Raul Rodriguez-Esteban

Raul Rodriguez-Esteban

@RaulREsteban

Followers204
Following122

BioNLP, data science, text & data mining, information science at Roche Pharmaceuticals research & development. Tweets are mine.

Basel, Switzerland
Joined on August 15, 2013
Statistics

We looked inside some of the tweets by @RaulREsteban and here's what we found interesting.

Inside 100 Tweets

Time between tweets:
8 days
Average replies
1
Average retweets
15
Average likes
43
Tweets with photos
26 / 100
Tweets with videos
0 / 100
Tweets with links
0 / 100

Special issue on Semantic Resources and Text Mining https://t.co/cqaLWlsWM8 @nactem_unimcr

Jane Lomax
14 days ago

Preprint of our work on the COVID-19 Open Research Dataset is now out: Reference ontology and database annotation of the COVID-19 Open Research Dataset (CORD-19) https://t.co/EK23QLeRZK

Quoted @SuGolder

Just published our article comparing social media to other sources to identify the side effects of statins. Social media provides information on side effects important to patients and more quickly than other sources. https://t.co/Q6410iRIv4

From our publication @DrugSafetyJour: "Patients on social media are proportionally far more likely to complain about musculoskeletal symptoms than other adverse events. Most adverse events showed a high level of agreement between Twitter and regulatory data" @UPennDBEI @nlm_news https://t.co/SodoC5PJaO

Quoted @mjp39

@ani_nenkova I looked at this some with @Xiaolei33 https://t.co/Sye3TaEhXH like the quoted tweet, our advice is to split train/dev/test chronologically, training on earlier data, tuning hyperparams on more recent data, and testing on latest. domain adaptation also improves robustness to time.

Analysis of temporal changes in reviews, news and tweets https://t.co/NayNO7GNdB

Siamak Barzegar from our Text Mining Unit at the Barcelona Supercomputing Center (BSC) presenting efforts to generate a Spanish cTakes contribution for processing clinical records in Spanish, also in teh context of the IctusNet and Plan TL efforts. https://t.co/hv2wXTOxwM https://t.co/UPRIixUt4K
4

Siamak Barzegar from our Text Mining Unit at the Barcelona Supercomputing Center (BSC) presenting efforts to generate a Spanish cTakes contribution for processing clinical records in Spanish, also in teh context of the IctusNet and Plan TL efforts. https://t.co/hv2wXTOxwM https://t.co/UPRIixUt4K

Existing progam of BioAsq 8 / CLEF2020 https://t.co/JjZyAyVCDl Great intro and overview of used methods for biomedical question answering. BioASQ and COVID-19 https://t.co/vdaL5vZi4S
2

Existing progam of BioAsq 8 / CLEF2020 https://t.co/JjZyAyVCDl Great intro and overview of used methods for biomedical question answering. BioASQ and COVID-19 https://t.co/vdaL5vZi4S

Quoted @UPennDBEI

Congrats🎉 to Graciela Gonzalez Hernandez (@gracielagon), who will join the National Library of Medicine Board of Scientific Counselors! @nlm_news @moorejh @UPennIBI #informatics https://t.co/DVpL9tpYP1

Congrats🎉 to Graciela Gonzalez Hernandez (@gracielagon), who will join the National Library of Medicine Board of Scientific Counselors! @nlm_news @moorejh @UPennIBI  #informatics https://t.co/DVpL9tpYP1

Honored to participate on the board! https://t.co/ut0p2kBvqL

BioAsq session program online (CLEF 2020) Great talks and free registration, including our Mesinesp track on semantic indexing of medical literature, clinical trials and health related public projects in Spanish. Thanks a lot George, Tasos and Anastasia https://t.co/L5KdsidRD6

A great venue for sharing your health NLP research this year ⁦@abchapman93⁩ ⁦@jianlinshihttps://t.co/vstIgXjZBa

Quoted @JFutoma

Excited to share our viewpoint, “The myth of generalizability in clinical research & ML in health care”, now out in @LancetDigitalH w/ Morgan Simons, @basslinetherapy @FinaleDoshi & Leo Celi https://t.co/bFtn0gGonc 1/x

Cool thread. I pretty much agree with everything here regarding our at times unreasonable overemphasis on cross site generalizability. It may be difficult to convince study sections though 🙂 https://t.co/6CCB9RmDLL

LBERT: Lexically-aware Transformers based Bidirectional Encoder Representation model for learning Universal Bio-Entity Relations. https://t.co/eyCsL54mc7 https://t.co/pY7Qay15UW

LBERT: Lexically-aware Transformers based Bidirectional Encoder Representation model for learning Universal Bio-Entity Relations. https://t.co/eyCsL54mc7 https://t.co/pY7Qay15UW

#nlproc folks: Has anyone used MTurk to get annotations for tweets to generate training data. Is this okay. What are best practices for anyone trying to do this. What are some ethical issues that we need to consider before doing this. How can one handle them appropriately.

Well, the bot announced it, so I should too :)

https://t.co/s3oqAoxUoj #COVID19 Scientific Evidence Explorer

Still in dev, feedback welcome.

@SimonSuster @YuliaOtmakhova @eltimster @eltoroquerie @jibmaird & Shevon Mendis, Zenan Zhai, Biaoyan Fang, Jey Han Lau @ARC_AIMedTech https://t.co/BeUejE64k1 https://t.co/VuWKzgRXfp

Well, the bot announced it, so I should too :) https://t.co/s3oqAoxUoj #COVID19 Scientific Evidence Explorer Still in dev, feedback welcome. @SimonSuster @YuliaOtmakhova @eltimster @eltoroquerie @jibmaird & Shevon Mendis, Zenan Zhai, Biaoyan Fang, Jey Han Lau @ARC_AIMedTech https://t.co/BeUejE64k1 https://t.co/VuWKzgRXfp

"This tool uses a ML model trained on 1.7M PubMed Central documents to recommend suitable journals based on the textual content of your bioRxiv preprint." ✨ https://t.co/UTY18LHOYv 
A stellar proof-of-concept leveraging the full text of preprints/OA articles. (ht @carlystrasser) https://t.co/sX6FRXj0s9
2

"This tool uses a ML model trained on 1.7M PubMed Central documents to recommend suitable journals based on the textual content of your bioRxiv preprint." ✨ https://t.co/UTY18LHOYv A stellar proof-of-concept leveraging the full text of preprints/OA articles. (ht @carlystrasser) https://t.co/sX6FRXj0s9

MIMIC-IV is public! ~70,000 ICU stays, deidentified, ready for research. It's only 7 GB! ... but with >300 million charted observations, there's a lot to dig through. Quick thread on the highlights. https://t.co/N6Rzrrkjxg

Recent advances of automated methods for searching and extracting genomic variant information from biomedical literature https://t.co/1NMghi2OfD

Quoted @coling2020

COLING’2020 IS GOING ENTIRELY VIRTUAL! 👉 https://t.co/TUVMwUvtq9

COLING2020 is entirely virtual, and our Social Media Mining 4 Health Research Workshop and Shared Tasks as well #SMM4H https://t.co/fksqOZ8a8q

Our work funded by @nlm_news - Deep neural networks ensemble for detecting medication mentions in tweets. On a naturally balanced corpus of tweets posted by 112 users (only 0.26% of 100K tweets mention medications), F1 of 78.8%. https://t.co/sqstB9axj3 @UPennIBI

Neural networks for open and closed Literature-based Discovery. https://t.co/jNtsI7V1ie

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