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Pain Intensity Assessment in Sickle Cell Disease Patients Using Vital Signs During Hospital Visits.
Swati Padhee1, Amanuel Alambo1, Tanvi Banerjee1
1Wright State University, Dayton, USA.
Pattern Recognition : ICPR International Workshops and Challenges, Virtual Event, January 10-15, 2021, Proceedings. Part I. International Conference on Pattern Recognition (25Th : 2021 : Online)
|August 2, 2021
Summary
Machine learning models can objectively predict sickle cell disease (SCD) pain intensity using physiological data. This approach offers a more reliable assessment than subjective self-reports, improving patient care.
Area of Science:
- Biomedical Engineering
- Data Science
- Pain Management
Background:
- Sickle cell disease (SCD) pain significantly impacts patient morbidity, mortality, and healthcare costs.
- Subjective self-report is the standard for pain assessment but presents challenges for clinicians and can be affected by pain medications.
- Objective physiological measures show promise for predicting pain intensity, particularly with machine learning (ML) techniques.
Purpose of the Study:
- To evaluate the generalizability of ML techniques for predicting SCD pain intensity across different hospital visit types (inpatient, outpatient).
- To compare the performance of five classification algorithms in predicting pain levels both within (intra-individual) and between (inter-individual) patients.
- To determine the effectiveness of ML in providing an objective and quantitative pain evaluation for SCD patients.
Main Methods:
- Collected physiological data from 50 SCD patients over an extended period across three hospital visit types.
- Applied and compared five ML classification algorithms to predict pain intensity on an 11-point scale and a 2-point scale (no/mild vs. severe pain).
- Evaluated model performance at both intra-individual and inter-individual levels.
Main Results:
- All tested ML classifiers significantly outperformed chance in predicting pain intensity.
- A Decision Tree (DT) model achieved the highest accuracy: 0.728 (inter-individual) and 0.653 (intra-individual) on an 11-point scale.
- DT accuracy dramatically improved to 0.941 (inter-individual) on a simplified 2-point pain scale.
Conclusions:
- ML techniques demonstrate significant potential for objective and quantitative pain intensity evaluation in SCD patients.
- The generalizability of ML models across various hospital visit types supports their clinical utility.
- Objective pain assessment using ML can overcome limitations of subjective self-reporting, potentially improving pain management strategies.
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