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4928 search results found
  • Hirsch-card
    November 6, 2017 ILP Faculty Feature

    When the Science Advances Faster Than the System

  • September 25, 2020 MIT News

    Expanding access to the world’s top medical minds

  • July 1, 2015
    Department of Electrical Engineering and Computer Science

    Institute for Data, Systems and Society (IDSS): Application Domains

    Principal Investigator Munther Dahleh

  • Members Only

    Health Science Technologies @MIT

    Fri, March 26, 2021 Webinar
    Leading Edge Startup Workshop - DIGITAL HEALTH

    We've seen a tremendous progress in digital health in the last few years and COVID-19 has only accelerated the pace of change.  MIT Startup Exchange is convening the workshop on Friday March 26, 11am ET, to showcase the best of MIT Startups in this topic.  This workshop will present perspectives from industry leaders and startups on challenges and opportunities in digital health.

  • Sanki Engineering Co., Ltd.

    Tokyo 100, Japan
  • March 12, 2009
    MIT Energy Initiative

    Executive Education and Professional Development (Professional Development Institute)

    Principal Investigator Aruna Joglekar

  • September 10, 2015

    Science of Stem Cell in Iran

  • Olivier de Weck
    September 12, 2013 ILP Faculty Feature

    Future Proofing: Engineering for Change

    Olivier de Weck

  • February 18, 2010

    Intercepted Signals for Ionospheric Science Array (ISIS)

  • Andrew W. Lo - 2019 Life Science Conference

    December 10, 2019Conference Video Duration: 31:58

    Estimating and Predicting Clinical Trial Success Rates: A Data Science Approach

    All investors require some understanding of the potential risk and reward of a given venture before they’re willing to invest, and the less they know about it, the less capital will be available. This is especially true with biomedical assets in which the risks and rewards are equally outsized and hard to evaluate. Therefore, a prerequisite for addressing the so-called “Valley of Death” in early-stage biomedical R&D funding is better risk analytics. In this talk, Prof. Lo will describe his latest research on estimating historical probabilities of success for clinical trials and then applying machine-learning techniques to predict future success rates across a variety of drug/indication pairs. With more accurate assessments of these success probabilities, capital can be deployed more efficiently and at lower cost, thereby bringing greater amounts of capital into the biopharma industry at a time when capital is needed most.
     
    2019 MIT Increased Productivity in the Biopharmaceutical Industry Conference

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