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An Overview of Explainable AI Studies in the Prediction of Sepsis Onset and Sepsis Mortality.
Nicolaou, Andria; Stylianides, Charithea; Sulaiman, Waqar A; Antoniou, Zinonas; Palazis, Lakis; Vavlitou, Anna; Pattichis, Constantinos S; Panayides, Andreas S.
Afiliación
  • Nicolaou A; Department of Computer Science, University of Cyprus, Nicosia, Cyprus.
  • Stylianides C; CYENS Centre of Excellence, Nicosia, Cyprus.
  • Sulaiman WA; CYENS Centre of Excellence, Nicosia, Cyprus.
  • Antoniou Z; Department of Computer Science, University of Cyprus, Nicosia, Cyprus.
  • Palazis L; 3aHealth, Nicosia, Cyprus.
  • Vavlitou A; State Health Services Organization, Cyprus.
  • Pattichis CS; State Health Services Organization, Cyprus.
  • Panayides AS; Department of Computer Science, University of Cyprus, Nicosia, Cyprus.
Stud Health Technol Inform ; 316: 808-812, 2024 Aug 22.
Article en En | MEDLINE | ID: mdl-39176915
ABSTRACT
Explainable artificial intelligence (AI) focuses on developing models and algorithms that provide transparent and interpretable insights into decision-making processes. By elucidating the reasoning behind AI-driven diagnoses and treatment recommendations, explainability can gain the trust of healthcare experts and assist them in difficult diagnostic tasks. Sepsis is characterized as a serious condition that happens when the immune system of the body has an extreme response to an infection, causing tissue and organ damage and leading to death. Physicians face challenges in diagnosing and treating sepsis due to its complex pathogenesis. This work aims to provide an overview of the recent studies that propose explainable AI models in the prediction of sepsis onset and sepsis mortality using intensive care data. The general findings showed that explainable AI can provide the most significant features guiding the decision-making process of the model. Future research will investigate explainability through argumentation theory using intensive care data focused on sepsis patients.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Inteligencia Artificial / Sepsis Límite: Humans Idioma: En Revista: Stud Health Technol Inform Asunto de la revista: INFORMATICA MEDICA / PESQUISA EM SERVICOS DE SAUDE Año: 2024 Tipo del documento: Article País de afiliación: Chipre Pais de publicación: Países Bajos
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Inteligencia Artificial / Sepsis Límite: Humans Idioma: En Revista: Stud Health Technol Inform Asunto de la revista: INFORMATICA MEDICA / PESQUISA EM SERVICOS DE SAUDE Año: 2024 Tipo del documento: Article País de afiliación: Chipre Pais de publicación: Países Bajos