Preemptive Forecasting of Symptom Escalation in Cancer Patients Undergoing Chemotherapy

Presentation Time: 02:15 PM - 02:30 PM

Abstract Keywords: Nursing Informatics, Machine Learning, Chronic Care Management, Bioinformatics
Primary Track: Applications
Programmatic Theme: Clinical Informatics

This study evaluates the utility of machine learning (ML) algorithms in early forecasting of total symptom score changes from daily self-reports of 339 chemotherapy patients. The dataset comprised 12 specific symptoms, with severity and distress for each symptom rated on a 1 to 10 scale, generating a "total symptom score" ranging from 0 to 230. To address the challenge of an unbalanced original dataset, where Class I (score change ≥ 5) and Class II (score change < 5) were unevenly represented, we created a balanced dataset specifically for model training.
Using the MATLAB® Classification Learner application, we investigated nine ML models with various classifiers. The objective was to predict the total symptom score change based on the preceding 3 to 5 days' symptom data. Models were trained on the balanced dataset to mitigate the original imbalance's impact, with comparative evaluations also conducted on the unbalanced data to assess performance differences.
The analysis revealed that certain classifiers, delivered optimal performance on the unbalanced dataset, with an accuracy rate peaking at 82%. Yet, these models tended to frequently misclassify Class I as Class II. In contrast, the Ensemble algorithm equipped with the RUSBoost classifier demonstrated exceptional skill in accurately classifying both classes on both datasets, achieving accuracies of 59%, 59.3%, and 59.4% for data from 3, 4, and 5 days prior, respectively. Notably, these figures slightly improved to 61.16%, 58.41%, and 60.05% upon utilizing the balanced dataset for training.

Speaker(s):
AREF SMILEY, Assistant Professor/PhD
The University of Utah

Author(s):
Joseph Finkelstein, MD, PhD - University of Utah; Aref Smiley, Assistant Professor/PhD - The University of Utah; Christina Echeverria, MA - College of Nursing, The University of Utah; Kathi Mooney, PhD, RN, FAAN - College of Nursing, The University of Utah;