ArticlePloS one2015
Visualizing Risk Prediction Models.
Article in PloS one, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 1 of them a synthesis that pooled it.
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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.
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Who cites it
18 citing papers in PubMed, 1 synthesis or guideline pooled it, 45 citations in OpenAlex.
- Supporting clinical reasoning through visual summarization and presentation of patient data: a systematic review.Journal of the American Medical Informatics Association : JAMIA · 2025Pooled it
- User-Centered Design and Implementation of an Interoperable FHIR Application for Pediatric Pneumonia Prognostication in a Randomized Trial.Applied clinical informatics · 2024Trial
- Interpretable Machine Learning-Derived Nomogram Model to Predict Population Health Literacy: Cross-Sectional Development and Validation Study.JMIR medical informatics · 2026Article
- [A nomogram combining ultrasound radiomics and clinical features for predicting pathological invasiveness of papillary thyroid carcinoma].Zhejiang da xue xue bao. Yi xue ban = Journal of Zhejiang University. Medical sciences · 2026Article
- A nomogram-based clinical prediction model for adverse clinical outcomes in non-HIV Pneumocystis jirovecii pneumonia patients.BMC pulmonary medicine · 2025Article
- A prognostic nomogram based on least absolute shrinkage and selection operator Cox regression in patients with pulmonary large-cell neuroendocrine carcinoma.Translational cancer research · 2024Article
- Utilizing big data from electronic health records in pediatric clinical care.Pediatric research · 2023Review
- A novel LASSO-derived prognostic model predicting survival for non-small cell lung cancer patients with M1a diseases.Cancer medicine · 2022Article
- Construction of Xinjiang metabolic syndrome risk prediction model based on interpretable models.BMC public health · 2022Article
- Novel nomogram-based integrated gonadotropin therapy individualization in in vitro fertilization/intracytoplasmic sperm injection: A modeling approach.Clinical and experimental reproductive medicine · 2021Article
- Development and validation of a lifestyle-based model for colorectal cancer risk prediction: the LiFeCRC score.BMC medicine · 2021Article
- A qualitative research framework for the design of user-centered displays of explanations for machine learning model predictions in healthcare.BMC medical informatics and decision making · 2020Article
- Nomogram construction for predicting survival of patients with non-small cell lung cancer with malignant pleural or pericardial effusion based on SEER analysis of 10,268 patients.Oncology letters · 2020Article
- Prediction and clinical utility of a contralateral breast cancer risk model.Breast cancer research : BCR · 2019Article
- Methodological standards for the development and evaluation of clinical prediction rules: a review of the literature.Diagnostic and prognostic research · 2019Review
- Visualising statistical models using dynamic nomograms.PloS one · 2019Article
- Functional form estimation using oblique projection matrices for LS-SVM regression models.PloS one · 2019Article
- Explaining Support Vector Machines: A Color Based Nomogram.PloS one · 2016Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveRisk prediction models can assist clinicians in making decisions. To boost the uptake of these models in clinical practice, it is important that end-users understand how the model works and can efficiently communicate its results. We introduce novel methods for interpretable model visualization.
methodsThe proposed visualization techniques are applied to two prediction models from the Framingham Heart Study for the prediction of intermittent claudication and stroke after atrial fibrillation. We represent models using color bars, and visualize the risk estimation process for a specific patient using patient-specific contribution charts.
resultsThe color-based model representations provide users with an attractive tool to instantly gauge the relative importance of the predictors. The patient-specific representations allow users to understand the relative contribution of each predictor to the patient's estimated risk, potentially providing insightful information on which to base further patient management. Extensions towards non-linear models and interactions are illustrated on an artificial dataset.
conclusionThe proposed methods summarize risk prediction models and risk predictions for specific patients in an alternative way. These representations may facilitate communication between clinicians and patients.
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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.