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Pain in sickle cell disease (SCD) is often associated with increasedmorbidity, mortality, and high healthcare costs. The standard method forpredicting the absence, presence, and intensity of pain has long beenself-report. However, medical providers struggle to manage patients based onsubjective pain reports correctly and pain medications often lead to furtherdifficulties in patient communication as they may cause sedation andsleepiness. Recent studies have shown that objective physiological measures canpredict subjective self-reported pain scores for inpatient visits using machinelearning (ML) techniques. In this study, we evaluate the generalizability of MLtechniques to data collected from 50 patients over an extended period acrossthree types of hospital visits (i.e., inpatient, outpatient and outpatientevaluation). We compare five classification algorithms for various painintensity levels at both intra-individual (within each patient) andinter-individual (between patients) level. While all the tested classifiersperform much better than chance, a Decision Tree (DT) model performs best atpredicting pain on an 11-point severity scale (from 0-10) with an accuracy of0.728 at an inter-individual level and 0.653 at an intra-individual level. Theaccuracy 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. Ourexperimental results demonstrate that ML techniques can provide an objectiveand quantitative evaluation of pain intensity levels for all three types ofhospital visits.

Pain Intensity Assessment in Sickle Cell Disease patients using Vital Signs during Hospital Visits