ArticleSmart health (Amsterdam, Netherlands)2020
Machine learning models for synthesizing actionable care decisions on lower extremity wounds.
Article in Smart health (Amsterdam, Netherlands), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 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
10 citing papers in PubMed, 2 syntheses or guidelines pooled it, 19 citations in OpenAlex.
- Artificial Intelligence Methods for Diagnostic and Decision-Making Assistance in Chronic Wounds: A Systematic Review.Journal of medical systems · 2025Pooled it
- Risk predictions of surgical wound complications based on a machine learning algorithm: A systematic review.International wound journal · 2024Pooled it
- Integrating Wound Images and Clinical Text for Pressure Injury Assessment and Treatment Recommendation.Bioengineering (Basel, Switzerland) · 2026Article
- Clinical and Operational Outcomes Associated with the Adoption of a Digital Wound Care Solution in Home Health Settings.Healthcare (Basel, Switzerland) · 2026Article
- Machine Learning Applications for Venous Ulcer Assessment and Wound Care: A Review.Diagnostics (Basel, Switzerland) · 2026Review
- Predictive model and risk analysis for outcomes in diabetic foot ulcer using eXtreme Gradient Boosting algorithm and SHapley Additive exPlanation.World journal of diabetes · 2025Article
- Multimodal AI for Home Wound Patient Referral Decisions From Images With Specialist Annotations.IEEE journal of translational engineering in health and medicine · 2025Article
- Automated Prediction of Photographic Wound Assessment Tool in Chronic Wound Images.Journal of medical systems · 2024Article
- Clinical decision support systems for diabetic foot ulcers: a scoping review.Revista da Escola de Enfermagem da U S P · 2024Article
- Initial Clinical Experience with a Simple, Home System for Early Detection and Monitoring of Diabetic Foot Ulcers: The Foot Selfie.Journal of diabetes science and technology · 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
9 authors at 3 institutions in 1 country.
Funding
Abstract
Lower extremity chronic wounds affect 4.5 million Americans annually. Due to inadequate access to wound experts in underserved areas, many patients receive non-uniform, non-standard wound care, resulting in increased costs and lower quality of life. We explored machine learning classifiers to generate actionable wound care decisions about four chronic wound types (diabetic foot, pressure, venous, and arterial ulcers). These decisions (target classes) were: (1) Continue current treatment, (2) Request non-urgent change in treatment from a wound specialist, (3) Refer patient to a wound specialist. We compare classification methods (single classifiers, bagged & boosted ensembles, and a deep learning network) to investigate (1) whether visual wound features are sufficient for generating a decision and (2) whether adding unstructured text from wound experts increases classifier accuracy. Using 205 wound images, the Gradient Boosted Machine (XGBoost) outperformed other methods when using both visual and textual wound features, achieving 81% accuracy. Using only visual features decreased the accuracy to 76%, achieved by a Support Vector Machine classifier. We conclude that machine learning classifiers can generate accurate wound care decisions on lower extremity chronic wounds, an important step toward objective, standardized wound care. Higher decision-making accuracy was achieved by leveraging clinical comments from wound experts.
Indexed as
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.