ArticleBMC medical informatics and decision making2025
Machine learning techniques in hepatic encephalopathy: a scoping review.
Article in BMC medical informatics and decision making, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- Management of Refractory Hepatic Encephalopathy.Digestive diseases and sciences · 2026Review
- Artificial intelligence and digital transformation of gastroenterology and hepatology: A critical review of clinical applications and future challenges.World journal of hepatology · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionHepatic encephalopathy (HE) is defined as a specific type of cerebral dysfunction that encompasses a wide range of cognitive, psychomotor, and psychiatric disturbances. The burgeoning field of Artificial Intelligence (AI), particularly Machine Learning (ML), offers promising avenues for early detection and enhanced control of HE. This scoping review aims to provide a consolidated overview of AI’s role in the diagnosis and management of HE, thereby informing and guiding future research endeavors in this domain.
methodsWe followed Arksey and O’Malley’s methodological framework to perform this scoping review, using PubMed, Web of Science, Scopus, ScienceDirect, and IEEE databases to find relevant articles. We also utilized the PRISMA standard to report our review in a standardized manner. Studies that focused on the applications of AI or ML techniques in relation to the prediction or diagnosis of HE disease were included.
resultsOut of the 231 articles identified, 20 were ultimately included in this scoping review. The integration of artificial neural networks and expert systems represented an early and pioneering approach in applying AI to HE. Among supervised learning algorithms, Support Vector Machine emerged as the most frequently employed technique in HE research, based on our review of the selected studies. Notably, the primary application of AI in HE studies has been predictive modeling (n = 14), followed by five studies focused on classifying HE stages and one study analyzing patient survival using AI methodologies.
conclusionsThis scoping review highlights the growing use of AI and ML diagnostic models and predictive tools utilizing various data types. These advancements have the potential to positively impact patient outcomes. Future research should focus on validating and implementing these AI models in clinical settings to assess their real-world effectiveness in improving patient care.
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What OpenQuestion holds
Registered trials
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.