ArticleComplex engineering systems (Alhambra, Calif.)2022
Interpretable AI for bio-medical applications.
Article in Complex engineering systems (Alhambra, Calif.), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT07731191 (Development of Digital Solutions for the Prediction of the Biological Mechanisms of Alzheimer's Disease Through the Analysis of Risk Factors), which is not on this map. Cited by 27 papers, 2 of them syntheses 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.
Development of Digital Solutions for the Prediction of the Biological Mechanisms of Alzheimer's Disease Through the Analysis of Risk Factors (PrevAI)
Who cites it
27 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Artificial intelligence for risk-stratified breast cancer screening: a systematic review of evidence, clinical integration, and ethical implications in risk assessment tools.La Radiologia medica · 2026Pooled it
- Application of artificial intelligence in the diagnosis of malignant digestive tract tumors: focusing on opportunities and challenges in endoscopy and pathology.Journal of translational medicine · 2025Pooled it
- Global Genomic Surveillance.Methods in molecular biology (Clifton, N.J.) · 2027Article
- Stage-specific predictors of exercise adherence after percutaneous coronary intervention: a prospective multicenter study.BMC cardiovascular disorders · 2026Observational
- Cross-Model Explainability Consistency in Hepatitis C Stage Classification: A SHAP, LIME, and Counterfactual Analysis Across Five Machine Learning Architectures.Diagnostics (Basel, Switzerland) · 2026Article
- Predicting mortality risk in hospitalized ACS patients with hypertensive comorbidity: an interpretable machine learning approach.BMC medical informatics and decision making · 2026Article
- Prediction models for adherence to cardiac rehabilitation programs in patients with cardiovascular disease: a scoping review.BMC medical informatics and decision making · 2026Article
- Artificial intelligence in haematology laboratory diagnosis: Current applications, challenges, and future directions.African journal of laboratory medicine · 2026Review
- Network toxicology & molecular docking: endocrine-disrupting chemicals induce prostate cancer - a system study.International journal of clinical and experimental pathology · 2026Article
- AI-driven virtual cell models in preclinical research: technical pathways, validation mechanisms, and clinical translation potential.NPJ digital medicine · 2025Review
- Beyond Binary: A Machine Learning Framework for Interpreting Organismal Behavior in Cancer Diagnostics.Biomedicines · 2025Review
- Integrating Spatial Omics and Deep Learning: Toward Predictive Models of Cardiomyocyte Differentiation Efficiency.Bioengineering (Basel, Switzerland) · 2025Review
- Association Between the Glucose-to-Lymphocyte Ratio and 28-Day Mortality in Patients with Mechanical Ventilation.Balkan medical journal · 2025Article
- Breast Lesion Detection Using Weakly Dependent Customized Features and Machine Learning Models with Explainable Artificial Intelligence.Journal of imaging · 2025Article
- The clinical implications and interpretability of computational medical imaging (radiomics) in brain tumors.Insights into imaging · 2025Review
- Naïve Bayes is an interpretable and predictive machine learning algorithm in predicting osteoporotic hip fracture in-hospital mortality compared to other machine learning algorithms.PLOS digital health · 2025Article
- Augmenting radiological assessment of imaging evident dementias with radiomic analysis.NPJ dementia · 2025Article
- Multianalyte nano-biosensor diagnostics: advances through microfluidic and AI integration.Frontiers in bioengineering and biotechnology · 2025Review
- Genome-scale quantification and prediction of pathogenic stop codon readthrough by small molecules.Nature genetics · 2024Article
- Leveraging artificial intelligence to identify high-risk patients for postoperative sore throat: An observational study.Biomolecules & biomedicine · 2024Observational
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
This paper presents the use of two popular explainability tools called Local Interpretable Model-Agnostic Explanations (LIME) and Shapley Additive exPlanations (SHAP) to explain the predictions made by a trained deep neural network. The deep neural network used in this work is trained on the UCI Breast Cancer Wisconsin dataset. The neural network is used to classify the masses found in patients as benign or malignant based on 30 features that describe the mass. LIME and SHAP are then used to explain the individual predictions made by the trained neural network model. The explanations provide further insights into the relationship between the input features and the predictions. SHAP methodology additionally provides a more holistic view of the effect of the inputs on the output predictions. The results also present the commonalities between the insights gained using LIME and SHAP. Although this paper focuses on the use of deep neural networks trained on UCI Breast Cancer Wisconsin dataset, the methodology can be applied to other neural networks and architectures trained on other applications. The deep neural network trained in this work provides a high level of accuracy. Analyzing the model using LIME and SHAP adds the much desired benefit of providing explanations for the recommendations made by the trained model.
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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.