ReviewFrontiers in oncology2023
On the importance of interpretable machine learning predictions to inform clinical decision making in oncology.
Review in Frontiers in oncology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 54 papers, 2 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
54 citing papers in PubMed, 2 syntheses or guidelines pooled it, 86 citations in OpenAlex.
- Machine Learning Used in Communicable Disease Control: A Scoping Review.Public health reviews · 2026Pooled it
- AI in Medical Questionnaires: Scoping ReviewJournal of medical Internet research · 2025Pooled it
- Predicting delayed graft function after kidney transplant: Do complex models help compared to standard statistics?World journal of nephrology · 2026Article
- Clinical Variable-Based Machine Learning for Predicting Early mCRPC Using Exclusively Clinical Variables: Development and Multicenter External Validation.The Prostate · 2026Article
- Integrating Deep Learning of Low-Dose CT Imaging With Clinical Data for Lung Cancer Risk Prediction.Chest · 2026Article
- Hypothesis Generation via Interpretable Machine Learning: A Case Study on Risk Factors for Postradiation Therapy Lung Cancer Recurrence.Advances in radiation oncology · 2026Article
- Unified comparison of machine learning paradigms for blood transfusion prediction in pediatric congenital heart surgery.iScience · 2026Article
- Evaluation of the reliability of markerless tumor tracking with single-energy and dual-energy imaging using machine learning.Journal of applied clinical medical physics · 2026Article
- Artificial Intelligence Tools in Precision Lung Cancer Care: From Early Detection to Clinical Decision Support.Cancers · 2026Review
- Comprehensive validation of machine learning models predicting chemotherapy related electrolyte disorders in a multicenter study.Communications medicine · 2026Article
- Explainable machine learning-based mortality prediction in critically ill patients with rheumatoid arthritis-associated lung disease: a radiomics and clinical data integration study.BMC medical imaging · 2026Article
- Predicting COVID-19 Mortality Risk Among Cardiovascular Disease Patients Using Artificial Intelligence Algorithms: A Retrospective Study on Clinical Data.Health science reports · 2026Article
- Integrating Blood-Based Immune-Inflammation Biomarkers into Artificial Intelligence-Driven Prognostic Models in Oncology.International journal of molecular sciences · 2026Review
- Differential Diagnosis of Parotid Tumors on Ultrasound: Interobserver Variability and Examiner-Specific Decision Rules-A Machine Learning Approach.Diagnostics (Basel, Switzerland) · 2026Article
- Early-Stage Breast Cancer in Women Younger Than 50 Years: Comparing American Joint Committee on Cancer Anatomic and Prognostic Stages With Partitioning Around Medoids Clusters in SEER Data.JCO clinical cancer informatics · 2026Article
- Predicting the magnitude of risk for non-curative endoscopic submucosal dissection in superficial esophageal cancer using explainable artificial intelligence.World journal of gastrointestinal oncology · 2026Article
- Predicting emergency department disposition using machine learning and large language models to support proactive capacity management: a multicenter retrospective study.BMC emergency medicine · 2026Article
- Interpretable Machine Learning Model for Survival Prediction in Pediatric Adrenocortical Tumors.Journal of the Endocrine Society · 2026Article
- The crucial role of machine learning models in predicting current childhood asthma: model comparison, calibration, and SHAP-based interpretation.Frontiers in public health · 2026Article
- AI-based neoadjuvant immunotherapy response prediction across pan-cancer: a comprehensive review.Cancer cell international · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Machine learning-based tools are capable of guiding individualized clinical management and decision-making by providing predictions of a patient's future health state. Through their ability to model complex nonlinear relationships, ML algorithms can often outperform traditional statistical prediction approaches, but the use of nonlinear functions can mean that ML techniques may also be less interpretable than traditional statistical methodologies. While there are benefits of intrinsic interpretability, many model-agnostic approaches now exist and can provide insight into the way in which ML systems make decisions. In this paper, we describe how different algorithms can be interpreted and introduce some techniques for interpreting complex nonlinear algorithms.
Indexed as
Identifiers
What Socratic holds
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 Socratic graph.