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    1. Python For Artificial Intelligence

      Python For Artificial Intelligence

      楼主 只看作者 | 倒序 General AI AIMA - Python implementation of algorithms from Russell and Norvig's 'Artificial Intelligence: A Modern Approach' pyDatalog - Logic Programming engine in Python SimpleAI - Python implementation of many of the artificial intelligence algorithms described on the book "Artificial Intelligence, a Modern Approach". It focuses on providing an easy to use, well documented and tested library.

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    2. Fact over Fiction

      Politics is a distracting affair which I generally believe it’s best to stay out of if you want to be able to concentrate on research. Nevertheless, the US presidential election looks like something that directly politicizes the idea and process of research by damaging the association of scientists & students, funding for basic research, and creating political censorship.

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    3. Google set to deploy next-gen machine learning for Indian languages, IT News, Et Cio

      Google set to deploy next-gen machine learning for Indian languages, IT News, Et Cio

      NEW DELHI: Aiming to bring a billion people online and make the web more useful for them, Google India is slated to unveil new products on advancement in machine learning for the Indian languages, the company said on Friday.In an event to be organised here on April 25, Google will also share findings from a new report by Google and KPMG India, titled "Indian Languages-Defining India's Internet".Rajan Anandan, vice president, SouthEast Asia and India, Google, will address the event, the company said in a statement.In a bid to help Bengali speakers discover new information quickly, Google ...

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      Mentions: India Brazil Google
    4. Automated annotation and classification of BI-RADS assessment from radiology reports.

      Automated annotation and classification of BI-RADS assessment from radiology reports.

      Automated annotation and classification of BI-RADS assessment from radiology reports.

      J Biomed Inform. 2017 Apr 17;:

      Authors: Castro SM, Tseytlin E, Medvedeva O, Mitchell K, Visweswaran S, Bekhuis T, Jacobson RS

      Abstract The Breast Imaging Reporting and Data System (BI-RADS) was developed to reduce variation in the descriptions of findings. Manual analysis of breast radiology report data is challenging but is necessary for clinical and healthcare quality assurance activities.

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      Mentions: Naïve Bayes SVM NLP
    5. 6 Shades of Masking Your Data Database

      6 Shades of Masking Your Data Database

      Foundry is Redgate’s research and development division. We develop products and technologies for the Microsoft data platform. Each project progresses through Foundry’s four-stage product development process: Research, Concept, Prototype, and Beta. At each stage, the Foundry team is exploring the scope and potential for Redgate to develop a product. One of our projects, data masking , has seen us working to improve the management of sensitive data and synthesize more realistic data.

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      Mentions: Microsoft
    6. Google set to deploy next-gen learning for Indian languages

      Google set to deploy next-gen learning for Indian languages

      Google set to deploy next-gen machine learning for Indian languages TECHNOLOGY Google set to deploy next-gen machine learning for Indian languages IANS | April 22, 2017 (File) Google logo | AP In an event to be organised here on April 25, Google will also share findings from a new report by Google and KPMG India Aiming to bring a billion people online and make the web more useful for them, Google India is slated to unveil new products on advancement in machine learning for the Indian languages, the company said on Friday. In an event to be organised here on April 25 ...

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      Mentions: India Brazil Google
    7. Google set to deploy next-gen machine learning for Indian languages

      Google set to deploy next-gen machine learning for Indian languages

      A- A A+ Aiming to bring a billion people online and make the web more useful for them, Google India is slated to unveil new products on advancement in machine learning for the Indian languages, the company said on Friday. In an event to be organised here on April 25, Google will also share findings from a new report by Google and KPMG India, titled “Indian Languages-Defining India’s Internet”.

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      Mentions: India Brazil Google
    8. How Machine Learning and Data Can Improve Cancer Care Big Data

      How Machine Learning and Data Can Improve Cancer Care Big Data

      Over 1.5 million people are diagnosed with cancer each year in America alone. But despite these huge volumes, a tiny amount register for clinical trials. Indeed, research currently relies on data from just 3% of patients. MIT’s Regina Barzilay is hoping to rectify that. Via the MIT Stata Center, she leads a Machine Learning-based project that aims to derive insight from patient data.

