ArticleTheScientificWorldJournal2013
Mortality predicted accuracy for hepatocellular carcinoma patients with hepatic resection using artificial neural network.
Article in TheScientificWorldJournal, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 2 of them syntheses that pooled it.
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Who cites it
19 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Machine learning to guide clinical decision-making in abdominal surgery-a systematic literature review.Langenbeck's archives of surgery · 2022Pooled it
- Prognostic role of artificial intelligence among patients with hepatocellular cancer: A systematic review.World journal of gastroenterology · 2020Pooled it
- Predicting hepatocellular carcinoma survival with artificial intelligence.Scientific reports · 2025Article
- Prediction of Long-Term Poor Clinical Outcomes in Cerebral Venous Thrombosis Using Neural Networks Model: The BEAST Study.International journal of general medicine · 2024Article
- The Role of Radiomics and AI Technologies in the Segmentation, Detection, and Management of Hepatocellular Carcinoma.Cancers · 2022Review
- Role of three-dimensional printing and artificial intelligence in the management of hepatocellular carcinoma: Challenges and opportunities.World journal of gastrointestinal oncology · 2022Review
- Machine Learning-Based Short-Term Mortality Prediction Models for Patients With Cancer Using Electronic Health Record Data: Systematic Review and Critical Appraisal.JMIR medical informatics · 2022Review
- Challenges and opportunities in the application of artificial intelligence in gastroenterology and hepatology.World journal of gastroenterology · 2021Review
- Current updates in machine learning in the prediction of therapeutic outcome of hepatocellular carcinoma: what should we know?Insights into imaging · 2021Review
- Feature Selection and Classification of Clinical Datasets Using Bioinspired Algorithms and Super Learner.Computational and mathematical methods in medicine · 2021Article
- Application of artificial intelligence in the diagnosis and treatment of hepatocellular carcinoma: A review.World journal of gastroenterology · 2020Review
- Development of machine learning-based clinical decision support system for hepatocellular carcinoma.Scientific reports · 2020Article
- Effective prediction of postoperative complications for patients after open hepatectomy: a simplified scoring system based on perioperative parameters.BMC surgery · 2019Article
- Comparative Study of Back Propagation Artificial Neural Networks and Logistic Regression Model in Predicting Poor Prognosis after Acute Ischemic Stroke.Open medicine (Warsaw, Poland) · 2019Article
- Real and predicted mortality under health spending constraints in Italy: a time trend analysis through artificial neural networks.BMC health services research · 2018Article
- Article
- Mortality prediction in patients with isolated moderate and severe traumatic brain injury using machine learning models.PloS one · 2018Article
- Macro- and micro-environmental factors in clinical hepatocellular cancer.Seminars in oncology · 2014Article
- A Proposed Multi-Criteria Optimization Approach to Enhance Clinical Outcomes Evaluation for Diabetes Care: A Commentary.Health services research and managerial epidemiologyArticle
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The aim of this present study is firstly to compare significant predictors of mortality for hepatocellular carcinoma (HCC) patients undergoing resection between artificial neural network (ANN) and logistic regression (LR) models and secondly to evaluate the predictive accuracy of ANN and LR in different survival year estimation models. We constructed a prognostic model for 434 patients with 21 potential input variables by Cox regression model. Model performance was measured by numbers of significant predictors and predictive accuracy. The results indicated that ANN had double to triple numbers of significant predictors at 1-, 3-, and 5-year survival models as compared with LR models. Scores of accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUROC) of 1-, 3-, and 5-year survival estimation models using ANN were superior to those of LR in all the training sets and most of the validation sets. The study demonstrated that ANN not only had a great number of predictors of mortality variables but also provided accurate prediction, as compared with conventional methods. It is suggested that physicians consider using data mining methods as supplemental tools for clinical decision-making and prognostic evaluation.
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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.