Autonomous LLMs for Healthcare Diagnoses

Title:Autonomous LLMs for Healthcare Diagnoses

Authors:Hafiz Farooq Ahmad and Sarah Shafqat

Conference:isads2025

Tags:auto ml models, autonomous large language models llms, autonomous llm models tools and techniques, big data analytics bda, big data healthcare analytics, big data healthcare diagnostics, biomedical natural language processing, Cloud Analytics, Comorbidity Diseases, deep learning, Diagnoses, Distributed Healthcare Knowledgebase, Endocrine, entity recognition and normalization tools, high diagnostic accuracy, llms for healthcare, manifold and hierarchical clustering, manual annotation, ml and nlp techniques, named entity recognition and normalization, natural language processing, pc for practitioner comments, pretrained language models, SmartHealth, text mining, unified endocrine corpora, unified hierarchical annotation and unified medical corpora

Abstract:

Researchers present a significant contribution to the field of healthcare diagnostics by integrating Named Entity Recognition (NER) sequence embedding with real-time unified medical corpora named as, ‘DM_Comorbid_EHR_ICD10’, for endocrine patients suffering multiple diseases with primary disease as diabetes mellitus (DM). Furthering the proposed approach by leveraging Deep Learning (DL) with Autonomous Large Language Models (LLMs) for big data analytics (BDA) in healthcare demonstrates high potential in improving diagnostic accuracy for DM and its comorbidities.