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    9. Europe Machine Translation (MT) Market Report 2017 With sales, price, revenue and market share

      Europe Machine Translation (MT) Market Report 2017 With sales, price, revenue and market share

      OthersAbout Us: We are a leading repository of market research reports and solutions from the top publishers and market research companies across globe, catering to various industries. This large collection of reports assists organizations in decision-making on aspects such as market entry strategies, market sizing, market share analysis, competitive analysis, product portfolio analysis andopportunity analysis among others.

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      Mentions: India Italy France
    10. Worldwide Market for Computer Assisted Coding Market Is Expected to Reach $5.1 Billion by 2023; Finds New Report

      RSS Worldwide Market for Computer Assisted Coding Market Is Expected to Reach $5.1 Billion by 2023; Finds New Report Market Research Reports, Inc. has announced the addition of “Computer Assisted Coding: Market Shares, Strategies, and Forecasts, Worldwide, 2016 to 2022” research report to their website www.MarketResearchReports.com Lewes, DE -- ( SBWIRE ) -- 04/21/2017 -- Computer assisted coding of medical information uses natural language solutions to link the physician notes in an electronic patient record to the codes used for billing Medicare, Medicaid, and private insurance companies. Natural language processing is used determine the links to codes. 88% of the ...

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      Mentions: Medicare
    11. Singapore on Regulating Fintech: "Financial Regulators Should not be Afraid to Work Collaboratively" - Crowdfund Insider

      Singapore on Regulating Fintech: "Financial Regulators Should not be Afraid to Work Collaboratively" - Crowdfund Insider

      By JD Alois This is a special time of the year in Washington, DC. It is the annual Spring Meetings of the IMF and World Bank meaning a diverse group of policymakers and regulators descend upon the US Capitol clogging restaurants and over-booking hotels. This also means there is a diverse group of financial regulators in attending including representatives from Singapore.

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    12. Virtual CPE Market Players: AT&T, Intel, Verizon Wireless, Ericsson, Cisco, IBM, Dell, NEC, Orange Business Services

      5.1 Value chain analysis/Supply chain analysis 5.2 Porters five forces Continue………. The report for Virtual CPE Market of Market Research Future comprises of extensive primary research along with the detailed analysis of qualitative as well as quantitative aspects by various industry experts, key opinion leaders to gain the deeper insight of the market and industry performance.

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    13. IIIT-Hyderabad sets up seed fund network

      IIIT-Hyderabad sets up seed fund network

      Comments International Institute of Information Technology (IIIT)-Hyderabad has announced a seed fund network to invest in technology startups in its incubator and elsewhere. “The venture capitalists who are senior industry leaders with extensive domain knowledge and peer networks will assist in creating a structure to support the start-ups and further enhance IIIT-H’s startup ecosystem,” an official release in this regard said.

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    14. Is the legal sector embracing the tech revolution?

      Is the legal sector embracing the tech revolution?

      Dan Taylor, director of systems at Fletchers Solicitors, explains how the legal sector is opening its mind to the innovations tech has to offer. Shares Dan Taylor, director of systems at Fletchers Solicitors , explains how the legal sector is opening its mind to the innovations tech has to offer. At the start of 2017, the Law Society (guardians of the UK legal profession) published a report on the state of tech in the nation’s law firms .

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    15. Automated Transformation of openEHR Data Instances to OWL.

      Automated Transformation of openEHR Data Instances to OWL.

      Automated Transformation of openEHR Data Instances to OWL.

      Stud Health Technol Inform. 2016;223:63-70

      Authors: Haarbrandt B, Jack T, Marschollek M

      Abstract Standard-based integration and semantic enrichment of clinical data originating from electronic medical records has shown to be critical to enable secondary use.

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    16. Evaluation of the Terminology Coverage in the French Corpus LiSSa.

      Evaluation of the Terminology Coverage in the French Corpus LiSSa.

      Evaluation of the Terminology Coverage in the French Corpus LiSSa.

      Stud Health Technol Inform. 2017;235:126-130

      Authors: Cabot C, Soualmia LF, Grosjean J, Griffon N, Darmoni SJ

      Abstract Extracting concepts from medical texts is a key to support many advanced applications in medical information retrieval. Entity recognition in French texts is moreover challenged by the availability of many resources originally developed for English texts.

