Modeling and prediction of pressure injury in hospitalized patients using artificial intelligence.

ABSTRACT:

Background

Hospital-acquired pressure injuries (PIs) induce significant patient suffering, inflate healthcare costs, and increase clinical co-morbidities. PIs are mostly due to bed-immobility, sensory impairment, bed positioning, and length of hospital stay. In this study, we use electronic health records and administrative data to examine the contributing factors to PI development using artificial intelligence (AI).

Methods

We used advanced data science techniques to first preprocess the data and then train machine learning classifiers to predict the probability of developing PIs. The AI training was based on large, incongruent, incomplete, heterogeneous, and time-varying data of hospitalized patients. Both model-based statistical methods and model-free AI strategies were u

SUBMITTER: Anderson C 

PROVIDER: S-EPMC8406893 | biostudies-literature | 2021 Aug

REPOSITORIES: biostudies-literature