  Acuity assessments are vital in critical care settings to provide timely
interventions and fair resource allocation. Traditional acuity scores rely on
manual assessments and documentation of physiological states, which can be
time-consuming, intermittent, and difficult to use for healthcare providers.
Furthermore, such scores do not incorporate granular information such as
patients' mobility level, which can indicate recovery or deterioration in the
ICU. We hypothesized that existing acuity scores could be potentially improved
by employing Artificial Intelligence (AI) techniques in conjunction with
Electronic Health Records (EHR) and wearable sensor data. In this study, we
evaluated the impact of integrating mobility data collected from wrist-worn
accelerometers with clinical data obtained from EHR for developing an AI-driven
acuity assessment score. Accelerometry data were collected from 86 patients
wearing accelerometers on their wrists in an academic hospital setting. The
data was analyzed using five deep neural network models: VGG, ResNet,
MobileNet, SqueezeNet, and a custom Transformer network. These models
outperformed a rule-based clinical score (SOFA= Sequential Organ Failure
Assessment) used as a baseline, particularly regarding the precision,
sensitivity, and F1 score. The results showed that while a model relying solely
on accelerometer data achieved limited performance (AUC 0.50, Precision 0.61,
and F1-score 0.68), including demographic information with the accelerometer
data led to a notable enhancement in performance (AUC 0.69, Precision 0.75, and
F1-score 0.67). This work shows that the combination of mobility and patient
information can successfully differentiate between stable and unstable states
in critically ill patients.
