Renard Lab
@RenardLab
Followers
168
Following
92
Media
46
Statuses
156
Data Analytics and Computational Statistics research group @HPI_DE ✨ Account run by Prof. Renard & Ferdous Nasri (@ferbsx).
Potsdam, Germany
Joined July 2022
The 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.
1
0
1
Ferdous Nasri presenting her work on the 'Spatio-temporal immunity index tool for infectious diseases' at the Digital Health Summit @UCT_news in Cape Town, South Africa!
Happy that I had the honour of presenting my work at the Digital Health Summit in Cape Town, and got the chance to get into great discussions with researchers on the continent!. Thanks to @ictdchick for organising the summit and bringing us all together 💐
1
1
2
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.
0
0
0
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
1
0
0
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
1
0
0
For automated cohort identification, we compared two computable phenotyping approaches with different levels of NLP and information extracted from clinical notes incorporated. 🧵 3/6
1
0
0
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
1
0
0
New paper: Electronic Health Records-Based Identification of Newly Diagnosed Crohn’s Disease Cases. By @SusanneIbing , @JulianHug0, et al., now available in Artificial Intelligence in Medicine. 🧵 1/6 Thread below:.
1
0
3
RT @HPI_MS: Check out “Electronic Health Records-based identification of newly diagnosed Crohn’s Disease cases”, a new paper from HPI·MS Ph….
0
3
0
. and that's a wrap from our group at #ECCB2024!. Thank you and congratulations to @EloLab_fi, @nyronen and.the organising teams for bringing together such a great conference!. Looking forward to the next @ECCBinfo 🚀
1
2
16
They show that feature driving the mean prediction of low-density lipoprotein cholesterol considerably differ from the features driving aleatoric uncertainty. This highlights the value of uncovering hidden factors behind predictive uncertainty.
0
0
1
Simon Witzke presenting his poster @ECCBinfo! #ECCB2024. @witsyke and @Iv_Pascal recent work in explaining aleatoric uncertainty in regression can help increase transparency in digital health.
1
3
11
Check out Ferdous Nasri's poster (P410) @ECCBinfo now!. She will tell you about the spatiotemporal immunity index tool for infectious diseases ✨. #ECCB2024 #immunity #modelling #spatiotemporal
0
2
15
Towards connecting deep learning and ODEs: We present a framework to optimize synthetic dataset characteristics for time series forecasting in biological systems. Meet @JulianZabbarov at poster 188, today from 5-6:30pm @ECCBinfo. #ECCB2024
0
2
12
@YoSukrit @steen_lab Listen to the talk held today by Tobias Pietz at 15:25 in Logomo Hall!. #ECCB2024 @ECCBinfo @BostonChildrens @HPI_DE @FU_Berlin.
0
1
3
PEPerMINT is based on a graph neural network structure and also leverages amino acid sequence embeddings. It is a joint project with the labs of Katharina Baum, Sukrit Gupta (@YoSukrit) and Hanno Steen (@steen_lab).
1
0
0
We proudly present PEPerMINT @ECCBinfo #ECCB2024, a new deep learning method for replacing missing peptide measurement values for improved downstream analyses!. #DeepLearning.
1
4
11