metaterms., Methods: We created 16 pairs of queries. Each pair concerned the same topic, but one used metaterms and one MeSH terms. To assess precision, each document retrieved by the query was classified as irrelevant, partly relevant or fully relevant., Results: The 16 queries yielded 943 documents for metaterm queries and 139 for MeSH term queries. The recall of MeSH term queries was 0.44 (compared to 1 for metaterm queries) and the precision were identical for MeSH term and metaterm queries., Conclusion: Metaconcept such as CISMeF metaterms allows a better recall with a similar precision that MeSH terms in a quality controlled health gateway.

Data warehouses must provide a flexible data model that is integrated with knowledge and metadata describing their components and contents. To provide for advanced query functionality at The Ohio State University Medical Center (OSUMC), we have developed an abstraction layer, or meta-model for our existing Information Warehouse (IW) in order to conceptually and semantically describe and classify its structure and contents using the UMLS.

The ability to simultaneously monitor expression of all genes in any bacterium whose genome has been sequenced has only recently become available. This requires not only careful experimentation but also that voluminous data be organized and interpreted. Here we review the emerging technologies that are impacting the study of bacterial global regulatory mechanisms with a view toward discussing both perceived best practices and the current state of the art. To do this, we concentrate upon examples using Escherichia coli and Bacillus subtilis because prior work in these organisms provides a sound basis for comparison.

An understanding of the organizational context and taxonomy of health care databases is essential to appropriately use these data sources for research purposes. Characteristics of the organizational structure of the specific health care setting, including the model type, financial arrangement, and provider access, have implications for accessing and using this data effectively. Additionally, the benefit coverage environment may affect the utility of health care databases to address specific research questions. Coverage considerations that affect pharmacoepidemiologic research include eligibility, the nature of the pharmacy benefit, and regulatory aspects of the treatment under consideration.

This study determines the interrater and intrarater reliability of the Traumatic Coma Data Bank (TCDB) computed tomography (CT) scan classification for severe head injury. This classification grades the severity of the injury as follows: I = normal, II = diffuse injury, III = diffuse injury with swelling, IV = diffuse injury with shift, V = mass lesion surgically evacuated, or VI = mass lesion not operated. Patients with severe closed head injury were included. Outcome was assessed using the Glasgow Outcome Score (GOS) at 3 and 6 months. Four observers, two of them classifying the scans twice, independently evaluated CT scans. Of the initial CT scans of 63 patients (36 males, 27 females; age, 34+/-24 years), 6.3% were class I, 26.9% class II, 28.6% class III, 6.3% class IV, 22.2% were class V, and 9.6% class VI. The overall interrater and intrarater reliability was 0.80 and 0.85, respectively. Separate analyses resulted in higher inter- and intrarater reliabilities for the mass lesion categories (V and VI), 0.94 and 0.91, respectively, than the diffuse categories (I-IV) 0.71 and 0.67. Merging category III with IV, and V with VI resulted in inter- and intrarater reliabilities of 0.93 and 0.78, respectively. Glasgow outcome scores after 6 months were as follows: 19 dead (30%), one vegetative (2%), five severely disabled (8%), 17 moderately disabled (27%), and 21 good recovery (33%). Association measures (Sommers' D) between CT and GOS scores were statistically significant for all observers. This study shows a high intra- and interobserver agreement in the assessment of CT scan abnormalities and confirms the predictive power on outcome when the TCDB classification is used.

