Patrick Schwab

@schwabpa

Principal Architect for in . Prev: PhD Zurich. I tweet about all things , and .

Zurich, Switzerland
Vrijeme pridruživanja: srpanj 2013.

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  1. Prikvačeni tweet
    15. stu 2018.

    Our work on learning to diagnose Parkinson's from smartphones has been accepted . Using mPower cohort (n=1853), we show that we can distinguish between people with and without PD at 0.85 AUC and that attention uncovers the important data segments

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  2. 29. sij

    Using smartphone data from the Floodlight Open study (n=774), we trained deep learning models that identify digital biomarkers for multiple sclerosis, and showed that these digital biomarkers contain significant signal (AUC=0.88) for diagnosing MS Link:

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  3. proslijedio/la je Tweet
    28. sij

    Ever wonder, are there any machine learning applications actually being used to care for patients in health care? If yes, check out our new review: In it, we ( ) present 21 ML products translated into clinical care THREAD

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  4. proslijedio/la je Tweet
    28. sij

    Keep in mind: "The current medical diagnostic field to show that less than 0·1% (14 of 20,000) studies were of sufficient methodological quality for clinical implementation" source:

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  5. 28. sij

    New paper out on crowdsourcing digital biomarker algorithms for Parkinson's disease. We present a wide range of methods for identifying typical symptoms and disease status from wearable sensors, and report their performance in the PD DREAM challenge Link:

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  6. proslijedio/la je Tweet
    15. sij
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  7. proslijedio/la je Tweet
    14. sij

    Check out the paediatric intensive care (PIC) database - 12,000 patients admitted from 5 ICUs in China - all publicly available!

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  9. proslijedio/la je Tweet
    20. pro 2019.

    Teaming up with our fellows at the we have showed how the two largest CRISPR-Cas9 cancer dependency datasets to date are consistent with each other and can be integrated 1/7

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  10. 13. pro 2019.

    Nenad Tomasev is presenting their very thorough work on developing a clinical warning score acute kidney injury (AKI) at ML4HC

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  11. 12. pro 2019.
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  12. 12. pro 2019.

    Boyi Li presenting their very exciting work on positional normalisation (a channel-wise alternative to batch normalisation). They show impressive improvements by including moment information in the network.

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  13. 10. pro 2019.

    Rahul Singh (w ) presents his work on Instrumental Variable Kernel Regression for counterfactual estimation

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  14. 7. pro 2019.

    Or try it out yourself at

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  15. 7. pro 2019.

    I will be presenting causal explanations (CXPlain, poster 163) for any machine learning model on Tuesday from 10.45 to 12.45 in East Hall B+C at - come see me if you want to chat!

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  16. proslijedio/la je Tweet
    25. stu 2019.

    Causality for Machine Learning. (arXiv:1911.10500v1 [cs.LG])

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  17. proslijedio/la je Tweet
    22. stu 2019.

    A first look at Google’s EMR interface. Demo version, simple in design, incorporates many familiar google suite functions such as autocomplete, text hover, smart search.

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  18. proslijedio/la je Tweet
    13. stu 2019.

    "Learning From [Mouse] Brains How to Regularize Machines," Li et al.: Wild stuff - they showed images to mice, recorded the mice's neural activity, made a model of that, then penalized not-mouse-brain-y representations when training new classifiers.

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  19. proslijedio/la je Tweet

    NEW: NEJM just published some really interesting results from the Apple Heart Study, which involved more than 400,000 participants. Cardiologists like have questions and concerns, but say there's some promise. Our deep dive:

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  20. 12. stu 2019.

    Dose response networks (DRNets) that learn to estimate individual dose response to appear at . Paper includes new open source benchmarks and metrics for estimating dose response. Link: Code:

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  21. 31. lis 2019.

    The basic idea behind this thread of research is to throw more compute, more simulations at big state spaces. This idea will never translate to anything outside a very narrow domain, since real-world tasks are difficult *precisely* because we do not have accurate simulators.

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