Multi-modal prediction of extracorporeal support—a resource intensive therapy, utilizing a large national database
We developed a hierarchical model for continuous ECMO use prediction, utilizing a large multicenter dataset incorporating both static and time series variables of various granularities. This novel approach reflects the nuanced decision-making process inherent in ECMO initiation and has the potential to be used as an early alert tool to guide patient triage and ECMO resource allocation. Future directions include prospective validation and generalizability on non-COVID-19 refractory respiratory failure, aiming to improve patient outcomes.
Authors
- Neel Shah (ORCID: https://orcid.org/0000-0001-8873-6482)
- Bing Xue (ORCID: https://orcid.org/0000-0002-9162-098X)
- Philip Payne (ORCID: https://orcid.org/0000-0002-9532-2998)
- Ahmed S. Said (ORCID: https://orcid.org/0000-0002-1215-2664)
- D. H. Zhu
Institutions
- Washington University in St. Louis (US)
- Artificial Intelligence in Medicine (Canada) (CA)
Publication Details
- Journal
- JAMIA Open
- Published
- 2024-12-26
- DOI
- https://doi.org/10.1093/jamiaopen/ooae158
- Citations
- 2
- Primary Topic
- Mechanical Circulatory Support Devices
- Type
- article
- Field-Weighted Citation Impact
- 0.37
Funders
- Washington University in St. Louis
- Children's Discovery Institute
- St. Louis Children's Hospital
- National Center for Advancing Translational Sciences