Organizations frequently have to deal with documents received as images: for example, scanned, photographed, or faxed documents. In order to include such documents in your analysis and automate processes, you first need to convert them to a machine readable form. Megaputer offers a solution for converting images to electronic text.
Megaputer provides a text analysis solution that simplifies knowledge discovery in customer communications data. The solution provides clustering and classification of documents, extraction of facts and patterns of interest, and elaborate sentiment analysis capabilities. It performs joint analysis of textual and structured data to determine key trends, relationships, and emerging patterns and then summarizes key findings in customizable graphical reports.
This webinar will present a live demonstration. To efficiently process adverse event (AE) reports, patient safety professionals encounter a number of challenges. First, they need to convert documents to a machine readable format and process very diverse AE reports received from multiple channels and that are possibly in multiple languages and formats. Second, it is not easy to identify and classify the actual adverse events related to specific medications, differentiating them from the indications the medications were prescribed for. And third, it is necessary to isolate serious adverse events that might affect patient safety and thus require immediate attention to be reported to regulators. Join us as we present a solution that enables patient safety professionals to efficiently handle all these tasks.
Time is of the essence when detecting emerging issues. This solution performs the analysis of customer feedback and service notes to identify patterns and trends in issues related to specific products. It detects previously unknown issues that have consistent growth over a sufficient period of time and reports them as emerging issues. The solution presented can also discern a weak but growing signal against the background noise of already known issues.
Discover how text analysis solutions can scout external sources, fetch documents of potential interest, and perform sophisticated analysis to extract relevant entities, facts and relationships with high recall and precision. See how you can timely reveal information about emerging technologies, growing negative impact signifiers, maneuvering of competitors, changing structure of corporate ownership, along with new acquisitions, partnerships, investments, and much more.
The analysis of data from online sources could enable the organization to learn about existing threats and opportunities from a global community of interested individuals. Many organizations are establishing Web Intelligence teams tasked with analyzing web data. Such analysis requires accurate categorization of textual posts and detection of sentiment toward numerous discussed topics. However, due to the complex nature of textual data, the analysis is often performed only manually, which severely limits the amount of data that could be processed. Oftentimes, less data means reducing the value of the results. However, the advent of adequate tools for text clustering and classification combined with sentiment analysis helps to generate more timely insights based on the analysis of all available data, thus streamlining Web Intelligence efforts.
The WCM Conference is an annual platform to bring together near 300 warranty professionals and executives as well as analytics tool and service providers. Dr. Sergei Ananyan will hold a three hour training workshop on Identifying Warranty Cost, Emerging Issues & Fraud Using Text Analysis.
What if you could automatically reveal key researchers by analyzing a large collection of journal articles related to your topics of interest? Explore a system that determines the role and relative importance of each individual author and presents the results in both graphical and tabular formats.
Megaputer is proud to present a solution for performing quick and precise extraction of complex information patterns from various pre-indexed data sources including PubMed, ClinicalTrials.gov, FAERS, FDA Drug Labels and USPTO. Researchers may either create their own semantic queries or select from pre-set queries that can be expanded through various medical ontologies from a supplied library (MeSH, SNOMED CT, RxNorm, Genes, and MedDRA). The extracted elements of information are presented to the user in the form of tables with preset attributes. This solution is well poised for handling data curation tasks of nearly any complexity.