B-111 Advancing Precision Medicine in Multiple Myeloma: Addressing Demographic Variabilities and Imbalanced Data in the NIH All of Us Research Program Cohort
Authors: Houze T A , Azuine R E , Mack N , McClain J , Lunt C , Denny J C 
Published: October 2024

Abstract

Background
The diagnosis of multiple myeloma (MM), a complex hematologic malignancy, presents unique challenges in prognostication and treatment planning, exacerbated by disparities across demographic groups and imbalanced datasets. The heterogeneity observed in MM's clinical manifestations and outcomes necessitates a nuanced approach to patient care, emphasizing the need for models that accurately predict long-term health outcomes across the diverse spectrum of MM participants. Given the variable response to treatment observed among different demographic cohorts, achieving precision in treatment strategies and ensuring equitable healthcare provision is paramount.

Objective
The objective of this study was to develop and assess predictive models tailored for various demographic groups of participants diagnosed with MM in the All of Us Research Program cohort, which is enrolling 1 million diverse participants who share multiple sources of data.

Methods
The Synthetic Minority Over-Sampling Technique (SMOTE) was used to rectify the prevalent imbalances within datasets, thereby significantly enhancing the predictive accuracy of patient outcomes over a 1-year post-diagnosis period. By addressing these imbalances, we provide a cornerstone for the development of more personalized and effective treatment paradigms. Employing a comprehensive dataset extracted from electronic health records (EHRs), we segmented participants based on key demographic variables such as age, gender, race, and socioeconomic status. This segmentation included detailed analyses of MM diagnosis, treatment regimens, and subsequent patient responses. To tackle the issue of dataset imbalance, two sets of predictive models were meticulously crafted for each demographic segment: one utilized the original dataset distributions and the other leveraged datasets balanced with SMOTE. A suite of performance metrics, including accuracy, precision, recall, Area Under the Curve (AUC), and F1 score, were deployed to evaluate and compare the efficacy of these models. Models trained on SMOTE-balanced datasets demonstrated marked improvements in prediction accuracy, from a baseline of 75% to over 90%. Precision rates climbed to 88%, and recall rates reached 90%, with an AUC of 0.95 and an F1 score of 0.89, highlighting the models' enhanced predictive capabilities.

Results
The application of SMOTE to balance datasets resulted in significant predictive accuracy improvements for minority groups within the MM patient population. This was especially evident in demographic cohorts historically underrepresented in clinical research, showcasing the potential of SMOTE in creating more equitable treatment outcomes.

Conclusions
Our findings underscore the potential of employing tailored predictive models in refining prognosis and treatment planning for multiple myeloma, particularly by addressing data imbalances. The integration of SMOTE into our methodology validates its efficacy in enhancing prediction accuracy, setting a new standard for oncological research.

Implications
Advocating for a more sophisticated and inclusive approach to data handling, our research contributes to a paradigm shift in oncological research, especially within hematological malignancies like MM. This study is poised to revolutionize predictive modeling in oncology, fostering more informed clinical decision-making and advancing the goals of precision medicine.