Advancing Precision Medicine: VAE Enhanced Predictions of Pancreatic Cancer Patient Survival in Local Hospital

Abstract

In this research, we address the urgent need for accurate prediction of in-hospital survival periods for patients diagnosed with pancreatic cancer (PC), a disease notorious for its late-stage diagnosis and dismal survival rates. Utilizing machine learning (ML) technologies, we focus on the application of Variational Autoencoders (VAE) for data augmentation and ensemble learning techniques for enhancing predictive accuracy. Our dataset comprises biochemical blood test (BBT) results from stage II/III PC patients, which is limited in size, making VAE's capability for data augmentation particularly valuable. The study employs several ML models, including Elastic Net (EN), Decision Trees (DT), and Radial Basis Function Support Vector Machine (RBF-SVM), and evaluates their performance using metrics such as Mean Absolute Error (MAE) and Mean Squared Error (MSE). Our findings reveal that EN, DT, and RBF-SVM are the most effective models within a VAE-augmented framework, showing substantial improvements in predictive accuracy. An ensemble learning approach further optimized the results, reducing the MAE to approximately 10 days. These advancements hold significant implications for the field of precision medicine, enabling more targeted therapeutic interventions and optimizing healthcare resource allocation. The study can also serve as a foundational step towards more personalized and effective healthcare solutions for PC patients.

Suboptimal capability of individual machine learning algorithms in modeling small-scale imbalanced clinical data of local hospital

“…For instance, deep learning techniques have been employed to predict protein structures with remarkable accuracy, revolutionizing the field of structural biology [7]. Similarly, machine learning models like random forest and support vector machines have been instrumental in identifying potential biomarkers for various diseases, thereby aiding in early diagnosis and personalized treatment [11,15]. The convergence of AI technologies with traditional biochemical and bioinformatical methods is thus heralding a new era in scientific exploration and discovery, promising to reshape the landscape of biomedical research in the coming years.…”

“…If basic AI models prove inadequate for the task, it would be prudent to exercise caution in relying solely on them. Such shortcomings warrant a deeper investigation into the root causes and may necessitate the development of innovative solutions, such as generative models [15,19,20], transfer learning [21,22], or the use of large language models [23].…”