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Natural Language Processing in Healthcare Market by Technologies, Solutions, Services, Deployment Models and Forecast to 2021, by iHealthcareAnalyst, Inc. Natural Language Processing (NLP) in Healthcare Market by Technology (Automatic Summarization, Information Extraction, Machine Translation, Text and Voice Processing), Solutions (Rule-Based NLP, Statistical NLP, Hybrid NLP), Services (Professional Services, Support and Maintenance Services), and Deployment Model (On-Premises, On-Demand) and Forecast 2017-2021 Maryland Heights, MO, May 11, 2017 --( Visit Natural Language Processing (NLP) in Healthcare Market by Technology (Automatic Summarization, Information Extraction, Machine Translation, Text and Voice Processing), Solutions (Rule-Based NLP, Statistical NLP, Hybrid NLP), Services (Professional Services, Support and Maintenance Services), and Deployment Model (On-Premises, On-Demand) and Forecast 2017-2021 at https://www.ihealthcareanalyst.com/report/natural-language-processing-healthcare-market/ The global natural language processing in healthcare market segmentation is based on technology (automatic summarization, information extraction, machine translation, text and voice processing), type of solutions (rule-based NLP, statistical NLP, and hybrid NLP), type of services (professional services, support and maintenance services) and deployment model (on-premises and on-demand). The global natural language processing in healthcare market report provides market size (Revenue USD Million 2014 to 2021), market share, trends and forecasts growth trends (CAGR%, 2017 to 2021). The global healthcare natural language processing market research report is further segmented by geography into North America (U.S., Canada), Latin America (Brazil, Mexico, Rest of LA), Europe (U.K., Germany, France, Italy, Spain, Rest of EU), Asia Pacific (Japan, China, India, Rest of APAC), and Rest of the World. The global natural language processing in healthcare market report also provides the detailed market landscape (market drivers, restraints, opportunities), market attractiveness analysis and also tracks the major competitors operating in the market by company overview, financial snapshot, key products, technologies and services offered, market share analysis and recent trends in the global market. Major players operating in the global natural language processing in healthcare market and included in this report are Apple, Inc., Artificial Solutions, Dolbey Systems, eContext, IBM Corporation, Linguamatics Ltd., Microsoft Corporation, NEC Corporation, NetBase Solutions, Inc., NLP Technologies, SAS Institute Inc., and Verint Systems, Inc. 1. Technology 1.1. Automatic Summarization 1.2. Information Extraction 1.3. Machine Translation 1.4. Text and Voice Processing 2. Solution 2.1. Rule-based NLP 2.2. Statistical NLP 2.3. Hybrid NLP 3. Service 3.1. Professional Services 3.2. Support and Maintenance Services 4. Deployment Model 4.1. On-Premises 4.2. On-Demand 5. Company Profiles 5.1. Apple, Inc. 5.2. Artificial Solutions 5.3. Dolbey Systems 5.4. eContext 5.5. IBM Corporation 5.6. Linguamatics Ltd. 5.7. Microsoft Corporation 5.8. NEC Corporation 5.9. NetBase Solutions, Inc. 5.10. NLP Technologies 5.11. SAS Institute Inc. 5.12. Verint Systems, Inc. To request Table of Contents and Sample Pages of this report visit: https://www.ihealthcareanalyst.com/report/natural-language-processing-healthcare-market/ About Us iHealthcareAnalyst, Inc. is a global healthcare market research and consulting company providing market analysis, and competitive intelligence services to global clients. The company publishes syndicate, custom and consulting grade healthcare reports covering animal healthcare, biotechnology, clinical diagnostics, healthcare informatics, healthcare services, medical devices, medical equipment, and pharmaceuticals. In addition to multi-client studies, we offer creative consulting services and conduct proprietary single-client assignments targeted at client’s specific business objectives, information needs, time frame and budget. Please contact us to receive a proposal for a proprietary single-client study. Contact Us iHealthcareAnalyst, Inc. 2109, Mckelvey Hill Drive, Maryland Heights, MO 63043 United States Email: sales@ihealthcareanalyst.com Website: https://www.ihealthcareanalyst.com Maryland Heights, MO, May 11, 2017 --( PR.com )-- Natural language processing (NLP) is process of using of computer algorithms to identify key elements in everyday language and extract meaning from unstructured spoken or written input. NLP is a discipline of computer science that requires skills in artificial