Data warehouses and data marts have been successfully applied to a multitude of commercial business applications. They have proven to be invaluable tools by integrating information from distributed, heterogeneous sources and summarizing this data for use throughout the enterprise. Although the need for information dissemination is as vital in science as in business, working warehouses in this community are scarce because traditional warehousing techniques do not transfer to scientific environments. There are two primary reasons for this difficulty. First, schema integration is more difficult for scientific databases than for business sources, because of the complexity of the concepts and the associated relationships. While this difference has not yet been fully explored, it is an important consideration when determining how to integrate autonomous sources. Second, scientific data sources have highly dynamic data representations (schemata). When a data source participating in a warehouse changes its schema, both the mediator transferring data to the warehouse and the warehouse itself need to be updated to reflect these modifications. The cost of repeatedly performing these updates in a traditional warehouse, as is required in a dynamic environment, is prohibitive. This paper discusses these issues within the context of the DataFoundry project, an ongoing research effort at Lawrence Livermore National Laboratory. DataFoundry utilizes a unique integration strategy to identify corresponding instances while maintaining differences between data from different sources, and a novel architecture and an extensive meta-data infrastructure, which reduce the cost of maintaining a warehouse.

The U.S. Department of Veterans Affairs (VA) operates and maintains one of the largest health care systems under a single management structure in the world. The coordination of administrative and clinical information on veterans served by the VA health care system is a daunting and critical function of the Department. This article provides an overview of VA Health Services Research and Development Service initiatives to assist researchers in using extant VA databases to study patient-centered health care outcomes. As examples, studies using the VA's Patient Treatment File (PTF) and the Beneficiary Identification and Records Locator System (BIRLS) Death File are described.

Secondary data sources are being used more frequently than ever in outcomes research. The speed and relative low cost of these data bases makes them ideal for analyzing outcomes. Today's researcher has numerous secondary data bases available for use. Few publications exist to help researchers locate the ideal data set for their needs. Herein, two national secondary data bases are reviewed: the Area Resource File (ARF) and the Healthcare Cost and Utilization Project (HCUP). These two data sets represent the two types of secondary data: aggregate and individual. ARF represents an aggregate data set and HCUP represents an individual data set. The advantages of each type of secondary data will also be reviewed.

We examined long-term care (LTC) utilization by male and female veterans using administrative databases maintained by VA. Research questions included: (1) Which LTC services are utilized? (2) Do utilization patterns of older veterans differ from those of elderly persons in the general U.S. population? (3) Do LTC needs of veterans vary by gender? We were unable to track LTC utilization of individuals across administrative databases. Some databases could only provide information at the national level, or alternatively, were available only at local facilities, or only at the patient or program-level data--making it impossible to get a clear picture of all the services received by an individual. Those planning to use administrative databases to conduct research must: (1) take more time than expected; (2) be flexible/willing to compromise, (3) "ferret out" information, and (4) recognize that because of dynamism inherent in information systems, results may change over time.

A distributed design is the most cost-effective system for small-to medium-scale picture archiving and communications systems (PACS) implementations. However, the design presents an interesting challenge to developers and implementers: to make stored image data, distributed throughout the PACS network, appear to be centralized with a single access point for users. A key component for the distributed system is a central or master database, containing all the studies that have been scanned into the PACS. Each study includes a list of one or more locations for that particular dataset so that applications can easily find it. Non-Digital Imaging and Communications in Medicine (DICOM) clients, such as our worldwide web (WWW)-based PACS browser, query the master database directly to find the images, then jump to the most appropriate location via a distributed web-based viewing system. The Master Database Broker provides DICOM clients with the same functionality by translating DICOM queries to master database searches and distributing retrieval requests transparently to the appropriate source. The Broker also acts as a storage service class provider, allowing users to store selected image subsets and reformatted images with the original study, without having to know on which server the original data are stored.

As part of preliminary studies for the development of a digital library, we have studied the possibility of using the UMLS Information Sources Map (ISM) database to provide the means to connect and map different terminologies, as well as to facilitate access to available information sources. The main issues discussed are the indexing of and connection to relevant online sources. We found the features of the ISM to be consistent with the need to support automated source selection and retrieval. However, attention should be paid to three aspects of the information: granularity, completeness, and accuracy. We found the ISM to be potentially useful; however, significant modifications will be required if the ISM is to be able to support automated source selection and retrieval.