Machine Learning-Based Prediction Model for ICU Mortality After Continuous Renal Replacement Therapy Initiation in Children
These are the first ML models to predict survival at ICU and hospital discharge in children and young adults receiving CRRT. RF outperformed other models for predicting ICU mortality. Future studies should expand the input variables, conduct a more sophisticated feature selection, and use deep learning algorithms to generate more precise models.
Authors
- Shina Menon (ORCID: https://orcid.org/0000-0001-8473-0307)
- Aadil Kakajiwala (ORCID: https://orcid.org/0000-0002-4172-916X)
- Katja M. Gist (ORCID: https://orcid.org/0000-0002-7427-670X)
- Rachana Srivastava (ORCID: https://orcid.org/0000-0002-6588-0315)
- Danielle E. Soranno (ORCID: https://orcid.org/0000-0001-7019-809X)
- Gérard Cortina (ORCID: https://orcid.org/0000-0003-3700-2117)
- Danny T Y Wu (ORCID: https://orcid.org/0000-0002-7658-3754)
- Sameer V. Thadani (ORCID: https://orcid.org/0000-0001-5178-9839)
- Tzu-Chun Wu (ORCID: https://orcid.org/0000-0001-8343-4872)
Institutions
- Cincinnati Children's Hospital Medical Center (US)
- Apple (Israel) (IL)
- Children's National (US)
- Innsbruck Medical University (AT)
- University of California, Los Angeles (US)
- Universität Innsbruck (AT)
- Cincinnati Health Department (US)
- Collaborative Group (United States) (US)
- Texas Children's Hospital (US)
- Indiana University – Purdue University Indianapolis (US)
- University of Cincinnati (US)
- University of Cincinnati Medical Center (US)
- Stanford University (US)
Publication Details
- Journal
- Critical Care Explorations
- Published
- 2024-12-01
- DOI
- https://doi.org/10.1097/cce.0000000000001188
- Citations
- 4
- Primary Topic
- Sepsis Diagnosis and Treatment
- Type
- article
- Field-Weighted Citation Impact
- 1.55