intelligence, computational linguistics, and other machine learning disciplines. Specific tasks for NLP systems may include: Summarizing lengthy blocks of narrative text, such as a clinical note or academic journal article, by identifying key concepts or phrases present in the source material, Mapping data elements present in unstructured text to structured fields in an electronic health record in order to improve clinical data integrity, Converting data in the other direction from machine-readable formats into natural language for reporting and educational purposes, Answering unique free-text queries that require the synthesis of multiple data sources, Engaging in optical character recognition to turn images, like PDF documents or scans of care summaries and imaging reports, into text files that can then be parsed and analyzed, and Conducting speech recognition to allow users to dictate clinical notes or other information that can then be turned into text.Visit Natural Language Processing (NLP) in Healthcare Market by Technology (Automatic Summarization, Information Extraction, Machine Translation, Text and Voice Processing), Solutions (Rule-Based NLP, Statistical NLP, Hybrid NLP), Services (Professional Services, Support and Maintenance Services), and Deployment Model (On-Premises, On-Demand) and Forecast 2017-2021 at https://www.ihealthcareanalyst.com/report/natural-language-processing-healthcare-market/The global natural language processing in healthcare market segmentation is based on technology (automatic summarization, information extraction, machine translation, text and voice processing), type of solutions (rule-based NLP, statistical NLP, and hybrid NLP), type of services (professional services, support and maintenance services) and deployment model (on-premises and on-demand).The global natural language processing in healthcare market report provides market size (Revenue USD Million 2014 to 2021), market share, trends and forecasts growth trends (CAGR%, 2017 to 2021). The global healthcare natural language processing market research report is further segmented by geography into North America (U.S., Canada), Latin America (Brazil, Mexico, Rest of LA), Europe (U.K., Germany, France, Italy, Spain, Rest of EU), Asia Pacific (Japan, China, India, Rest of APAC), and Rest of the World. The global natural language processing in healthcare market report also provides the detailed market landscape (market drivers, restraints, opportunities), market attractiveness analysis and also tracks the major competitors operating in the market by company overview, financial snapshot, key products, technologies and services offered, market share analysis and recent trends in the global market.Major players operating in the global natural language processing in healthcare market and included in this report are Apple, Inc., Artificial Solutions, Dolbey Systems, eContext, IBM Corporation, Linguamatics Ltd., Microsoft Corporation, NEC Corporation, NetBase Solutions, Inc., NLP Technologies, SAS Institute Inc., and Verint Systems, Inc.1. Technology1.1. Automatic Summarization1.2. Information Extraction1.3. Machine Translation1.4. Text and Voice Processing2. Solution2.1. Rule-based NLP2.2. Statistical NLP2.3. Hybrid NLP3. Service3.1. Professional Services3.2. Support and Maintenance Services4. Deployment Model4.1. On-Premises4.2. On-Demand5. Company Profiles5.1. Apple, Inc.5.2. Artificial Solutions5.3. Dolbey Systems5.4. eContext5.5. IBM Corporation5.6. Linguamatics Ltd.5.7. Microsoft Corporation5.8. NEC Corporation5.9. NetBase Solutions, Inc.5.10. NLP Technologies5.11. SAS Institute Inc.5.12. Verint Systems, Inc.To request Table of Contents and Sample Pages of this report visit:https://www.ihealthcareanalyst.com/report/natural-language-processing-healthcare-market/About UsiHealthcareAnalyst, Inc. is a global healthcare market research and consulting company providing market analysis, and competitive intelligence services to global clients. The company publishes syndicate, custom and consulting grade healthcare reports covering animal healthcare, biotechnology, clinical diagnostics, healthcare informatics, healthcare services, medical devices, medical equipment, and pharmaceuticals.In addition to multi-client studies, we offer creative consulting services and conduct proprietary single-client assignments targeted at client’s specific business objectives, information needs, time frame and budget. Please contact us to receive a proposal for a proprietary single-client study.Contact UsiHealthcareAnalyst, Inc.2109, Mckelvey Hill Drive,Maryland Heights, MO 63043United StatesEmail: sales@ihealthcareanalyst.comWebsite: https://www.ihealthcareanalyst.com


