Improving Pain Management in Patients with Sickle Cell Disease from Physiological Measures Using Machine Learning Techniques.

Journal: Smart health (Amsterdam, Netherlands)
Published Date: Feb 2, 2018

Abstract

Pain management is a crucial part in Sickle Cell Disease treatment. Accurate pain assessment is the first stage in pain management. However, pain is a subjective response and hard to assess via objective approaches. In this paper, we proposed a system to map objective physiological measures to subjective self-reported pain scores using machine learning techniques. Using Multinomial Logistic Regression and data from 40 patients, we were able to predict patients' pain scores on an 11-point rating scale with an average accuracy of 0.578 at the intra-individual level, and an accuracy of 0.429 at the inter-individual level. With a condensed 4-point rating scale, the accuracy at the inter-individual level was further improved to 0.681. Overall, we presented a preliminary machine learning model that can predict pain scores in SCD patients with promising results. To our knowledge, such a system has not been proposed earlier within the SCD or pain domains by exploiting machine learning concepts within the clinical framework.

Authors

  • Fan Yang
    School of Electrical Engineering and Automation, Jiangsu Normal University, Xuzhou, China.
  • Tanvi Banerjee
    Department of Computer Science and Engineering, Wright State University, Dayton, OH, USA.
  • Kalindi Narine
    Department of Pediatrics, Division of Hematology and Oncology, Duke University Hospital, NC 27710, USA.
  • Nirmish Shah
    Division of Hematology, Department of Medicine, Duke University, NC 27710, USA.

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