Knowledge discovery of patients reviews on breast cancer drugs: Segmentation of side effects using machine learning
Mehrbakhsh Nilashi1,2, Hossein Ahmadi3, Rabab Ali Abumalloh4
1UCSI Graduate Business School, UCSI University, 56000, Cheras, Kuala Lumpur, Malaysia.
Abstract:
Breast cancer stands as the most frequently diagnosed life-threatening cancer among women worldwide. Understanding patients' drug experiences is essential to improving treatment strategies and outcomes. In this research, we conduct knowledge discovery on breast cancer drugs using patients' reviews. A new machine learning approach is developed by employing clustering, text mining and regression techniques. We first use Latent Dirichlet Allocation (LDA) technique to discover the main aspects of patients' experiences from the patients' reviews on breast cancer drugs. We also use Expectation-Maximization (EM) algorithm to segment the data based on patients' overall satisfaction. We then use the Forward Entry Regression technique to find the relationship between aspects of patients' experiences and drug's effectiveness in each segment. The textual reviews analysis on breast cancer drugs found 8 main side effects: Musculoskeletal Effects, Menopausal Effects, Dermatological Effects, Metabolic Effects, Gastrointestinal Effects, Neurological and Cognitive Effects, Respiratory Effects and Cardiovascular. The results are provided and discussed. The findings of this study are expected to offer valuable insights and practical guidance for prospective patients, aiding them in making informed decisions regarding breast cancer drug consumption.
Insights
This study analyzed breast cancer drug reviews using machine learning to identify key patient experiences and side effects. Findings offer insights for patients choosing breast cancer medications.
Area of Science:
- Oncology
- Pharmacovigilance
- Data Science
Background:
- Breast cancer is a leading global health concern for women.
- Understanding patient drug experiences is crucial for treatment optimization.
- Patient reviews offer a valuable, yet underutilized, data source.
Purpose of the Study:
- To apply machine learning for knowledge discovery in breast cancer drug reviews.
- To identify key aspects of patient experiences and their relationship with drug effectiveness.
- To categorize common side effects reported by breast cancer patients.
Main Methods:
- Latent Dirichlet Allocation (LDA) for topic modeling of patient reviews.
- Expectation-Maximization (EM) algorithm for data segmentation based on satisfaction.
- Forward Entry Regression to correlate patient experiences with drug effectiveness.
Main Results:
- Identified 8 primary categories of breast cancer drug side effects: Musculoskeletal, Menopausal, Dermatological, Metabolic, Gastrointestinal, Neurological/Cognitive, Respiratory, and Cardiovascular.
- Established relationships between specific patient-reported experiences and perceived drug effectiveness within satisfaction segments.
- Revealed patterns in patient feedback regarding breast cancer treatments.
Conclusions:
- Machine learning effectively extracts valuable insights from patient reviews on breast cancer drugs.
- The identified side effect categories provide a framework for understanding patient-reported adverse events.
- Findings can empower patients to make more informed decisions about their breast cancer treatment options.
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