JMIR Publications

ABSTRACT

Background:

Chemotherapy-Induced Nausea and Vomiting (CINV) are the two most dreadful and unpleasant side-effects of chemotherapy. CINV is accountable for poor treatment outcomes, treatment failure, or even death. It can affect patients' overall quality of life, leading to many social, economic, and clinical consequences.

Objective:

To compare the performances of different data mining models for predicting the risk of CINV among the patients and develop a smartphone application for clinical decision support to recommend the risk of CINV at the point of care.

Methods:

Data were collected by retrospective record review from the electronic medical records (EMR) used at the University of Missouri Ellis Fischel Cancer Center. The patients, who received chemotherapy and standard antiemetic at the oncology outpatient service from June 1, 2010, to July 31, 2012, were included in the study. There were six independent datasets of patients based on emetogenicity (low, medium, and high) and two phases of CINV (acute and delayed). A total of 14 risk-factors of CINV were chosen for data mining. For our study, we used five popular data mining algorithms: (a) Naïve Bayes, (b) Logistic Regression classifier, (c) Neural Network, (d) Support Vector Machine (using sequential minimal optimization), and (e) Decision Tree. Performance measures, such as accuracy, sensitivity, and specificity with 10-fold cross-validation, were used for model comparisons. A smartphone app called CINV Risk Prediction Application was developed using the ResearchKit in iOS utilizing the Decision Tree algorithm, which conforms to the criteria of explainable, usable, and actionable Artificial Intelligence (AI). The app was created using both the bulk-questionnaire approach and the adaptive approach.

Results:

Decision Tree performed well in both phases of high emetogenic chemotherapies with a significant margin than the other algorithms. The accuracy measure for the six patient groups ranges from 79.3%-94.8%. The app was developed using the results from the Decision Tree because of its consistent performance and simple, explainable nature. The bulk-questionnaire approach asks 14 questions in the smartphone app, while adaptive can determine questions based on the previous questions' answers. The adaptive approach saves time and can be beneficial when used at the point-of-care.

Conclusions:

This study solved a real clinical problem that can be used for personalized and precise evidence-based CINV management leading to a better life quality for patients and reduced healthcare costs.