ArticleScientific reports2022
Prediction and risk assessment of sepsis-associated encephalopathy in ICU based on interpretable machine learning.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 4 of them syntheses that pooled it.
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Who cites it
17 citing papers in PubMed, 4 syntheses or guidelines pooled it, 27 citations in OpenAlex.
- Explainable AI for critical care: a systematic review of interpretable models for sepsis and ICU mortality prediction.BMC medical informatics and decision making · 2026Pooled it
- Risk prediction models for sepsis-associated encephalopathy: a systematic evaluation and meta-analysis.PeerJ · 2026Pooled it
- Diagnostic models for sepsis-associated encephalopathy: a comprehensive systematic review and meta-analysis.Frontiers in neurology · 2025Pooled it
- Systematic review of risk prediction models for sepsis-associated brain dysfunction.Frontiers in neurologyPooled it
- Targeting GLS and LPIN2 in renal fibroblasts: potential therapeutic targets for kidney stone disease identified by integrated multi-omics analysis.Urolithiasis · 2026Article
- A 24-hour landmark machine learning model for predicting new-onset sepsis-associated encephalopathy.Frontiers in medicine · 2026Article
- Online Clinical Calculator for Predicting 28-Day Mortality in Older Adult Patients With Sepsis-Associated Encephalopathy: Retrospective Study Using MIMIC-IV.JMIR medical informatics · 2025Article
- Subinhibitory Concentrations of Oxacillin Alter Motility, Biofilm Production, and Transformation Capability of the Oxacillinase-Producing Acinetobacter baumannii.Current microbiology · 2025Article
- Role of Acorus calamus extract in reducing exosome secretion by targeting Rab27a and nSMase2: a therapeutic approach for breast cancer.Molecular biology reports · 2025Article
- Early prediction of sepsis associated encephalopathy in elderly ICU patients using machine learning models: a retrospective study based on the MIMIC-IV database.Frontiers in cellular and infection microbiology · 2025Article
- Elucidation of population stratifying markers and selective sweeps in crossbred Landlly pig population using genome-wide SNP data.Mammalian genome : official journal of the International Mammalian Genome Society · 2024Article
- Enhancing predictions with a stacking ensemble model for ICU mortality risk in patients with sepsis-associated encephalopathy.The Journal of international medical research · 2024Article
- Not deep enough: Modeling the effect of shallow placement of the DaVinci Xi "bariatric" long trocar on the muscular abdominal wall.Surgical endoscopy · 2023Article
- Nutritional and metabolic factors in amyotrophic lateral sclerosis.Nature reviews. Neurology · 2023Review
- Early detection of lung cancer using artificial intelligence-enhanced optical nanosensing of chromatin alterations in field carcinogenesis.Scientific reports · 2023Article
- High Mobility Group Box 1 (HMGB1): Potential Target in Sepsis-Associated Encephalopathy.Cells · 2023Review
- Optimizing the prediction of sepsis-associated encephalopathy with cerebral circulation time utilizing a nomogram: a pilot study in the intensive care unit.Frontiers in neurology · 2023Article
Corrections and comments
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Authors and funding
4 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Sepsis-associated encephalopathy (SAE) is a major complication of sepsis and is associated with high mortality and poor long-term prognosis. The purpose of this study is to develop interpretable machine learning models to predict the occurrence of SAE after ICU admission and implement the individual prediction and analysis. Patients with sepsis admitted to ICU were included. SAE was diagnosed as glasgow coma score (GCS) less than 15. Statistical analysis at baseline was performed between SAE and non-SAE. Six machine learning classifiers were employed to predict the occurrence of SAE, and the adjustment of model super parameters was performed by using Bayesian optimization method. Finally, the optimal algorithm was selected according to the prediction efficiency. In addition, professional physicians were invited to evaluate our model prediction results for further quantitative assessment of the model interpretability. The preliminary analysis of variance showed significant differences in the incidence of SAE among patients with pathogen infection. There were significant differences in physical indicators like respiratory rate, temperature, SpO
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