ArticlePloS one2012
Comparison of artificial neural network and logistic regression models for predicting in-hospital mortality after primary liver cancer surgery.
Article in PloS one, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 42 papers, 1 of them a synthesis that pooled it.
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Who cites it
42 citing papers in PubMed, 1 synthesis or guideline pooled it, 113 citations in OpenAlex.
- Prognostic role of artificial intelligence among patients with hepatocellular cancer: A systematic review.World journal of gastroenterology · 2020Pooled it
- Risk stratification for postoperative complications after CRS and HIPEC in recurrent ovarian cancer patients: a comparative analysis of logistic regression and machine learning models.Scientific reports · 2025Article
- 90-day mortality prediction in elective visceral surgery using machine learning: a retrospective multicenter development, validation, and comparison study.International journal of surgery (London, England) · 2025Article
- Factor Analysis of the Effect of Hepatitis B-Related Liver Cancer Treatment Efficacy.Journal of hepatocellular carcinoma · 2025Article
- Development of a System for Predicting Hospitalization Time for Patients With Traumatic Brain Injury Based on Machine Learning Algorithms: User-Centered Design Case Study.JMIR human factors · 2024Article
- Digital Epidemiology of Prescription Drug References on X (Formerly Twitter): Neural Network Topic Modeling and Sentiment Analysis.Journal of medical Internet research · 2024Article
- Prediction of Long-Term Poor Clinical Outcomes in Cerebral Venous Thrombosis Using Neural Networks Model: The BEAST Study.International journal of general medicine · 2024Article
- Machine learning models in clinical practice for the prediction of postoperative complications after major abdominal surgery.Surgery today · 2023Article
- Development and verification of a combined diagnostic model for primary Sjögren's syndrome by integrated bioinformatics analysis and machine learning.Scientific reports · 2023Article
- Hematocrit and lactate trends help predict outcomes in trauma independent of CT and other clinical parameters.Frontiers in radiology · 2023Article
- Prediction of gastrointestinal cancers in the ONCONUT cohort study: comparison between logistic regression and artificial neural network.Frontiers in oncology · 2023Article
- The Role of Radiomics and AI Technologies in the Segmentation, Detection, and Management of Hepatocellular Carcinoma.Cancers · 2022Review
- Machine learning insights concerning inflammatory and liver-related risk comorbidities in non-communicable and viral diseases.World journal of gastroenterology · 2022Review
- Electrophysiological and kinesiological analysis of deep tendon reflex responses, importance of angular velocity.Medical & biological engineering & computing · 2022Article
- Machine Learning Algorithms for understanding the determinants of under-five Mortality.BioData mining · 2022Article
- Role of three-dimensional printing and artificial intelligence in the management of hepatocellular carcinoma: Challenges and opportunities.World journal of gastrointestinal oncology · 2022Review
- Development of machine learning models for mortality risk prediction after cardiac surgery.Cardiovascular diagnosis and therapy · 2022Article
- Development of a random forest model for hypotension prediction after anesthesia induction for cardiac surgery.World journal of clinical cases · 2021Article
- Challenges and opportunities in the application of artificial intelligence in gastroenterology and hepatology.World journal of gastroenterology · 2021Review
- An Artificial Neural Network-Based Pediatric Mortality Risk Score: Development and Performance Evaluation Using Data From a Large North American Registry.JMIR medical informatics · 2021Article
Corrections and comments
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Authors and funding
7 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundSince most published articles comparing the performance of artificial neural network (ANN) models and logistic regression (LR) models for predicting hepatocellular carcinoma (HCC) outcomes used only a single dataset, the essential issue of internal validity (reproducibility) of the models has not been addressed. The study purposes to validate the use of ANN model for predicting in-hospital mortality in HCC surgery patients in Taiwan and to compare the predictive accuracy of ANN with that of LR model. METHODOLOGY/PRINCIPAL
findingsPatients who underwent a HCC surgery during the period from 1998 to 2009 were included in the study. This study retrospectively compared 1,000 pairs of LR and ANN models based on initial clinical data for 22,926 HCC surgery patients. For each pair of ANN and LR models, the area under the receiver operating characteristic (AUROC) curves, Hosmer-Lemeshow (H-L) statistics and accuracy rate were calculated and compared using paired T-tests. A global sensitivity analysis was also performed to assess the relative significance of input parameters in the system model and the relative importance of variables. Compared to the LR models, the ANN models had a better accuracy rate in 97.28% of cases, a better H-L statistic in 41.18% of cases, and a better AUROC curve in 84.67% of cases. Surgeon volume was the most influential (sensitive) parameter affecting in-hospital mortality followed by age and lengths of stay. CONCLUSIONS/SIGNIFICANCE: In comparison with the conventional LR model, the ANN model in the study was more accurate in predicting in-hospital mortality and had higher overall performance indices. Further studies of this model may consider the effect of a more detailed database that includes complications and clinical examination findings as well as more detailed outcome data.
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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.