| Title: | Subtyping Gastrointestinal Surgical Outcomes from Real World Data: A Comprehensive Analysis of UK Biobank. |
| Journal: | AMIA Annual Symposium Proceedings |
| Published: | 11 Jan 2024 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/38222374/ |
Publication 8960
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Abstract
Chronic gastrointestinal (GI) conditions, such as inflammatory bowel diseases (IBD), offer a promising opportunity to create classification systems that can enhance the accuracy of predicting the most effective therapies and prognosis for each patient. Here, we present a novel methodology to explore disease subtypes using our open-sourced BiomedSciAI toolkit. Applying methods available in this toolkit on the UK Biobank, including subpopulation-based feature selection and multi-dimensional subset scanning, we aimed to discover unique subgroups from GI surgery cohorts. Of a 12,073-patient cohort, a subgroup of 440 IBD patients was discovered with an increased risk of a subsequent GI surgery (OR: 2.21, 95% CI [1.81-2.69]). We iteratively demonstrate the discovery process using an additional cohort (with a narrower definition of GI surgery). Our results show that the iterative process can refine the subgroup discovery process and generate novel hypotheses to investigate determinants of treatment response.</p>
12 Authors
- Uri Kartoun
- Kingsley Njoku
- Tesfaye Yadete
- Sivan Ravid
- Eileen Koski
- William Ogallo
- Joao Bettencourt-Silva
- Natasha Mulligan
- Jianying Hu
- Julia Liu
- Thaddeus Stappenbeck
- Vibha Anand
1 Application
| Application ID | Title |
|---|---|
| 95318 | Multi-modal AI representation learning to accelerate discovery of composite biomarkers |
Enabling scientific discoveries that improve human health