5,095 research outputs found

    Prospect patents, data markets, and the commons in data-driven medicine : openness and the political economy of intellectual property rights

    Get PDF
    Scholars who point to political influences and the regulatory function of patent courts in the USA have long questioned the courtsā€™ subjective interpretation of what ā€˜thingsā€™ can be claimed as inventions. The present article sheds light on a different but related facet: the role of the courts in regulating knowledge production. I argue that the recent cases decided by the US Supreme Court and the Federal Circuit, which made diagnostics and software very difficult to patent and which attracted criticism for a wealth of different reasons, are fine case studies of the current debate over the proper role of the state in regulating the marketplace and knowledge production in the emerging information economy. The article explains that these patents are prospect patents that may be used by a monopolist to collect data that everybody else needs in order to compete effectively. As such, they raise familiar concerns about failure of coordination emerging as a result of a monopolist controlling a resource such as datasets that others need and cannot replicate. In effect, the courts regulated the market, primarily focusing on ensuring the free flow of data in the emerging marketplace very much in the spirit of the ā€˜free the dataā€™ language in various policy initiatives, yet at the same time with an eye to boost downstream innovation. In doing so, these decisions essentially endorse practices of personal information processing which constitute a new type of public domain: a source of raw materials which are there for the taking and which have become most important inputs to commercial activity. From this vantage point of view, the legal interpretation of the private and the shared legitimizes a model of data extraction from individuals, the raw material of information capitalism, that will fuel the next generation of data-intensive therapeutics in the field of data-driven medicine

    Data Mining in Large-Scale Clinical Visit Data for Rett Syndrome Patients

    Get PDF
    Rett syndrome (RTT) is a rare neurological disorder that predominantly affects girls. Research on RTT has mostly centered around gene mutations and possibility of cure using gene therapy. In this thesis we perform the first large scale systematic study of RTT patient records. The thesis has two major goals. One is to identify behavioral groups and the other is to study the association of medications and behavior or conditions. To achieve the first goal we apply standard clustering techniques like non-negative matrix factorization and k-means. We identify behavioral groups which could be used by clinicians for formulating better treatments. For the second goal we start with the most popular existing technique, disproportionality analysis, and make necessary adaptations for our data set. We then generalize this method and suggest an alternate approach which efficiently answers which medication caused the most change in a behavior. We test both approaches and show that the medications shown to decrease seizures the most are indeed those prescribed for the same. Using this as a tool, clinicians can identify possible side effects of medications

    Ancient and historical systems

    Get PDF

    Knowledge-based Biomedical Data Science 2019

    Full text link
    Knowledge-based biomedical data science (KBDS) involves the design and implementation of computer systems that act as if they knew about biomedicine. Such systems depend on formally represented knowledge in computer systems, often in the form of knowledge graphs. Here we survey the progress in the last year in systems that use formally represented knowledge to address data science problems in both clinical and biological domains, as well as on approaches for creating knowledge graphs. Major themes include the relationships between knowledge graphs and machine learning, the use of natural language processing, and the expansion of knowledge-based approaches to novel domains, such as Chinese Traditional Medicine and biodiversity.Comment: Manuscript 43 pages with 3 tables; Supplemental material 43 pages with 3 table

    Content Based Image Retrieval (CBIR) in Remote Clinical Diagnosis and Healthcare

    Full text link
    Content-Based Image Retrieval (CBIR) locates, retrieves and displays images alike to one given as a query, using a set of features. It demands accessible data in medical archives and from medical equipment, to infer meaning after some processing. A problem similar in some sense to the target image can aid clinicians. CBIR complements text-based retrieval and improves evidence-based diagnosis, administration, teaching, and research in healthcare. It facilitates visual/automatic diagnosis and decision-making in real-time remote consultation/screening, store-and-forward tests, home care assistance and overall patient surveillance. Metrics help comparing visual data and improve diagnostic. Specially designed architectures can benefit from the application scenario. CBIR use calls for file storage standardization, querying procedures, efficient image transmission, realistic databases, global availability, access simplicity, and Internet-based structures. This chapter recommends important and complex aspects required to handle visual content in healthcare.Comment: 28 pages, 6 figures, Book Chapter from "Encyclopedia of E-Health and Telemedicine

    Integrative methods for analyzing big data in precision medicine

    Get PDF
    We provide an overview of recent developments in big data analyses in the context of precision medicine and health informatics. With the advance in technologies capturing molecular and medical data, we entered the area of ā€œBig Dataā€ in biology and medicine. These data offer many opportunities to advance precision medicine. We outline key challenges in precision medicine and present recent advances in data integration-based methods to uncover personalized information from big data produced by various omics studies. We survey recent integrative methods for disease subtyping, biomarkers discovery, and drug repurposing, and list the tools that are available to domain scientists. Given the ever-growing nature of these big data, we highlight key issues that big data integration methods will face
    • ā€¦
    corecore