Tweetovi
- Tweetovi, trenutna stranica.
- Tweetovi i odgovori
Blokirali ste korisnika/cu @smadarshilo
Jeste li sigurni da želite vidjeti te tweetove? Time nećete deblokirati korisnika/cu @smadarshilo
-
Smadar Shilo proslijedio/la je Tweet
AOP
#ResearchHighlight: Electronic health records to predict gestational#diabetes mellitus (£) https://go.nature.com/2GDa2CU pic.twitter.com/ZnQRz5NpJY
Hvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
Smadar Shilo proslijedio/la je Tweet
A new computer algorithm can predict in the early stages of pregnancy, or even before pregnancy has occurred, which women are at a high risk of gestational diabetes, according to a study in
@NatureMedicine. https://go.nature.com/2RqOUFb pic.twitter.com/r6qb5kV1CO
Hvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
Smadar Shilo proslijedio/la je Tweet
Nice study from
@segal_eran: a machine-learning approach to predict gestational diabetes mellitus (GDM) from 588,622 pregnancies with high accuracy (auROC = 0.85), plus a simpler model based on 9 questions that also is accurate and easy (auROC = 0.80).https://www.nature.com/articles/s41591-019-0724-8 …Hvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
Smadar Shilo proslijedio/la je Tweet
This work is an ideal demonstration of how Machine Learning should be used in Biomedicine. All steps are very well-thought-out. Do disease predictions from EHR, do not search for genes, they are hard to bring to the Clinicshttps://twitter.com/segal_eran/status/1216972218388942848 …
Hvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
Smadar Shilo proslijedio/la je Tweet
Superb work by the people at
@suinleelab lab! Had so much impact even before being published here. Heavily used this framework with our recent study on prediction of gestational diabetes from nationwide EHRs: https://www.nature.com/articles/s41591-019-0724-8 … https://twitter.com/EricTopol/status/1218635928589045760 …pic.twitter.com/5PlfK9tIPs
Hvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
Smadar Shilo proslijedio/la je Tweet
Description of large datasets and development of counterfactual prediction using complex models is the ongoing revolution. Herein a nice review discussing trade-offs between depth of phenotype and cohort dimension.
#datascience#MachineLearning#BigDatahttps://twitter.com/segal_eran/status/1217530912407158785 …
Hvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
Smadar Shilo proslijedio/la je Tweet
Health data are being generated and collected at an unprecedented scale, but whether big data will truly revolutionize healthcare is still a matter of much debate, according to a Review in
@NatureMedicine. https://go.nature.com/2tf3h7v pic.twitter.com/eHvlxDOcj9
Hvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
Smadar Shilo proslijedio/la je Tweet
Important paper. 2 key concepts: the axes of health data and the "deep cohorts". Deep cohorts should be considered by research agencies when it's about addressing questions that require multidimensional data (+ deep cohorts can be meta-analyzed and include a small control arm) https://twitter.com/segal_eran/status/1217530912407158785 …pic.twitter.com/WgqUQRgm2q
Hvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
Smadar Shilo proslijedio/la je Tweet
Very proud to have been able to contribute to this amazing work, alongside Dr Becca Feldman of
@ClalitResearch. Wonderful achievement led by@segal_eran. And now - working to implement it at@ClalitHealth.https://twitter.com/EricTopol/status/1216762347756277760 …
Hvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
Smadar Shilo proslijedio/la je Tweet
2) Paper led by Eran Segal group
@WeizmannInstSci w/ our own Becca Feldman & top notch clinician team from Rabin MC. Already perfectly explained by Eran at: https://lnkd.in/gppqxA8 The best part in this, is we intend to go bench-to-bedside at Clalit, on both, asap! 3/3Prikaži ovu nitHvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
Smadar Shilo proslijedio/la je Tweet
• A detailed and informative read on big data in
#healthcare from@segal_eran et al in@NatureMedicine. IMHO, the importance of longitudinal data can not be understated— along with the need to increase the utilization of longitudinal analytics methods.Prikaži ovu nitHvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
Smadar Shilo proslijedio/la je Tweet
Leveraging nationwide electronic
#health records from over 500k pregnancies in Israel,@segal_eran & colleagues develop a#machinelearning approach able to predict gestational#diabetes with high accuracy at early stages of#pregnancy https://go.nature.com/2R1luy4#BigData#preventionpic.twitter.com/CJuuYbWKh4
Hvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
Smadar Shilo proslijedio/la je Tweet
Thank you
@JulianaAssisG@Metabolcenter ! Here is the@nature list of 11 tips for working with#bigdata https://go.nature.com/30s4ZhD and the@segal_eran reviewhttps://go.nature.com/2QYx39xHvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
Smadar Shilo proslijedio/la je Tweet
RT
@Sarah_Finer: A fabulous paper for those interested in GDM and/of health data science. Routine health data and machine learning brings game-changing, early prediction of GDM. (Needs validation in multiethnic and higher prevalence populations though...)https://twitter.com/segal_eran/status/1216972218388942848 …
Hvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
Smadar Shilo proslijedio/la je Tweet
Great work on this review, led by
@H_Rossman and@smadarshilo !Prikaži ovu nitHvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
Smadar Shilo proslijedio/la je Tweet
We discuss the great need for new approaches to drug development, and how human multi-omics data and physiological measurements at scale from deeply phenotyped cohorts may be one such direction, considered one of the most promising potentials of analyzing big data in medicinepic.twitter.com/pLyPFEx7BU
Prikaži ovu nitHvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
Smadar Shilo proslijedio/la je Tweet
We provide an overview of the geographical distribution of the main biobanks and cohort studies that are currently collecting and analyzing health datapic.twitter.com/edr66INDf9
Prikaži ovu nitHvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
Smadar Shilo proslijedio/la je Tweet
We discuss how big data is analyzed, how massive datasets may achieve the potential of medical data analyses, how to bridge the gap between the data and our understanding and knowledge of human health We cover Descriptive analysis, Prediction analysis, Counterfactual prediction
Prikaži ovu nitHvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
Smadar Shilo proslijedio/la je Tweet
We propose that medium-sized cohorts of hundreds or tens of thousands of people represent an interesting operating point, allowing collection of full molecular and phenotypic data on enough people We term these ‘deep cohorts’, as our own 10K project: http://www.weizmann.ac.il/sites/Project10K/ …pic.twitter.com/CbdcIwLZiG
Prikaži ovu nitHvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
Smadar Shilo proslijedio/la je Tweet
We discuss various tradeoffs in constructing human cohorts such as that between the scale of the data gathered (axis N) and the depth of the data (axis D), and show where different cohorts lie, including mega biobanks (e.g., UKBiobank), genetic datasets, EHR datasets, etc.pic.twitter.com/YUMb742YjE
Prikaži ovu nitHvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi
Čini se da učitavanje traje već neko vrijeme.
Twitter je možda preopterećen ili ima kratkotrajnih poteškoća u radu. Pokušajte ponovno ili potražite dodatne informacije u odjeljku Status Twittera.