ReviewClinical and translational science2024
Practical guide to SHAP analysis: Explaining supervised machine learning model predictions in drug development.
Review in Clinical and translational science, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 354 papers, 6 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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
354 citing papers in PubMed, 6 syntheses or guidelines pooled it.
- Risk prediction models for postoperative delirium in adult patients undergoing cardiac surgery: a systematic review and meta-analysis.BMC cardiovascular disorders · 2026Pooled it
- Machine Learning for the Analysis of Healthy Lifestyle Data: Scoping Review and Guidelines.JMIR human factors · 2026Guideline
- Unveiling the efficacy predictors and potential mechanisms ofFrontiers in immunology · 2026Pooled it
- Machine learning-based prediction models for severeFrontiers in public health · 2026Pooled it
- Risk prediction models for cognitive impairment in patients with chronic kidney disease: a systematic review.Frontiers in public health · 2026Pooled it
- Effects of traditional Chinese mind-body exercises on depressive symptoms in middle-aged and older adults: a multilevel meta-analysis with exploratory dose-response and machine learning analyses.Frontiers in psychology · 2026Pooled it
- Construction and validation of a machine learning model for predicting urine culture positivity in children with suspected urinary tract infections.Annals of medicine · 2026Article
- Temporal epidemiology and multi-source forecasting of hemorrhagic fever with renal syndrome and leptospirosis in mainland China: An interpretable machine learning study.Preventive medicine reports · 2026Article
- Understanding Cancer Information Burden and Quality Concerns: The Roles of Medical Statistics Literacy, Online Health Information Confidence, and Social Media.Journal of cancer education : the official journal of the American Association for Cancer Education · 2026Article
- Predicting PD-L1 + CD8 - status in non-small cell lung cancer tissue from clinical indicators using machine learning.Chinese medical journal · 2026Article
- Predicting positive youth development among Chinese adolescents: A machine learning approach using multiwave longitudinal data.Applied psychology. Health and well-being · 2026Article
- AI-driven discovery of natural product-derived FAK1 inhibitors for idiopathic pulmonary fibrosis.Molecular diversity · 2026Article
- An integrated machine learning and chemical space network approach for the design of potent epigenetic HDAC6 inhibitors for targeting neurological disorders.Molecular diversity · 2026Article
- Discrimination of normal from slow-aging mice by plasma metabolomic and proteomic features.GeroScience · 2026Article
- Comparative Predictive Performance of Machine Learning and Population Pharmacokinetic Models for Valproic Acid Clearance and Trough Concentrations: An In-silico Simulation Study in Epilepsy Scenarios.Pharmaceutical research · 2026Article
- Development and Validation of an Interpretable Machine Learning Model to Predict Mortality in Patients With Sepsis-Induced Coagulopathy: Multicenter Cohort Study.JMIR medical informatics · 2026Article
- Screening Signals of Reference-Defined Metabolic Syndrome Using HbA1c and LDL Cholesterol: An Explainable Machine Learning Study.Diagnostics (Basel, Switzerland) · 2026Article
- Demographic Shortcuts Account for Most of the Apparent Accuracy of Four-Class Gait-Based Differential Diagnosis in Neurodegenerative Disease: An Interpretability-Driven Audit of Machine Learning on a Public Gait Benchmark.Bioengineering (Basel, Switzerland) · 2026Article
- Predicting zero-dose vaccination status in 27 sub-Saharan African countries: a machine learning approach.BMJ health & care informatics · 2026Article
- Nonlinear Association of Length of Stay with In-Hospital Mortality in Alzheimer's Disease Hospitalizations: Admission-Only vs. Inpatient-Course Prediction Using Explainable Machine Learning.Geriatrics (Basel, Switzerland) · 2026Article
294 more citing papers are in PubMed but not listed here.
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.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Despite increasing interest in using Artificial Intelligence (AI) and Machine Learning (ML) models for drug development, effectively interpreting their predictions remains a challenge, which limits their impact on clinical decisions. We address this issue by providing a practical guide to SHapley Additive exPlanations (SHAP), a popular feature-based interpretability method, which can be seamlessly integrated into supervised ML models to gain a deeper understanding of their predictions, thereby enhancing their transparency and trustworthiness. This tutorial focuses on the application of SHAP analysis to standard ML black-box models for regression and classification problems. We provide an overview of various visualization plots and their interpretation, available software for implementing SHAP, and highlight best practices, as well as special considerations, when dealing with binary endpoints and time-series models. To enhance the reader's understanding for the method, we also apply it to inherently explainable regression models. Finally, we discuss the limitations and ongoing advancements aimed at tackling the current drawbacks of the method.
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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.