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Interpretability of an FDA-authorized AI/ML sepsis diagnostic tool improved by SHAP values
0
Zitationen
51
Autoren
2026
Jahr
Abstract
These results suggest health care providers value transparency into AI/ML algorithms designed for clinical use, and that Shapley values are a useful approach to providing that transparency, which in turn may improve tool adoption and clinical utility.
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Autoren
- Gregory Watson
- Grace L. Staples
- Robin Carver
- Akhil Bhargava
- Carlos G. Lopez-Espina
- Lee Schmalz
- Farhan Ali
- Peter S. Antkowiak
- Saleem Azad
- Ramona Berghea
- Lavneet Chawla
- M.J. Crisp
- Alon Dagan
- Francisco R. Davila
- Hugo Dávila
- Carmen DeMarco
- Amanda Doodlesack
- Aimee Espinosa
- Neil R. Evans
- Clinton Ezekiel
- Andrew Friederich
- Falgun Gosai
- Alexandra Halalau
- Karthik Iyer
- Max S Kravitz
- Niko Kurtzman
- J. H. Lee
- Nicholas Maddens
- Roneil G. Malkani
- Stockton Mayer
- Vikram Oke
- Ashok Palagiri
- Roshni Patel
- Lekshminarayan Raghavakurup
- Samuel Raouf
- Eric Reseland
- Farid Sadaka
- Deesha Sarma
- Scott Smith
- Tatyana Shvilkina
- Matthew Sims
- Sahib Singh
- B. Stenson
- Aamer Syed
- Muleta Tafa
- Kurian Thomas
- Sihai Dave Zhao
- Ruoqing Zhu
- Rashid Bashir
- Bobby Reddy
- Nathan I Shapiro
Institutionen
- Chicago Institute for Psychoanalysis(US)
- LifeBridge Health(US)
- Beth Israel Deaconess Medical Center(US)
- Jefferson Hospital(US)
- Beaumont Hospital, Royal Oak(US)
- OSF Saint Francis Medical Center(US)
- University of California, Davis(US)
- Harvard University(US)
- Emory Healthcare(US)
- Emory University(US)
- Jesse Brown VA Medical Center(US)
- Mercy Hospital(US)
- University of Illinois Urbana-Champaign(US)