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Prikvačeni tweet
Our work on learning to diagnose Parkinson's from smartphones has been accepted
@RealAAAI. 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 https://arxiv.org/abs/1810.01485 pic.twitter.com/fCKNwxEBAW
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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: https://arxiv.org/abs/2001.09748 pic.twitter.com/bVzSfRbWxx
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Patrick Schwab proslijedio/la je Tweet
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: https://emj.emg-health.com/wp-content/uploads/sites/2/2020/01/A-Path-for-Translation-of-Machine-Learning.....pdf … In it, we (
@Sehj_K@michaelgao88) present 21 ML products translated into clinical care THREADPrikaži ovu nitHvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
Patrick Schwab proslijedio/la je Tweet
Keep in mind: "The current
#AI medical diagnostic field to show that less than 0·1% (14 of 20,000) studies were of sufficient methodological quality for clinical implementation" https://www.thelancet.com/journals/landig/article/PIIS2589-7500(20)30004-2/fulltext …@LancetDigitalH source: https://www.thelancet.com/journals/landig/article/PIIS2589-7500(19)30123-2/fulltext …pic.twitter.com/6ZRunEKlJi
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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: https://www.biorxiv.org/content/10.1101/2020.01.13.904722v1.full …pic.twitter.com/NX56VGIWiV
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Patrick Schwab proslijedio/la je Tweet
For
#AI and medicine, we need to get over the preoccupation of man vs machines. It's a plus story. https://www.healthnewsreview.org/2020/01/a-machine-versus-doctors-fixation-masks-important-questions-about-artificial-intelligence/ … by@mcjaklevic@HealthNewsRevu w/@DrHughHarvey@RogueRad@cragcrest@LizSzabo@jeremyhsuHvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
Patrick Schwab proslijedio/la je Tweet
Check out the paediatric intensive care (PIC) database - 12,000 patients admitted from 5 ICUs in China - all publicly available!https://www.nature.com/articles/s41597-020-0355-4 …
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Patrick Schwab proslijedio/la je Tweet
2019 will be remembered for the 1st hyper-personalized, true N of 1, from sequence to therapy in < 1 year https://www.nature.com/articles/s41591-019-0700-3.pdf …
@NatureMedicine https://worldin.economist.com/article/17520/edition2020creating-personalised-drugs?navigation=true&category=extra …@TheEconomist@natashaloder https://www.nejm.org/doi/pdf/10.1056/NEJMoa1813279?articleTools=true …@NEJM@timyu@BostonChildrens https://www.statnews.com/2018/10/22/a-tailor-made-therapy-may-have-halted-a-rare-disease/ …pic.twitter.com/wTNq22LQfH
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Patrick Schwab proslijedio/la je Tweet
Teaming up with our
@CancerDepMap fellows at the@broadinstitute we have showed how the two largest CRISPR-Cas9 cancer dependency datasets to date are consistent with each other and can be integrated@DepMapSanger@sangerinstitute@NatureComms 1/7https://www.sanger.ac.uk/news/view/analysis-crispr-cas9-datasets-leads-largest-genetic-screen-resource-cancer-research …Prikaži ovu nitHvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
Nenad Tomasev
@weballergy@GoogleAI is presenting their very thorough work on developing a clinical warning score acute kidney injury (AKI) at ML4HC#NeurIPS2019@NeurIPSConfpic.twitter.com/c4vURJwKfX
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Paper Link: https://arxiv.org/abs/1907.04312
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Boyi Li
@Cornell 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.#NeurIPS2019@NeurIPSConfpic.twitter.com/JWlyntqEol
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Rahul Singh (w
@ArthurGretton) presents his work on Instrumental Variable Kernel Regression for counterfactual estimation#NeurIPS2019@NeurIPSConfpic.twitter.com/zxJuQ0k9Tm
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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
#NeurIPS2019@NeurIPSConf - come see me if you want to chat!Prikaži ovu nitHvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
Patrick Schwab proslijedio/la je Tweet
Causality for Machine Learning. (arXiv:1911.10500v1 [cs.LG]) https://ift.tt/2KRSyVO
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Patrick Schwab proslijedio/la je Tweet
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.https://youtu.be/P3SYqcPXqNk
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Patrick Schwab proslijedio/la je Tweet
"Learning From [Mouse] Brains How to Regularize Machines," Li et al.: https://arxiv.org/abs/1911.05072 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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Patrick Schwab 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:https://www.cnbc.com/2019/11/13/apple-releases-heart-study-results.html …
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Dose response networks (DRNets) that learn to estimate individual dose response to appear at
#AAAI2020. Paper includes new open source benchmarks and metrics for estimating dose response. Link: https://arxiv.org/abs/1902.00981 Code: https://github.com/d909b/drnet pic.twitter.com/t54ucAeoa2
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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.https://twitter.com/mtrc/status/1189842933907935232 …
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