Bio-IT World also chose Linguamatics customer Pentavere Research Group as a Best Practices finalist, based on their work using I2E to mine unstructured data for real-world evidence to improve health outcomes. Best Practices finalists are recognized for their outstanding examples of technology innovation, from basic R&D to translational medicine. Pentavere deployed I2E to effectively mine unstructured EHR data, expediting delivery of their product daRWEn™ to the Real World Evidence market. "The annual Bio-IT World Conference & Expo is a premier event in the life sciences industry and we are honored to be considered a Best of Show Award contender," said Phil Hastings, Linguamatics chief business development officer. "We are also proud to feature in the Best Practices awards program through Pentavere's use of I2E, and congratulate them for being named a finalist." Since its debut in 2002, the Bio-IT World Conference & Expo has showcased the myriad of IT and informatics technologies that drive biomedical research, drug discovery and development, and clinical and healthcare initiatives. This year marks Linguamatics' 12th consecutive year as a conference participant and exhibitor. The 2017 conference features over 200 technology and scientific track sessions, including a presentation by Linguamatics CTO David Milward, Ph.D., on Wednesday May 24 at 12pm ET. During the Clinical Research and Translational Informatics track, Dr. Milward will discuss how text mining extracts and connects relevant clinical and scientific data in a session entitled, "Text Mining in Translational Research: Bench to Bedside and Back Again." During his talk Dr. Milward will highlight Eli Lilly's use of I2E for systematic drug repositioning from clinical trial records. Linguamatics Life Science platform uses advanced NLP to transform unstructured text into structured data. I2E 5.0 provides major new enhancements, including powerful concept normalization, advanced range search, and a new query language. These capabilities tackle the variety in big data to provide insights from the estimated 80% of data trapped in unstructured text, as well as from semi-structured and structured data sources. Linguamatics will provide demonstrations of its new I2E 5.0 release at Booth 345. Linguamatics transforms unstructured big data into big insights to advance human health and wellbeing. A natural language processing (NLP)-based text mining leader, Linguamatics' solutions are used by top commercial, academic and government organizations for high-value knowledge discovery and decision support, including 18 of the top 20 global pharmaceutical companies and leading US healthcare organizations. Linguamatics I2E mines a wide variety of text resources, including scientific literature, patents, Electronic Health Records (EHRs), clinical trials data, news feeds and proprietary content. I2E can be deployed as an in-house enterprise system or as Software-as-a-Service (SaaS). To view the original version on PR Newswire, visit:http://www.prnewswire.com/news-releases/linguamatics-to-highlight-new-text-analytics-technologies-and-innovative-deployments-at-bio-it-world-2017-300461026.html


