Arjun (Raj) Manrai

@arjunmanrai

Assistant Professor , . Stats+ML for improving clinical decision making and scientific reproducibility.

Boston, MA
Vrijeme pridruživanja: srpanj 2014.

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  1. Prikvačeni tweet
    27. sij

    Excited to announce a new version of our course at Harvard, BMI704: Data Science for Medical Decision Making, and to be teaching with . We'll release lectures, data, and an interactive text of data science methods at link below. Sched + Readings:

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  2. "In my opinion, the careers of Trevor Hastie and Rob Tibshirani highlight the best of what happens when statisticians interact richly with machine learning researchers." - great post on the benefits of bridging stats & ML by

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  3. proslijedio/la je Tweet
    13. sij

    This is a patient safety issue. GC’s understand genetics and probability more than many doctors (see MD skills: )

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  4. proslijedio/la je Tweet
    12. sij
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  5. proslijedio/la je Tweet
    10. sij

    So proud of undergrad student Luke Melas Kyriazi who is now a Rhodes Scholar! (and great mentorship by )

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  6. proslijedio/la je Tweet
    7. sij

    The importance of reproducibility and replication for models in , and the under-appreciated barriers for this to be achieved by and

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  7. proslijedio/la je Tweet
    6. sij
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  8. proslijedio/la je Tweet
    6. sij

    Presenting "cui2vec" today at . Work done with and (and many others!) on extracting medical knowledge across multiple massive data sources

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  9. proslijedio/la je Tweet
    6. sij

    "[...] machine learning researchers moving into medical applications could [should] adhere to standard reporting guidelines such as TRIPOD, CONSORT, and SPIRIT, which are now being adapted for machine learning and artificial intelligence applications"

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  10. proslijedio/la je Tweet
    6. sij

    At a minimum, a model should be reproduced, and ideally replicated, before it is deployed in a clinical setting

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  11. 6. sij

    Reproducing neural architecture search for some ML models can cost 3 R01 grants, but do most medical applications need this? Our take on the top challenges to reproducibility of machine learning in medicine in the latest :

    , , i još njih 3
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  12. proslijedio/la je Tweet
    6. sij

    New viewpoint worth reading about reproducible machine learning by . Reproducing machine learning models can be expensive (>1M$ just to rerun the algorithms) but vital before implemented in clinical practice

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  13. proslijedio/la je Tweet
    6. sij

    New article with & where we outline some specific reproducibility challenges for ML in healthcare. Main issues: - Data sharing - Code documentation - Expense of reproduction Read the full article now in :

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  14. 18. pro 2019.

    Incredibly privileged to work alongside this group and proud of all they accomplished in 2019. Excited for the next year!

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  15. 16. pro 2019.

    ⁩ at ⁦⁩ today demonstrating the power of human networks to shape our health and our lives.

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  16. 10. pro 2019.

    Breadth and depth on display at ⁦

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  17. 2. pro 2019.

    Amazing opportunity for undergrads wanting to do cutting edge biomedical informatics and machine learning research at !

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  18. 2. pro 2019.

    Honoring 's Pete Szolovits at , shares 3 articles by Pete that were decades ahead of their time and are being recapitulated today.

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  19. 26. stu 2019.

    Incredibly proud of my student Luke Melas-Kyriazi for being selected as a Rhodes Scholar. Keep an eye out for his machine learning+medicine work at ⁦⁩!

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  20. proslijedio/la je Tweet
    19. stu 2019.

    After seven amazing years in Boston, I am both sad and excited to (finally) be able to officially announce that I'll be returning to my native Australia in 2020 to start a new adventure!

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