Utilizing Quantum Computing-based Large Language Transformer Models to Identify Social Determinants of Health from Electronic Health Records
Abstract
In the realm of modern healthcare, the fusion of quantum computing technology and data analytics has become indispensable for improving patient care and outcomes. Natural Language Processing (NLP) stands out as a pivotal tool, particularly in analyzing vast troves of unstructured data from Electronic Health Records (EHRs). The application of Transformer-based NLP models, such as BERT and GPT, in extracting Social Determinants of Health (SDOH) information from EHRs to offer insight into factors that extend beyond clinical indicators to influence patient health. Despite the significance of SDOH in healthcare, their extraction from EHRs remains underexplored. This study aims to demonstrate the efficacy of quantum computing-based NLP in identifying and categorizing SDOH information. Through systematic investigation, this study contributes to advancing healthcare interventions by providing insights into patient health beyond clinical indicators.
Read the paper
Related studies
- In silico toxicology protocols
- QM7-X, a comprehensive dataset of quantum-mechanical properties spanning the chemical space of small organic molecules
- Quantum Informed Machine-Learning Potentials for Molecular Dynamics Simulations of CO2's Chemisorption and Diffusion in Mg-MOF-74
- In-silico strategies in nano-drug design: Bridging nanomaterials and pharmacological applications
- mRNA codon optimization with quantum computers
- The computational power of the human brain
Go deeper in Evidence Hub
Evidence Hub adds a study data sheet (design, sample size, evidence level, key numbers), an AI summary and a chat grounded in the paper's text.