SynthesisIndian journal of gastroenterology : official journal of the Indian Society of Gastroenterology2023
Application and impact of Lasso regression in gastroenterology: A systematic review.
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Who cites it
27 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Prediction model developed on the basis of meta-analysis in the field of medicine: a systematic survey and methodological summaries.BMC medical research methodology · 2025Pooled it
- Identification and validation of a robust inflammation-related three-gene signature for the diagnosis of neonatal necrotizing enterocolitis via machine learning integration.Pediatric surgery international · 2026Article
- Prognostic significance and pathological correlation analysis of DKK4 in colorectal cancer.Translational cancer research · 2026Article
- Machine Learning Model Predicts Recurrent Clostridioides difficile Infection in Patients With Inflammatory Bowel Disease (Recur CDI-IBD).The American journal of gastroenterology · 2026Article
- Article
- S-palmitoylation-related genes in Crohn's disease: Bioinformatic identification and validation.Biomolecules & biomedicine · 2025Article
- A multi-tissue integration of immunocytes and inflammaging biomarkers predicts biological age through LASSO-optimized modeling.Biogerontology · 2025Article
- Development and internal validation of a nomogram to predict postoperative atrial fibrillation in elderly patients with lung cancer.Journal of thoracic disease · 2025Article
- Machine learning identifies KRT8 dysregulation and endothelial remodeling in Moyamoya disease.Scientific reports · 2025Article
- Development and validation of a LASSO-based nomogram for predicting anastomotic leakage in elderly patients after laparoscopic gastrectomy.Journal of gastrointestinal oncology · 2025Article
- Unveiling etiology and mortality risks in community-acquired pneumonia: A machine learning approach.Biomolecules & biomedicine · 2025Article
- Identification of Diagnostic Biomarkers for Colorectal Polyps Based on Noninvasive Urinary Metabolite Screening and Construction of a Nomogram.Cancer medicine · 2025Article
- Exploring biomarkers and molecular mechanisms of Type 2 diabetes mellitus promotes colorectal cancer progression based on transcriptomics.Scientific reports · 2025Article
- Commentary: Analysis of risk factors for painful diabetic peripheral neuropathy and construction of a prediction model based on Lasso regression.Frontiers in endocrinology · 2025Article
- Establishment of a nomogram based on Lasso Cox regression for albumin combined with systemic immune-inflammation index score to predict prognosis in advanced pancreatic carcinoma.Frontiers in oncology · 2025Article
- Artificial Intelligence-Guided Identification of IGFBP7 as a Critical Indicator in Lactic Metabolism Determines Immunotherapy Response in Stomach Adenocarcinoma.Journal of cellular and molecular medicine · 2025Article
- Prediction of prognosis in T4 or N3 locally advanced nasopharyngeal carcinoma receiving chemoradiotherapy using machine learning methods.Frontiers in oncology · 2025Article
- A data-driven method for surgeon-specific difficulty assessment in third molar extraction.Frontiers in medicine · 2025Article
- Study on the Therapeutic Effect and Mechanism of Gu Pi Sheng Ji Hua Zhuo Jie Du Decoction in Inducing Remission of Crohn's Disease.International journal of general medicine · 2025Article
- Analysis of prognostic risk factors and risk management measures for patients with ischemic stroke and bloodstream infection based on machine learning.Frontiers in cellular and infection microbiology · 2025Article
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Abstract
Least absolute shrinkage and selection operator (Lasso) regression is a statistical technique that can be used to study the effects of clinical variables in outcome prediction. In this study, we aimed at systematically reviewing the application of Lasso regression in gastroenterology for developing predictive models and providing a method of performing Lasso regression. A comprehensive search strategy was conducted in PubMed, Embase and Cochrane CENTRAL databases (Keywords: lasso regression; gastrointestinal tract/diseases) following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Studies were screened for eligibility based on pre-defined selection criteria and the data was extracted using a standardized form. Total 16 studies were included, comprising a diverse range of gastroenterological disease-related outcomes. Sample sizes ranged from 134 to 8861 subjects. Eleven studies reported liver disease-related prediction models, while five focused on non-hepatic etiology models. Lasso regression was applied for variable selection, risk prediction and model development, with various validation methods and performance metrics used. Model performance metrics included Area Under the Receiver Operating Characteristics (AUROC), C-index and calibration plots. In gastroenterology, Lasso regression has been used in various diseases such as inflammatory bowel disease, liver disease and esophageal cancer. It is valuable for complex scenarios with many predictors. However, its effectiveness depends on high-quality and complete data. While it identifies important variables, it doesn't provide causal interpretations. Therefore, cautious interpretation is necessary considering the study design and data quality.
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