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 346 papers, 6 of them syntheses that pooled it.
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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.
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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.
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Who cites it
346 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
- 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
- Predicting positive youth development among Chinese adolescents: A machine learning approach using multiwave longitudinal data.Applied psychology. Health and well-being · 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
- 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
- Screening Signals of Reference-Defined Metabolic Syndrome Using HbA1c and LDL Cholesterol: An Explainable Machine Learning Study.Diagnostics (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
- Multiscale Explainable Machine Learning Reveals Descriptor-Invariant Molecular Determinants of Small-Molecule PD-1/PD-L1 Inhibition.ACS medicinal chemistry letters · 2026Article
- Integrative multi-omics analyses suggest a candidate microbial metabolite-associated host gene network in ulcerative colitis.Immunologic research · 2026Article
- Developing a Clinical Prediction Model for Nonimprovement of Depressive Symptoms at Discharge After Treatment of Eating Disorders.The International journal of eating disorders · 2026Article
- Development and Validation of an Interpretable Machine Learning Model for Predicting ICU-Acquired Weakness in Postoperative Patients.Nursing in critical care · 2026Article
- Evaluation of explainable machine learning models for predicting mid-term stone recurrence after percutaneous nephrolithotomy: a retrospective observational cohort study.International urology and nephrology · 2026Observational
286 more citing papers are in PubMed but not listed here.
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Authors and funding
5 authors.
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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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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.