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Continuous Pain Assessment Using Ensemble Feature Selection from Wearable Sensor Data.

Fan Yang¹, Tanvi Banerjee¹, Mark J Panaggio²

¹Department of Computer Science and Engineering, Wright State University, Dayton, OH, USA.

Proceedings. IEEE International Conference on Bioinformatics and Biomedicine

|August 15, 2020

Summary

This summary is machine-generated.

Researchers developed an objective pain assessment model for sickle cell disease (SCD) using wearable sensors. This method accurately estimates pain levels from physiological and movement data, improving SCD pain management.

Keywords:

ensemble feature selection machine learning pain assessment stacked generalization

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Published on: January 22, 2018

Area of Science:

Biomedical Engineering
Digital Health
Data Science

Background:

Sickle cell disease (SCD) is a debilitating blood disorder characterized by chronic pain.
Current pain management in SCD is hindered by the subjective nature of pain assessment.
Objective methods for continuous pain monitoring in SCD are critically needed.

Purpose of the Study:

To develop an objective, continuous pain assessment model for SCD using wearable sensor data.
To enhance the accuracy and reliability of pain estimation through advanced feature selection and machine learning techniques.

Main Methods:

Collected physiological and body movement data using a wrist-worn wearable device.
Implemented ensemble feature selection methods to identify robust features from wearable data.
Utilized stacked generalization to integrate features selected by different ensemble methods for improved pain prediction.

Main Results:

Ensemble feature selection significantly increased the stability of selected features.
The best performing model achieved a root-mean-square error of 1.526 and a Pearson correlation of 0.618 for continuous pain assessment.
Demonstrated high precision in estimating subjective pain scores from objective wearable sensor data.

Conclusions:

Objective pain assessment in SCD is feasible using wearable sensor technology.
Ensemble feature selection and stacked generalization are effective strategies for building accurate pain prediction models.
This approach holds promise for improving pain management and patient outcomes in sickle cell disease.