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

Authors

Institutions

Publication Details

Journal
Journal of Medical Internet Research
Published
2026-03-11
DOI
https://doi.org/10.2196/88952
Primary Topic
Intensive Care Unit Cognitive Disorders
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

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

Mian Ge, Yihan Zhang, Yuheng Luo, Linan Zhang et al.
Journal of Medical Internet Research
Intensive Care Unit Cognitive Disorders
article

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

Mian Ge, Yihan Zhang, Yuheng Luo, Linan Zhang, Jing Yang, Weifeng Yao, Ziqing Hei, Zhendong Ding, Chaojin Chen
article en

Abstract

No abstract available for this paper.
Journal of Medical Internet ResearchVol. 28
Sun Yat-sen University (CN), Third Affiliated Hospital of Sun Yat-sen University (CN), Guangzhou Electronic Technology (China) (CN)
Quality Education
Openalex Percentile: Top 28%
Intensive Care Unit Cognitive Disorders
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.