Abstract
Sickle Cell Disease (SCD) is a hereditary disorder of red blood cells in humans. Complications such as pain, stroke, and organ failure occur in SCD as malformed, sickled red blood cells passing through small blood vessels get trapped. Particularly, acute pain is known to be the primary symptom of SCD. The insidious and subjective nature of SCD pain leads to challenges in pain assessment among Medical Practitioners (MPs). Thus, accurate identification of markers of pain in patients with SCD is crucial for pain management. Classifying clinical notes of patients with SCD based on their pain level enables MPs to give appropriate treatment. We propose a binary classification model to predict pain relevance of clinical notes and a multiclass classification model to predict pain level. While our four binary machine learning (ML) classifiers are comparable in their performance, Decision Trees had the best performance for the multiclass classification task achieving 0.70 in F-measure. Our results show the potential clinical text analysis and machine learning offer to pain management in sickle cell patients.
Original language | American English |
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Title of host publication | 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC) |
Subtitle of host publication | Enabling Innovative Technologies for Global Healthcare, EMBC 2020 |
Publisher | IEEE |
Pages | 5838-5841 |
Number of pages | 4 |
ISBN (Electronic) | 978-1-7281-1990-8 |
ISBN (Print) | 978-1-7281-1991-5 |
DOIs | |
State | Published - Aug 27 2020 |
Event | 42nd Annual International Conferences of the IEEE Engineering in Medicine and Biology Society, EMBC 2020 - Montreal, Canada Duration: Jul 20 2020 → Jul 24 2020 |
Conference
Conference | 42nd Annual International Conferences of the IEEE Engineering in Medicine and Biology Society, EMBC 2020 |
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Country/Territory | Canada |
City | Montreal |
Period | 7/20/20 → 7/24/20 |
ASJC Scopus Subject Areas
- Signal Processing
- Biomedical Engineering
- Computer Vision and Pattern Recognition
- Health Informatics
Keywords
- Acute Pain
- Anemia
- Sickle Cell
- Erythrocyte Count
- Humans
- Paint Management
- Pain Measurement
- Sickle cell amenia--Pain
- Text Mining
- Computer Science--Machine Learning
Disciplines
- Computer Sciences
- Engineering