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

TL;DR

This study evaluates machine learning models using vital signs to objectively assess pain intensity in sickle cell disease patients across various hospital visit types, aiming to improve pain management.

Contribution

It demonstrates the effectiveness of ML algorithms, especially Decision Trees, in predicting pain levels from physiological data with high accuracy, generalizing across different visit types.

Findings

Decision Tree achieved 72.8% accuracy at inter-individual level.

Accuracy improved to 94.1% on a 2-point pain scale intra-individually.

ML models outperform chance in predicting pain across hospital visit types.

Abstract

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.