Combining large language models with traditional methods enhances accuracy in identifying early signs of cognitive decline, offering new hope for early diagnosis . Study: Enhancing early detection of cognitive decline in the elderly: a comparative study utilizing large language models in clinical notes. Image Credit: MarutStudio/Shutterstock.
com A recent study in eBioMedicine evaluated the effectiveness of large language models (LLMs) in identifying cognitive decay signs in electronic health records (EHRs). Background Alzheimer's disease and associated dementias afflict millions of individuals, lowering their quality of life and incurring financial and emotional costs. Early identification of cognitive deterioration might lead to more effective therapy and a higher level of care.
LLMs have demonstrated encouraging results in several healthcare domains and clinical language processing tasks, including information extraction, entity recognition, and question-answering. However, their efficacy in detecting specific clinical disorders, such as cognitive decline, using electronic health information is questionable. Few studies have evaluated EHR data using LLMs on Health Insurance Portability and Accountability Act (HIPAA)-compliant cloud computing systems.
Minimal research has compared large language models to traditional artificial intelligence (AI)-based approaches such as machine learning and deep learning. This type of research may influence model augmentation techniques. Abo.
