ArticleHealth data science2023
simpleNomo: A Python Package of Making Nomograms for Visualizable Calculation of Logistic Regression Models.
Article in Health data science, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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
14 citing papers in PubMed.
- Exploring the relationship between psychological interventions and glycemic stability in youth with type 2 diabetes mellitus using machine learning methods.Annals of pediatric endocrinology & metabolism · 2026Article
- Development of a nomogram for prediction of postoperative bleeding after transurethral resection of bladder tumors.International urology and nephrology · 2026Article
- Development of Diagnostic Nomograms Using Corneal Nerve Parameters for Diabetic Peripheral Neuropathy in Type 2 Diabetes Mellitus.Translational vision science & technology · 2025Article
- Derivation and Validation of Pediatric Sepsis-Associated Acute Kidney Injury Subphenotypes With Prognostic Relevance.Pediatric critical care medicine : a journal of the Society of Critical Care Medicine and the World Federation of Pediatric Intensive and Critical Care Societies · 2025Observational
- Comparative analysis of machine learning-derived nomogram and biomarkers in predicting side-specific extraprostatic extension: Preliminary findings.Clinical imaging · 2025Article
- Risk factor analysis and predictive nomogram development for in-hospital mortality in patients with ST-segment elevation myocardial infarction.BMC medical informatics and decision making · 2025Article
- A Risk Prediction Model (CMC-AKIX) for Postoperative Acute Kidney Injury Using Machine Learning: Algorithm Development and Validation.Journal of medical Internet research · 2025Article
- Deep learning for detecting and early predicting chronic obstructive pulmonary disease from spirogram time series.NPJ systems biology and applications · 2025Article
- Exploring the role of oxidative stress in carotid atherosclerosis: insights from transcriptomic data and single-cell sequencing combined with machine learning.Biology direct · 2025Article
- Identifying the Most Critical Predictors of Workplace Violence Experienced by Junior Nurses: An Interpretable Machine Learning Perspective.Journal of nursing management · 2025Observational
- Radiomics-based multiple machine learning approaches for investigating medial wall invasion of the cavernous sinus in pituitary adenomas.Frontiers in oncology · 2025Article
- Cardiac murmur grading and risk analysis of cardiac diseases based on adaptable heterogeneous-modality multi-task learning.Health information science and systems · 2024Article
- Machine learning to predict the occurrence of thyroid nodules: towards a quantitative approach for judicious utilization of thyroid ultrasonography.Frontiers in endocrinology · 2024Article
- A machine learning driven monogram for predicting chronic kidney disease stages 3-5.Scientific reports · 2023Article
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.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Logistic regression models are widely used in clinical prediction, but their application in resource-poor settings or areas without internet access can be challenging. Nomograms can serve as a useful visualization tool to speed up the calculation procedure, but existing nomogram generators often require the input of raw data, inhibiting the transformation of established logistic regression models that only provide coefficients. Developing a tool that can generate nomograms directly from logistic regression coefficients would greatly increase usability and facilitate the translation of research findings into patient care. Methods: We designed and developed simpleNomo, an open-source Python toolbox that enables the construction of nomograms for logistic regression models. Uniquely, simpleNomo allows for the creation of nomograms using only the coefficients of the model. Further, we also devoloped an online website for nomogram generation. Results: simpleNomo properly maintains the predictive ability of the original logistic regression model and easy to follow. simpleNomo is compatible with Python 3 and can be installed through Python Package Index (PyPI) or https://github.com/Hhy096/nomogram. Conclusion: This paper presents simpleNomo, an open-source Python toolbox for generating nomograms for logistic regression models. It facilitates the process of transferring established logistic regression models to nomograms and can further convert more existing works into practical use.
Identifiers
What OpenQuestion 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 OpenQuestion graph.