  Pain in sickle cell disease (SCD) is often associated with increased
morbidity, mortality, and high healthcare costs. The standard method for
predicting the absence, presence, and intensity of pain has long been
self-report. However, medical providers struggle to manage patients based on
subjective pain reports correctly and pain medications often lead to further
difficulties in patient communication as they may cause sedation and
sleepiness. Recent studies have shown that objective physiological measures can
predict subjective self-reported pain scores for inpatient visits using machine
learning (ML) techniques. In this study, we evaluate the generalizability of ML
techniques to data collected from 50 patients over an extended period across
three types of hospital visits (i.e., inpatient, outpatient and outpatient
evaluation). We compare five classification algorithms for various pain
intensity levels at both intra-individual (within each patient) and
inter-individual (between patients) level. While all the tested classifiers
perform much better than chance, a Decision Tree (DT) model performs best at
predicting pain on an 11-point severity scale (from 0-10) with an accuracy of
0.728 at an inter-individual level and 0.653 at an intra-individual level. The
accuracy of DT significantly improves to 0.941 on a 2-point rating scale (i.e.,
no/mild pain: 0-5, severe pain: 6-10) at an intra-individual level. Our
experimental results demonstrate that ML techniques can provide an objective
and quantitative evaluation of pain intensity levels for all three types of
hospital visits.