The University of Pennsylvania Health System will leverage the Linguamatics Health platform to build queries and automatically mine clinical data from patient encounter records, specialist reports and unstructured EHR notes. Specific use cases will include the identification of cohorts of patients with certain medical conditions and locating additional clinical annotations for the PennOmics system for translational research. "Our organization needed an NLP tool to make unstructured clinical data more accessible for our research and clinical efforts," said Jason Moore, director of the Penn Institute for Biomedical Informatics (IBI). "We look forward to exploring different opportunities to use Linguamatics I2E's NLP capabilities to gain additional insights from our unstructured patient data." Linguamatics transforms unstructured big data into big insights to advance human health and wellbeing. A world leader in deploying innovative natural language processing (NLP)-based text mining for high-value knowledge discovery and decision support, Linguamatics' solutions are used by top commercial, academic and government organizations, including 18 of the top 20 global pharmaceutical companies, and leading US healthcare organizations. Linguamatics I2E is used to mine a wide variety of text resources, such as scientific literature, patents, Electronic Health Records (EHRs), clinical trials data, news feeds, social media and proprietary content. I2E can be deployed as an in-house enterprise system, or as Software-as-a-Service (SaaS) on the cloud. For more information, visit linguamatics.com and follow @Linguamatics on Twitter. To view the original version on PR Newswire, visit:http://www.prnewswire.com/news-releases/linguamatics-announces-university-of-pennsylvania-health-system-as-latest-enterprise-nlp-client-300477287.html


