ArticleJournal of healthcare engineering2017
Noninvasive Evaluation of Portal Hypertension Using a Supervised Learning Technique.
Article in Journal of healthcare engineering, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.
What it found
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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
9 citing papers in PubMed, 1 synthesis or guideline pooled it, 15 citations in OpenAlex.
- Diagnosis of Liver Fibrosis Using Artificial Intelligence: A Systematic Review.Medicina (Kaunas, Lithuania) · 2023Pooled it
- Recent Advances in the Pathogenesis and Clinical Evaluation of Portal Hypertension in Chronic Liver Disease.Gut and liver · 2024Review
- Deep Neural Network Regression to Assist Non-Invasive Diagnosis of Portal Hypertension.Healthcare (Basel, Switzerland) · 2023Article
- Estimation of Physiologic Pressures: Invasive and Non-Invasive Techniques, AI Models, and Future Perspectives.Sensors (Basel, Switzerland) · 2023Review
- Artificial intelligence models for the diagnosis and management of liver diseases.Ultrasonography (Seoul, Korea) · 2023Article
- Artificial intelligence in the diagnosis of cirrhosis and portal hypertension.Journal of medical ultrasonics (2001) · 2022Review
- Artificial intelligence in gastroenterology: A state-of-the-art review.World journal of gastroenterology · 2021Review
- Cirrhotic portal hypertension: From pathophysiology to novel therapeutics.World journal of gastroenterology · 2020Review
- Endogenous motion of liver correlates to the severity of portal hypertension.World journal of gastroenterology · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Portal hypertension (PHT) is a key event in the evolution of different chronic liver diseases and leads to the morbidity and mortality of patients. The traditional reliable PHT evaluation method is a hepatic venous pressure gradient (HVPG) measurement, which is invasive and not always available or acceptable to patients. The HVPG measurement is relatively expensive and depends on the experience of the physician. There are many potential noninvasive methods to predict PHT, of which liver transient elastography is determined to be the most accurate; however, even transient elastography lacks the accuracy to be a perfect noninvasive diagnostic method of PHT. In this research, we are focusing on noninvasive PHT assessment methods that rely on selected best-supervised learning algorithms which use a wide set of noninvasively obtained data, including demographical, clinical, laboratory, instrumental, and transient elastography measurements. In order to build the best performing classification meta-algorithm, a set of 21 classification algorithms have been tested. The problem was expanded by selecting the best performing clinical attributes using algorithm-specific filtering methods that give the lowest error rate to predict clinically significant PHT. The suggested meta-algorithm objectively outperforms other methods found in literature and can be a good substitute for invasive PHT evaluation methods.
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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.