ArticleJournal of medical Internet research2025
A Supervised Explainable Machine Learning Model for Perioperative Neurocognitive Disorder in Liver-Transplantation Patients and External Validation on the Medical Information Mart for Intensive Care IV Database: Retrospective Study.
Article in Journal of medical Internet research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed.
- Artificial Intelligence for Perioperative Risk Prediction in Anesthesiology: A Bibliometric and Knowledge-Mapping Analysis.Healthcare (Basel, Switzerland) · 2026Review
- Mitochondrial-Based Nanomedicine in Treatment of Liver Cancer.Molecular biotechnology · 2026Review
- Correction: A Supervised Explainable Machine Learning Model for Perioperative Neurocognitive Disorder in Liver-Transplantation Patients and External Validation on the Medical Information Mart for Intensive Care IV Database: Retrospective Study.Journal of medical Internet research · 2026Article
- Artificial Intelligence in Post-Liver Transplantation: A Scoping Review of Comparative Model Performance.Journal of clinical medicine · 2026Review
- Synergistic Efficacy of Lactobacillus plantarum Supernatant-Preconditioned BM-MSCs in Alleviating Depression, Anxiety-Like Behavior, and Memory Impairment in a Rat Stress Model by Suppressing the NF-κB Pathway.Molecular neurobiology · 2026Article
- Integrating Genomics, Radiomics, and Pathomics in Oncology: A Scoping Review and a Framework for AI-Enabled Surgomics.Bioengineering (Basel, Switzerland) · 2026Review
- Towards advanced AI-based solutions for securing IoMT in smart health information systems.Scientific reports · 2026Article
- Elucidating the Mechanism of Modified Yiguanjian against Anti-Tuberculosis Drug-Induced Liver Injury via Integrated Pharmacology and Metabolomics.Chemistry & biodiversity · 2026Article
- Explainable artificial intelligence in anesthesiology prediction models: bridging the gap from black-box algorithms to clinical decision support.Frontiers in medicine · 2026Review
- Association between preoperative anemia and postoperative delirium in elderly patients undergoing total hip arthroplasty with combined spinal-epidural anesthesia: A retrospective cohort study.Journal of anesthesia and translational medicine · 2025Article
- Artificial Intelligence in the Diagnosis and Management of Atrial Fibrillation.Diagnostics (Basel, Switzerland) · 2025Review
- Chronic Stress Disrupts Immune and Endocrine Axis, Inducing Persistent Behavioral Impairments in Male Rats: In Silico and In Vivo Insights.Neurochemical research · 2025Article
- Development and validation of machine learning models to predict esophagogastric variceal rebleeding risk in HBV-related cirrhosis after endoscopic treatment: a prospective multicenter study.EClinicalMedicine · 2025Article
- Autoencoder-Assisted Stacked Ensemble Learning for Lymphoma Subtype Classification: A Hybrid Deep Learning and Machine Learning Approach.Tomography (Ann Arbor, Mich.) · 2025Article
- Theoretical analysis of MOFs for pharmaceutical applications by using machine learning models to predict loading capacity and cell viability.Scientific reports · 2025Article
- Machine Learning-Powered Smart Healthcare Systems in the Era of Big Data: Applications, Diagnostic Insights, Challenges, and Ethical Implications.Diagnostics (Basel, Switzerland) · 2025Review
- Enhancing cardiac disease detection via a fusion of machine learning and medical imaging.Scientific reports · 2025Article
- Exploring the Role of Artificial Intelligence in Smart Healthcare: A Capability and Function-Oriented Review.Healthcare (Basel, Switzerland) · 2025Review
- Electroacupuncture pretreatment ameliorates anesthesia and surgery-induced cognitive dysfunction in aged rats: insights from gut microbiota modulation.Frontiers in microbiology · 2025Article
- Integration of machine learning and large language models for screening and identifying key risk factors of acute kidney injury after cardiac surgery.Frontiers in medicine · 2025Article
Corrections and comments
- Erratum issued
Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundPatients undergoing liver transplantation (LT) are at risk of perioperative neurocognitive dysfunction (PND), which significantly affects the patients' prognosis.
objectiveThis study used machine learning (ML) algorithms with an aim to extract critical predictors and develop an ML model to predict PND among LT recipients.
methodsIn this retrospective study, data from 958 patients who underwent LT between January 2015 and January 2020 were extracted from the Third Affiliated Hospital of Sun Yat-sen University. Six ML algorithms were used to predict post-LT PND, and model performance was evaluated using area under the receiver operating curve (AUC), accuracy, sensitivity, specificity, and F
resultsIn the development cohort, 201 out of 751 (33.5%) patients were diagnosed with PND. The logistic regression model achieved the highest AUC (0.799) in the internal validation set, with comparable AUC in the temporal external (0.826) and MIMIC-Ⅳ validation sets (0.72). The top 3 features contributing to post-LT PND diagnosis were the preoperative overt hepatic encephalopathy, platelet level, and postoperative sequential organ failure assessment score, as revealed by the Shapley additive explanations method.
conclusionsA real-time logistic regression model-based online predictor of post-LT PND was developed, providing a highly interoperable tool for use across medical institutions to support early risk stratification and decision making for the LT recipients.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.