Renard Lab
@renardlab.bsky.social
Data Analytics and Computational Statistics research group @HPI ✨ Account run by Prof. Bernhard Renard, Yannick Hartmaring, and Ferdous Nasri.
In his project in collaboration with the Icahn School of Medicine at Mount Sinai @hpims.bsky.social, he is exploring the effect of biological administration timing on treatment outcomes in patients with Inflammatory Bowel Disease. Supervised by Dr. Susanne Ibing @sibing.bsky.social.
March 6, 2025 at 7:48 AM
In his project in collaboration with the Icahn School of Medicine at Mount Sinai @hpims.bsky.social, he is exploring the effect of biological administration timing on treatment outcomes in patients with Inflammatory Bowel Disease. Supervised by Dr. Susanne Ibing @sibing.bsky.social.
Her work produces daily immunity indices for each regional location, given vaccination and infection data for different infectious diseases, which in turn can be used to, e.g., predict the spread of disease 🧬🤧🌐
February 7, 2025 at 4:33 PM
Her work produces daily immunity indices for each regional location, given vaccination and infection data for different infectious diseases, which in turn can be used to, e.g., predict the spread of disease 🧬🤧🌐
She has published numerous papers and presented her work at various conferences.
Her next step will focus on her research collaborations across the atlantic. 🚀
Her next step will focus on her research collaborations across the atlantic. 🚀
January 31, 2025 at 3:02 PM
She has published numerous papers and presented her work at various conferences.
Her next step will focus on her research collaborations across the atlantic. 🚀
Her next step will focus on her research collaborations across the atlantic. 🚀
Her achievements are outstanding and we are very proud to be celebrating this day with her!💐
She started her PhD as a part of the Böttinger Lab, spent some time working with Mount Sinai in NY and switched to our Lab later. She led projects with many students, one of which won the DMEA Sparks Award🏆
She started her PhD as a part of the Böttinger Lab, spent some time working with Mount Sinai in NY and switched to our Lab later. She led projects with many students, one of which won the DMEA Sparks Award🏆
January 31, 2025 at 3:02 PM
Her achievements are outstanding and we are very proud to be celebrating this day with her!💐
She started her PhD as a part of the Böttinger Lab, spent some time working with Mount Sinai in NY and switched to our Lab later. She led projects with many students, one of which won the DMEA Sparks Award🏆
She started her PhD as a part of the Böttinger Lab, spent some time working with Mount Sinai in NY and switched to our Lab later. She led projects with many students, one of which won the DMEA Sparks Award🏆
With an ablation study, we demonstrated the added value of information derived from clinical notes not only for the computable phenotyping, but also the disease prediction task.
🧵 6/6
🧵 6/6
January 2, 2025 at 2:53 PM
With an ablation study, we demonstrated the added value of information derived from clinical notes not only for the computable phenotyping, but also the disease prediction task.
🧵 6/6
🧵 6/6
When comparing coded conditions between identified cases and controls, we saw significant overrepresentation of GI-related conditions in cases, indicating the diagnostic delay of the disease.
🧵 5/6
🧵 5/6
January 2, 2025 at 2:53 PM
When comparing coded conditions between identified cases and controls, we saw significant overrepresentation of GI-related conditions in cases, indicating the diagnostic delay of the disease.
🧵 5/6
🧵 5/6
We found that adding information on age at diagnosis extracted from the clinical notes improves the phenotyping performance and allows to better distinguish between referral and incident cases, compared phenotyping mainly relying on structured clinical data.
🧵 4/6
🧵 4/6
January 2, 2025 at 2:53 PM
We found that adding information on age at diagnosis extracted from the clinical notes improves the phenotyping performance and allows to better distinguish between referral and incident cases, compared phenotyping mainly relying on structured clinical data.
🧵 4/6
🧵 4/6
For automated cohort identification, we compared two computable phenotyping approaches with different levels of NLP and information extracted from clinical notes incorporated.
🧵 3/6
🧵 3/6
January 2, 2025 at 2:53 PM
For automated cohort identification, we compared two computable phenotyping approaches with different levels of NLP and information extracted from clinical notes incorporated.
🧵 3/6
🧵 3/6
Diagnostic delay is a common problem in Crohn’s disease, and with delayed treatment induction leading to overall worsened outcomes. This study aimed to automatically identify newly diagnosed patients to use pre-diagnostic EHR for data disease prediction.
🧵 2/6
🧵 2/6
January 2, 2025 at 2:53 PM
Diagnostic delay is a common problem in Crohn’s disease, and with delayed treatment induction leading to overall worsened outcomes. This study aimed to automatically identify newly diagnosed patients to use pre-diagnostic EHR for data disease prediction.
🧵 2/6
🧵 2/6