Grant
Agency: GTR | Branch: Innovate UK | Program: | Phase: Collaborative Research & Development | Award Amount: 153.68K | Year: 2014

In the age of Big Data, knowledge workers - individuals, companies and organisations whose primary focus is knowledge and information extraction and usage - find it increasingly difficult to search for and identify accurate and relevant information. In the domain of scientific literature and IP search, where the underlying corpora are growing at a huge rate, this is a daunting task and human expertise and involvement remain critical. This project aims to develop a suite of tools that will enable users to search for and identify relevant information within a corpus more efficiently and effectively. The methods developed will deploy new search paradigms together with semantic-based analysis, domain and lexical linguistic ontologies in order to understand the user needs based on the underlying domain of application and subsequently enable accurate information retrieval through enhanced search and cross-reference of information. The project aims to offer tools for sharing of search strategies which will be identified by observing and understanding patterns in users search behaviours.


Wiseguyreports.Com Adds “Healthcare Natural Language Processing -Market Demand, Growth, Opportunities and analysis of Top Key Player Forecast to 2022” To Its Research Database This report studies sales (consumption) of Healthcare Natural Language Processing in United States market, focuses on the top players, with sales, price, revenue and market share for each player, covering Split by product types, with sales, revenue, price, market share and growth rate of each type, can be divided into Type I Type II Split by applications, this report focuses on sales, market share and growth rate of Healthcare Natural Language Processing in each application, can be divided into Application 1 Application 2 United States Healthcare Natural Language Processing Market Report 2017 1 Healthcare Natural Language Processing Overview 1.1 Product Overview and Scope of Healthcare Natural Language Processing 1.2 Classification of Healthcare Natural Language Processing 1.2.1 Type I 1.2.2 Type II 1.3 Application of Healthcare Natural Language Processing 1.3.1 Application 1 1.3.2 Application 2 1.4 United States Market Size Sales (Volume) and Revenue (Value) of Healthcare Natural Language Processing (2012-2022) 1.4.1 United States Healthcare Natural Language Processing Sales and Growth Rate (2012-2022) 1.4.2 United States Healthcare Natural Language Processing Revenue and Growth Rate (2012-2022) 6 United States Healthcare Natural Language Processing Manufacturers Profiles/Analysis 6.1 3M 6.1.1 Company Basic Information, Manufacturing Base and Competitors 6.1.2 Healthcare Natural Language Processing Product Type, Application and Specification 6.1.2.1 Product A 6.1.2.2 Product B 6.1.3 3M Healthcare Natural Language Processing Sales, Revenue, Price and Gross Margin (2012-2017) 6.1.4 Main Business/Business Overview 6.2 IBM 6.2.2 Healthcare Natural Language Processing Product Type, Application and Specification 6.2.2.1 Product A 6.2.2.2 Product B 6.2.3 IBM Healthcare Natural Language Processing Sales, Revenue, Price and Gross Margin (2012-2017) 6.2.4 Main Business/Business Overview 6.3 Cerner Corp 6.3.2 Healthcare Natural Language Processing Product Type, Application and Specification 6.3.2.1 Product A 6.3.2.2 Product B 6.3.3 Cerner Corp Healthcare Natural Language Processing Sales, Revenue, Price and Gross Margin (2012-2017) 6.3.4 Main Business/Business Overview 6.4 Nuance Communication 6.4.2 Healthcare Natural Language Processing Product Type, Application and Specification 6.4.2.1 Product A 6.4.2.2 Product B 6.4.3 Nuance Communication Healthcare Natural Language Processing Sales, Revenue, Price and Gross Margin (2012-2017) 6.4.4 Main Business/Business Overview 6.5 Microsoft Corp 6.5.2 Healthcare Natural Language Processing Product Type, Application and Specification 6.5.2.1 Product A 6.5.2.2 Product B 6.5.3 Microsoft Corp Healthcare Natural Language Processing Sales, Revenue, Price and Gross Margin (2012-2017) 6.5.4 Main Business/Business Overview 6.6 Health Fidelity 6.6.2 Healthcare Natural Language Processing Product Type, Application and Specification 6.6.2.1 Product A 6.6.2.2 Product B 6.6.3 Health Fidelity Healthcare Natural Language Processing Sales, Revenue, Price and Gross Margin (2012-2017) 6.6.4 Main Business/Business Overview 6.7 Apixio 6.7.2 Healthcare Natural Language Processing Product Type, Application and Specification 6.7.2.1 Product A 6.7.2.2 Product B 6.7.3 Apixio Healthcare Natural Language Processing Sales, Revenue, Price and Gross Margin (2012-2017) 6.7.4 Main Business/Business Overview 6.8 Linguamatics 6.8.2 Healthcare Natural Language Processing Product Type, Application and Specification 6.8.2.1 Product A 6.8.2.2 Product B 6.8.3 Linguamatics Healthcare Natural Language Processing Sales, Revenue, Price and Gross Margin (2012-2017) 6.8.4 Main Business/Business Overview 6.9 Optum 6.9.2 Healthcare Natural Language Processing Product Type, Application and Specification 6.9.2.1 Product A 6.9.2.2 Product B 6.9.3 Optum Healthcare Natural Language Processing Sales, Revenue, Price and Gross Margin (2012-2017) 6.9.4 Main Business/Business Overview 6.10 Dolbey Systems 6.10.2 Healthcare Natural Language Processing Product Type, Application and Specification 6.10.2.1 Product A 6.10.2.2 Product B 6.10.3 Dolbey Systems Healthcare Natural Language Processing Sales, Revenue, Price and Gross Margin (2012-2017) 6.10.4 Main Business/Business Overview For more information, please visit https://www.wiseguyreports.com/sample-request/905558-global-dry-wine-sales-market-report-2017


Grant
Agency: European Commission | Branch: FP7 | Program: CSA | Phase: ICT-2007.4.4 | Award Amount: 2.20M | Year: 2009

This proposal defines a support action project that brings together the researchers from international biomedical text-mining groups to address the difficult issue of annotating large text corpora with a large set of semantic types. We propose a collaborative approach to this annotation task in the form of an open challenge to the biomedical text-mining community. The task is the annotation of named entities in a large biomedical corpus, for a variety of semantic categories. The project delivers as outcome a large, collaboratively annotated corpus, marked with the mentions of biomedical entities. The annotated corpus becomes a resource for the community, to be used as a reference for improving text-mining applications. The biomedical text-mining research community has a long tradition of organizing such challenges, as a way of evaluating techniques, sharing technical knowledge, and helping to improve the results from text-mining programs. However, such challenges have typically addressed relatively small corpora in a narrow sub-domain, in part because the evaluation of the results is extremely long and costly. As a result, the generated annotated corpora are too small and are only narrowly annotated to be useful in a variety of text-mining applications. In contrast, we propose to create a broadly-scoped and large annotated corpus by integrating the annotations from different named entity recognition systems. Metadata will also be added to the corpus. The participating systems have different application scopes and annotation strategies, and therefore complement each other. As a consequence, the annotated corpus reflects these different scopes and strategies. A secondary goal of this project is to define a standardized format for representing the annotations contributed by the participants and comparing them effectively. Currently the lack of such a format hinders progress in the evaluation of named entity recognition systems.


