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

by   Swati Padhee, et al.

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.



There are no comments yet.


page 1

page 2

page 3

page 4


Machine learning techniques to identify antibiotic resistance in patients diagnosed with various skin and soft tissue infections

Skin and soft tissue infections (SSTIs) are among the most frequently ob...

Modelling pathogen spread in a healthcare network: indirect patient movements

A hybrid network–deterministic model for simulation of multiresistant pa...

An approach for auxiliary diagnosing and screening coronary disease based on machine learning

How to accurately classify and predict whether an individual has coronar...

DeepSOFA: A Real-Time Continuous Acuity Score Framework using Deep Learning

Traditional methods for assessing illness severity and predicting in-hos...

Challenges in the application of a mortality prediction model for COVID-19 patients on an Indian cohort

Many countries are now experiencing the third wave of the COVID-19 pande...

Using machine learning techniques to predict hospital admission at the emergency department

Introduction: One of the most important tasks in the Emergency Departmen...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.