Publication Date
Spring 2024
Degree Type
Master's Project
Degree Name
Master of Science in Computer Science (MSCS)
Department
Computer Science
First Advisor
William Andreopoulos
Second Advisor
Navrati Saxena
Third Advisor
Thomas Austin
Keywords
Personalized Medical Predictions, BEHRT model, Graph Attention Networks
Abstract
Over the past few years, personalized medicine has gained traction due to its ability to solve medical issues efficiently for a person based on their personal characteristics. This project aims to design a machine-learning model that can generate predictions of lab test scores in the future based on past medical history. The model is trained using the MIMIC-4 (Medical Information Mart for Intensive Care) dataset that consists of medical records of over 40,000 patients. The proposed model, MOE-BEHRT, consists of Bidirectional Encoder Representations from Transformers on Electronic Health Records (BEHRT) and Mixture of Experts (MOE). The BEHRT model was originally designed for multi-class classification. In this project, we’ve fine-tuned the BEHRT model to predict future lab test scores based on each patient’s medical history and patient similarity data. Cosine similarity metric and Graph Attention Networks (GATv2) are used to identify the similarities between patients based on their medical history. Based on the experimental results, the MOE-BEHRT model performs better than the Linear Regression by 6.85% on Blood Sugar tests, 1.684% on Cholestrol and 3.81755% on Alkaline Phosphate tests on Root Mean Square Error (RMSE). On comparing the RMSE results of MOE-BEHRT with a neural network, we observed that MOE-BEHRT performed better by 87.66% on Blood Sugar tests, 26.9867% on Cholesterol and 3.54855% on Alkaline Phosphate.
Recommended Citation
Chevva, Bhargavi, "Personalized Medical Predictions" (2024). Master's Projects. 1372.
https://scholarworks.sjsu.edu/etd_projects/1372
