Large Language Models in Ambulatory Devices for Home Health Diagnostics: A case study of Sickle Cell Anemia Management

2023/05/05 by Oluwatosin Ogundare, Ogundare, Oluwatosin, Subuola Sofolahan +1
Computer Science · Medicine · #Computation and Language (cs.CL) #FOS: Computer and information sciences #Hemoglobinopathies and Related Disorders #Topic Modeling

paper · pdf · doi:10.48550/arxiv.2305.03715

openalex publication_date 2023/05/05 · openalex created_date 2023/05/10 · openalex updated_date 2026/07/28

Abstract

This study investigates the potential of an ambulatory device that incorporates Large Language Models (LLMs) in cadence with other specialized ML models to assess anemia severity in sickle cell patients in real time. The device would rely on sensor data that measures angiogenic material levels to assess anemia severity, providing real-time information to patients and clinicians to reduce the frequency of vaso-occlusive crises because of the early detection of anemia severity, allowing for timely interventions and potentially reducing the likelihood of serious complications. The main challenges in developing such a device are the creation of a reliable non-invasive tool for angiogenic level assessment, a biophysics model and the practical consideration of an LLM communicating with emergency personnel on behalf of an incapacitated patient. A possible system is proposed, and the limitations of this approach are discussed.

Related

• ALPHA: AnomaLous Physiological Health Assessment Using Large Language Models
• A smartphone based multi input workflow for non-invasive estimation of haemoglobin levels using machine learning techniques
• EHR-Based Mobile and Web Platform for Chronic Disease Risk Prediction Using Large Language Multimodal Models
• AI-MASLD Metabolic Dysfunction and Information Steatosis of Large Language Models in Unstructured Clinical Narratives
• Performance of a large language model for identifying central line-associated bloodstream infections (CLABSI) using real clinical notes