preprints.org > computer science and mathematics > algebra and number theory > doi: 10.20944/preprints202012.0518.v1

Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Version 1 : Received: 19 December 2020 / Approved: 21 December 2020 / Online: 21 December 2020 (12:05:12 CET)

With a reduction in the mortality rate of burn patients, patient length of stay (LOS) is increasingly adopted as an outcome measure. Some studies have attempted to identify factors that explain a burn patient's expected LOS. However, few have investigated the association between LOS and a patient's mental and socioeconomic status. There is anecdotal evidence for links between these factors and uncovering these will aid in better addressing the specific physical and emotional needs of burn patients, and facilitate the planning of scarce hospital resources. Here, we employ machine learning (clustering) and statistical models (regression) to investigate whether a segmentation by socioeconomic/mental status can improve the performance and interpretability of an upstream predictive model, relative to a unitary model derived for the full adult population of patients. Although we found no significant difference in the performance of the unitary model and segment-specific models, the interpretation of the segment-specific models reveals a reduced impact of burn severity in LOS prediction with increasing adverse socioeconomic and mental status. Furthermore, the models for the socioeconomic segments highlight an increased influence of living circumstances and source of injury on LOS. These findings suggest that, in addition to ensuring that the physical needs of patients are met, management of their mental status is crucial for delivering an effective care plan.

Burn care; length of stay; mental state; socioeconomic status; clustering; predictive models; regression analysis; collaborative decision making

Computer Science and Mathematics, Algebra and Number Theory