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Rapid identification of slow healing wounds
- Source :
- Wound Repair and Regeneration. 24:181-188
- Publication Year :
- 2016
- Publisher :
- Wiley, 2016.
-
Abstract
- Chronic nonhealing wounds have a prevalence of 2% in the United States, and cost an estimated $50 billion annually. Accurate stratification of wounds for risk of slow healing may help guide treatment and referral decisions. We have applied modern machine learning methods and feature engineering to develop a predictive model for delayed wound healing that uses information collected during routine care in outpatient wound care centers. Patient and wound data was collected at 68 outpatient wound care centers operated by Healogics Inc. in 26 states between 2009 and 2013. The dataset included basic demographic information on 59,953 patients, as well as both quantitative and categorical information on 180,696 wounds. Wounds were split into training and test sets by randomly assigning patients to training and test sets. Wounds were considered delayed with respect to healing time if they took more than 15 weeks to heal after presentation at a wound care center. Eleven percent of wounds in this dataset met this criterion. Prognostic models were developed on training data available in the first week of care to predict delayed healing wounds. A held out subset of the training set was used for model selection, and the final model was evaluated on the test set to evaluate discriminative power and calibration. The model achieved an area under the curve of 0.842 (95% confidence interval 0.834–0.847) for the delayed healing outcome and a Brier reliability score of 0.00018. Early, accurate prediction of delayed healing wounds can improve patient care by allowing clinicians to increase the aggressiveness of intervention in patients most at risk.
- Subjects :
- Adult
Male
medicine.medical_specialty
Time Factors
Adolescent
Referral
Dermatology
030204 cardiovascular system & hematology
Risk Assessment
Article
Machine Learning
Young Adult
030207 dermatology & venereal diseases
03 medical and health sciences
Wound care
0302 clinical medicine
Early Medical Intervention
medicine
Humans
In patient
Child
Healing wounds
Prognostic models
Aged
Retrospective Studies
Aged, 80 and over
Wound Healing
Training set
integumentary system
business.industry
Infant, Newborn
Disease Management
Infant
Reproducibility of Results
Middle Aged
Models, Theoretical
Prognosis
Confidence interval
Surgery
Rapid identification
Early Diagnosis
Logistic Models
Child, Preschool
Chronic Disease
Emergency medicine
Wounds and Injuries
Female
business
Subjects
Details
- ISSN :
- 10671927
- Volume :
- 24
- Database :
- OpenAIRE
- Journal :
- Wound Repair and Regeneration
- Accession number :
- edsair.doi.dedup.....ffa25efa45f90222951d7a8e174e3108