Ruchir Srivastava, Ee Ping Ong, et al., Early Prediction of Wound Healing Outcome Based on Chronic Wound Registry Database, International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2023
Abstract:
Abstract—Chronic wounds cause a number of unnecessary
amputations due to a delay in proper treatment. To expedite
timely treatment, this paper presents an algorithm which uses
a logistic regression classifier to predict whether the wound
will heal or not within a specified time. The prediction is made
at three time-points: one month, three months, and six months
from the first visit of the patient to the healthcare facility. This
prediction is made using a systematically collected chronic
wound registry and is based entirely on data collected during
patients’ first visit. The algorithm achieves an area under the
receiver operating characteristic curve (AUC) of 0.75, 0.72,
and 0.71 for the prediction at the three time-points, respectively.
Clinical relevance—Using the proposed prediction model, the
clinicians will have an early estimate of the time taken to heal
thereby providing appropriate treatments. We hope this will
ensure timely treatments and reduce the number of unnecessary
amputations.
License type:
Publisher Copyright
Funding Info:
This research / project is supported by the Singapore National Research Foundation (NRF) - Innovation cluster programme
Grant Reference no. : NA
This research / project is supported by the Agency for Science, Technology and Research (A*STAR) - Industry Alignment Fund – Pre-Positioning Programme (IAF-PP)
Grant Reference no. : H19/01/a0/0Y9