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    17. Development and Evaluation of a Case-Based Retrieval Service.

      Development and Evaluation of a Case-Based Retrieval Service.

      Development and Evaluation of a Case-Based Retrieval Service.

      Stud Health Technol Inform. 2017;235:186-190

      Authors: Pasche E, Chinali M, Gobeill J, Ruch P

      Abstract Identifying similar patients might greatly facilitate the treatment of a given patient, enabling to observe the response and outcome to a particular treatment. Case-based retrieval services dealing with natural language processing are of major importance to deal with the significant amount of unstructured clinical data.

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    18. Developing a Manually Annotated Corpus of Clinical Letters for Breast Cancer Patients on Routine Follow-Up.

      Developing a Manually Annotated Corpus of Clinical Letters for Breast Cancer Patients on Routine Follow-Up.

      Developing a Manually Annotated Corpus of Clinical Letters for Breast Cancer Patients on Routine Follow-Up.

      Stud Health Technol Inform. 2017;235:196-200

      Authors: Pitson G, Banks P, Cavedon L, Verspoor K

      Abstract This paper introduces the annotation schema and annotation process for a corpus of clinical letters describing the disease course and treatment of oestrogen receptor positive breast cancer patients, after completion of primary surgery and radiotherapy treatment.

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    19. Prevalence Estimation of Protected Health Information in Swedish Clinical Text.

      Prevalence Estimation of Protected Health Information in Swedish Clinical Text.

      Prevalence Estimation of Protected Health Information in Swedish Clinical Text.

      Stud Health Technol Inform. 2017;235:216-220

      Authors: Henriksson A, Kvist M, Dalianis H

      Abstract Obscuring protected health information (PHI) in the clinical text of health records facilitates the secondary use of healthcare data in a privacy-preserving manner.

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    20. Medical Text Classification Using Convolutional Neural Networks.

      Medical Text Classification Using Convolutional Neural Networks.

      Medical Text Classification Using Convolutional Neural Networks.

      Stud Health Technol Inform. 2017;235:246-250

      Authors: Hughes M, Li I, Kotoulas S, Suzumura T

      Abstract We present an approach to automatically classify clinical text at a sentence level. We are using deep convolutional neural networks to represent complex features. We train the network on a dataset providing a broad categorization of health information.

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    21. Acronym Disambiguation in Spanish Electronic Health Narratives Using Machine Learning Techniques.

      Acronym Disambiguation in Spanish Electronic Health Narratives Using Machine Learning Techniques.

      Acronym Disambiguation in Spanish Electronic Health Narratives Using Machine Learning Techniques.

      Stud Health Technol Inform. 2017;235:251-255

      Authors: Rubio-López I, Costumero R, Ambit H, Gonzalo-Martín C, Menasalvas E, Rodríguez González A

      Abstract Electronic Health Records (EHRs) are now being massively used in hospitals what has motivated current developments of new methods to process clinical narratives (unstructured data) making it possible to perform context-based searches.

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    22. Automated Classification of Semi-Structured Pathology Reports into ICD-O Using SVM in Portuguese.

      Automated Classification of Semi-Structured Pathology Reports into ICD-O Using SVM in Portuguese.

      Automated Classification of Semi-Structured Pathology Reports into ICD-O Using SVM in Portuguese.

      Stud Health Technol Inform. 2017;235:256-260

      Authors: Oleynik M, Patrão DFC, Finger M

      Abstract Pathology reports are a main source of information regarding cancer diagnosis and are commonly written following semi-structured templates that include tumour localisation and behaviour.

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    23. Personalized Guideline-Based Treatment Recommendations Using Natural Language Processing Techniques.

      Personalized Guideline-Based Treatment Recommendations Using Natural Language Processing Techniques.

      Personalized Guideline-Based Treatment Recommendations Using Natural Language Processing Techniques.

      Stud Health Technol Inform. 2017;235:271-275

      Authors: Becker M, Böckmann B

      Abstract Clinical guidelines and clinical pathways are accepted and proven instruments for quality assurance and process optimization. Today, electronic representation of clinical guidelines exists as unstructured text, but is not well-integrated with patient-specific information from electronic health records.

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