Category: Healthcare

Superheroes of Deep Learning Vol 2: Machine Learning for Healthcare

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Cover of Volume 2: Machine Learning for Healthcare, of the Superheroes of Deep Learning comic series
The year is 2021. A self-replicating 0.12 micron protein has taken the world hostage and inconvenienced millions of conservative politicians, who suddenly care about civil liberties. Flush with cash and deep minds, the AI illuminati have convened a great conference to confront their common foe
Meanwhile spurned by the ML elite, DAG-man de-stresses on a California beach with some sunnies and funnies… [DAG-Man] On beach, lounge chair, sipping beach cocktail with little umbrella & pineapple slice, Reading DL Superheroes Vol 1. [Tweeting out on Blackberry --- “Translation please? I grew up with Popeye and Little Red Riding Hood....” ]
[Somewhere in Westchester county...] [Giant mansion, inspired by Prof X’s school for the gifted—long trail of mafia town cars lined up outside. At the entrance is a long line and a registration desk, with 1000s of people in line to get their badge and nametag] [Poster outside says International Conference for ML Superheroes 2021] [All the factions of the ML community are here, The DL Superheroes, Rigor Police (dressed up like British bobbies), The Algorithmic Justice League, The Causal Conspirators, and the Symbol Slappers ]
Anon char 1: How did the Superheroes afford this place? Anon char 2: Was the Element AI acquihire more lucrative than we thought? Anon char 3: ... I heard Captain Convolution got in early on Gamestop Anon char 4: Shhh… he’s about to speak... The GodFather: I look around, I look around, and I see a lot of familiar faces. [nods at each] Don Valiant, Donna Boulamwini, GANfather.... It’s not every day that we gather the entire family under one roof. We unite here today And put aside our differences because a gathering threat imperils our common interests You may already know...
[Tensorial Professor] The curse of dimensionality? [Kernel Scholkopf] Confounding? [Code Poet] Injustice? [The GANfather] Schmidhubering?
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A Pedant’s Guide to MLHC 2017

By David Kale and Zachary Lipton

Starting Friday, August 18th and lasting two days, Northeastern University in Boston hosted the eighth annual Machine Learning for Healthcare (MLHC) conference. This year marked MLHC’s second year as a publishing conference with an archival proceedings in the Journal of Machine Learning Research (JMLR). Incidentally, the transition to formal publishing venue in 2016 coincided with the name change to MLHC from Meaningful Use of Complex Medical Data, denoted by the memorable acronym MUCMD (pronounced MUCK-MED).

From its beginnings at Children’s Hospital Los Angeles as a non-archival symposium, the meeting set out to address the following problem:

  • Machine learning, even then, was seen as a powerful tool that can confer insights and improve processes in domains with well-defined problems and large quantities of interesting data.
  • In the course of treating patients, hospitals produce massive streams of data, including vital signs, lab tests, medication orders, radiologic imaging, and clinical notes, and record many health outcomes of interest, e.g., diagnoses. Moreover, numerous tasks in clinical care present as well-posed machine learning problems.
  • However, despite the clear opportunities, there was surprisingly little collaboration between machine learning experts and clinicians. Few papers at elite machine learning conferences addressed problems in clinical health and few machine learning papers were submitted to the elite medical journals.

Continue reading “A Pedant’s Guide to MLHC 2017”