Development and Validation of a Deep Learning Model for Prediction of Adult Physiological Deterioration
DETERIO demonstrates promise in the viability of a state value-estimation approach for predicting adult physiologic deterioration. It may outperform EDI while offering additional clinical utility in triage and clinician interaction with prediction confidence and explanations. Additional studies are needed to assess generalizability and real-world clinical impact.
Authors
- James M. Ford (ORCID: https://orcid.org/0000-0002-2513-7025)
- Shamim Nemati (ORCID: https://orcid.org/0000-0002-0520-4948)
- Joshua Le (ORCID: https://orcid.org/0000-0002-9742-6619)
- Gabriel Wardi (ORCID: https://orcid.org/0000-0002-4845-0217)
- Supreeth P. Shashikumar (ORCID: https://orcid.org/0000-0002-0348-4261)
- Atul Malhotra (ORCID: https://orcid.org/0000-0002-9509-1827)
- Nathan Yung (ORCID: https://orcid.org/0009-0008-9779-2302)
- Karandeep Singh (ORCID: https://orcid.org/0000-0001-6030-1663)
Institutions
- University of San Diego (US)
- University of California, San Francisco (US)
- University of Limerick (IE)
- University of California System (US)
- University of California San Diego (US)
- University of California San Diego Medical Center (US)
Publication Details
- Journal
- Critical Care Explorations
- Published
- 2024-09-01
- DOI
- https://doi.org/10.1097/cce.0000000000001151
- Citations
- 3
- Primary Topic
- Sepsis Diagnosis and Treatment
- Type
- article
- Field-Weighted Citation Impact
- 1.16