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GidMK's profile
Health Nerd
Health Nerd
Health Nerd
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@GidMK

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Health NerdVerified account

@GidMK

Epidemiologist. Writer (Guardian, Observer etc). "Well known research trouble-maker". PhDing at @UoW Host of @senscipod Email gidmk.healthnerd@gmail.com he/him

Sydney, New South Wales
theguardian.com/profile/gideon…
Joined November 2015

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    1. Matt Tenan, PhD ATC‏ @TenanATC 18 Mar 2019

      I apparently have a bit of a reputation as someone who is anti-machine learning or anti-AI when it comes to human research. This is a bit of misrepresentation of my views, and (I'd argue) a misrepresentation of the issues "statistics people" take with AI/ML as a whole. (THREAD)

      7 replies 82 retweets 224 likes
      Show this thread
    2. Matt Tenan, PhD ATC‏ @TenanATC 18 Mar 2019

      I personally think that AI/ML has a lot to bring to the table to enhance science, health and human performance. The problem is that the AI/ML crowd are over-selling their wares and often being disingenuous about what is current state-of-the art 2/10

      2 replies 9 retweets 65 likes
      Show this thread
    3. Matt Tenan, PhD ATC‏ @TenanATC 18 Mar 2019

      Issue 1: CLAIMING EVERYTHING IS MACHINE LEARNING. Just because AI/ML may use algebra or linear regression, doesn't mean it is AI/ML. Same goes for Nonlinear regression, correlation, logistic regression, or everything else that IS STATISTICS (or information theory, etc.) 3/10

      3 replies 6 retweets 64 likes
      Show this thread
    4. Matt Tenan, PhD ATC‏ @TenanATC 18 Mar 2019

      It's cool if you use statistics and statistical concepts properly. Really, we're a big-tent kind of people. Just don't claim you invented something you clearly did not. And no, stringing together multiple correlations in an automated way doesn't make it extra special. 4/10

      3 replies 2 retweets 41 likes
      Show this thread
    5. Matt Tenan, PhD ATC‏ @TenanATC 18 Mar 2019

      Issue 2: OMG THE HYPE MACHINE, MAKE IT STOP. Again, a lot of really good stuff is being trialed with AI/ML. You don't need to oversell the genuinely good work and advances being pioneered. Here's the thing, most "statistics people" are allergic to hype. 5/10

      2 replies 2 retweets 42 likes
      Show this thread
    6. Matt Tenan, PhD ATC‏ @TenanATC 18 Mar 2019

      Many human research statisticians work in areas of health where people can die or receive in appropriate treatments if we do our job wrong. It isn't to say we're perfect, but we work hard to be conservative and criticize our models so we're confident in the results. 6/10

      1 reply 2 retweets 42 likes
      Show this thread
    7. Matt Tenan, PhD ATC‏ @TenanATC 18 Mar 2019

      This is, I think, the main reason statisticians have issues with the AI/ML crowd: we can smell snake oil. The really good and avant garde AI/ML work gets lumped in with the utter nonsense directed at VC's and the pop media. 7/10

      2 replies 6 retweets 48 likes
      Show this thread
    8. Health Nerd‏Verified account @GidMK 18 Mar 2019
      Replying to @TenanATC

      The problem is that I think that often there's a big difference between what's exciting in a ML model and what's exciting clinically. Take the Apple Watch - seems very cool as a predictive tool, but basically useless clinically

      1 reply 0 retweets 1 like
    9. Matt Tenan, PhD ATC‏ @TenanATC 18 Mar 2019
      Replying to @GidMK

      The funny thing is that I have friends who worked on developing and validating the apple watch and their biggest gripe it that they kept the watch in development for so long in an attempt to limit edge cases. The amount of work going into dev was phenomenal.

      1 reply 1 retweet 2 likes
      Health Nerd‏Verified account @GidMK 18 Mar 2019
      Replying to @TenanATC

      See that makes perfect sense. As a mechanistic piece of work, it was amazing. But as a medical tool for a clinical issue, it was a complete waste of time. Big mismatch between the two I think

      3:45 PM - 18 Mar 2019
      • 1 Like
      • Matt Tenan, PhD ATC
      0 replies 0 retweets 1 like

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