Grant
Agency: European Commission | Branch: FP7 | Program: CP | Phase: ICT-2011.4.1 | Award Amount: 2.31M | Year: 2012

This project will provide multilingual terminologies and semantically annotated multilingual documents, e.g., patent texts, to improve the accessibility of scientific information from multilingual documents. The two SME partners will use these resources to improve the quality and functionality of their product offerings, viz. delivering multilingual search and text mining engines based on multilingual terminologies. Both SMEs will market these solutions to their customer base.The MANTRA project capitalizes on parallel document corpora from which translational correspondences will be computed by the use of different alignment methods. Fortunately, the biomedical domain, the application scenario of MANTRA, offers a rich variety of such parallel corpora. We will exploit these multilingual document sets to harvest terms and concept representations in different languages in order to augment currently available terminological resources such as the Medical Subject Headings (MeSH).The project partners will collaboratively build two types of resources: automatically enhanced multilingual terminologies and semantically annotated multilingual documents. The novelty of the latter resource derives from the fact that we solicit and orchestrate community efforts for building up these annotated resources, a procedure that has already been proven successful for the semantic enrichment of large-scale biomedical document corpora (CALBC project) which was executed by the project partners. The novelty of the first comes from a new combination of existing technologies in the area of statistical machine translation, named entity tagging and terminological resources. We start from statistically aligned, parallel documents on which named entity taggers are run to produce highly diverse semantic (named entity) annotations. These annotations signal concept mentions in the text which can then be linked to corresponding entries in relevant biomedical ontologies (from the UMLS, OBO or BioPortal umbrellas), and, in addition, provide the corresponding concept identifiers. Parallel named entity occurrences lacking links to the chosen ontologies can be considered as putative translation equivalents. Validated putative translation equivalents can then be used to enhance already given monolingual terminological resources. Both types of resources will be made available to the public for translation purposes and for search in and text mining from multilingual documents.


CAMBRIDGE, United Kingdom and BOSTON, Feb. 20, 2017 /PRNewswire/ -- Clinical NLP provider Linguamatics, and Varian Medical Systems, today announced that Varian will utilize Linguamatics' natural language processing (NLP) technology as part of the data analytics within Varian's 360 Oncology...


Grant
Agency: GTR | Branch: Innovate UK | Program: | Phase: Collaborative Research & Development | Award Amount: 269.85K | Year: 2014

CambridgeIP (CIP) the global innovation and intellectual property consultancy, with the University of Cambridge (UoC) and Royal Society of Chemistry (RSC), propose to develop novel touch interfaces to global scientific literature archives, enabling more intuitive search and analysis across multiple devices. With over 1 billion smartphone users now performing traditionally pc-based activites on their phone, new techniques need to be used for big data analysis. This will be achieved by using the latest advances in touch-screen and mobile interfaces, alongside semantic data analysis. Touch interfaces to the semantic elements will create an intuitive, accessible search platform enabling high level analysis and exploration of highly complex and specialist data sectors. Interactive data analytics and higher level data visualisations will be created to help view patterns within the data. The project will improve specialist and non-specialist access to valuable information from global scientific literature, enhancing R&D, education and entepreneurship.


Grant
Agency: GTR | Branch: Innovate UK | Program: | Phase: Collaborative Research & Development | Award Amount: 164.86K | Year: 2011

The current generation of language processing has had considerable success in extracting useful information from large amounts of unstructured text, whether this is research literature or social media. However, adapting to a new domain is often a laborious process, with respect both to diverse types of data (e.g. newswire vs. patent literature) and to the terminology used in a given domain (e.g. in medical practice vs. pharmaceutical research). Humans can perform these tasks on small data sets, but face a challenge in the face of massively increasing amounts of electronic text. The EVOKES project is exploiting distributional similarity techniques to accelerate key components of customisation - the recognition of concepts, and the creation or adaptation of terminologies that link terms to concepts.

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