ArticleAnnals of translational medicine2018
Opening the black box of neural networks: methods for interpreting neural network models in clinical applications.
Article in Annals of translational medicine, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 101 papers, 2 of them syntheses that pooled it.
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Who cites it
101 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Pooled it
- Predictive modelling of brain disorders with magnetic resonance imaging: A systematic review of modelling practices, transparency, and interpretability in the use of convolutional neural networks.Human brain mapping · 2023Pooled it
- The prediction of cancer-specific mortality in T1 non-muscle-invasive bladder cancer: comparison of logistic regression and artificial neural network: a SEER population-based study.International urology and nephrology · 2023Trial
- Artificial intelligence model comparison for risk factor analysis of patent ductus arteriosus in nationwide very low birth weight infants cohort.Scientific reports · 2021Trial
- Deep learning for small-molecule drug discovery: From molecular design to clinical translation.Journal of pharmaceutical analysis · 2026Review
- Development of an Artificial Intelligence Web Application for Predicting Chemotherapy-Induced Neutropenia in Patients With Non-Small Cell Lung Cancer: A Prospective Study.Cancer medicine · 2026Article
- Orthodontists' Perceptions, Attitudes, and Adoption of Artificial Intelligence: A Scoping Review.Dentistry journal · 2026Review
- Performance of predictive AI-based clinical decision support systems across clinical domains: A systematic review and meta-analysis.PLOS digital health · 2026Article
- Robust and interpretable unit level causal inference in neural networks for pediatric myopia.NPJ digital medicine · 2026Article
- The influence of intercalated disk nanostructure on local ionic currents and cardiac conduction.Biophysical journal · 2026Article
- Establishment of Predictive Models for Second Hepatoma Recurrence.Cancer management and research · 2026Article
- Machine learning-based prediction of in-hospital mortality in patients with decompensated heart failure using the Charlson Comorbidity Index.SAGE open medicine · 2026Article
- Risk prediction of biliary infection after endoscopic drainage for malignant perihilar biliary obstruction: A 10-year multicenter retrospective study.World journal of gastroenterology · 2025Article
- Interpretable Artificial Neural Network Models for Predicting Anti-Adalimumab Immune Complex and Serum Drug Level in Crohn's Disease: A Proof-of-Concept Study.Pharmaceutics · 2025Article
- Regulation of Tetraspanin CD63 in Chronic Myeloid Leukemia (CML): Single-Cell Analysis of Asymmetric Hematopoietic Stem Cell Division Genes.Bioengineering (Basel, Switzerland) · 2025Article
- Optimal mean arterial pressure for favorable neurological outcomes in patients after cardiac arrest.Journal of intensive care · 2025Article
- Electromyography Signal Acquisition, Filtering, and Data Analysis for Exoskeleton Development.Sensors (Basel, Switzerland) · 2025Review
- Clinical Applications of Artificial Intelligence in Teleorthodontics: A Scoping Review.Medicina (Kaunas, Lithuania) · 2025Article
- Machine learning-based survival models for predicting rehospitalization of older hip fracture patients: a retrospective cohort study.BMC musculoskeletal disorders · 2025Article
- Leveraging graph neural networks and gate recurrent units for accurate and transparent prediction of baseball pitching speed.Scientific reports · 2025Article
41 more citing papers are in PubMed but not listed here.
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Artificial neural networks (ANNs) are powerful tools for data analysis and are particularly suitable for modeling relationships between variables for best prediction of an outcome. While these models can be used to answer many important research questions, their utility has been critically limited because the interpretation of the "black box" model is difficult. Clinical investigators usually employ ANN models to predict the clinical outcomes or to make a diagnosis; the model however is difficult to interpret for clinicians. To address this important shortcoming of neural network modeling methods, we describe several methods to help subject-matter audiences (e.g., clinicians, medical policy makers) understand neural network models. Garson's algorithm describes the relative magnitude of the importance of a descriptor (predictor) in its connection with outcome variables by dissecting the model weights. The Lek's profile method explores the relationship of the outcome variable and a predictor of interest, while holding other predictors at constant values (e.g., minimum, 20th quartile, maximum). While Lek's profile was developed specifically for neural networks, partial dependence plot is a more generic version that visualize the relationship between an outcome and one or two predictors. Finally, the local interpretable model-agnostic explanations (LIME) method can show the predictions of any classification or regression, by approximating it locally with an interpretable model. R code for the implementations of these methods is shown by using example data fitted with a standard, feed-forward neural network model. We offer codes and step-by-step description on how to use these tools to facilitate better understanding of ANN.
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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.