Integrating NLP and LLMs to discover biomarkers and mechanisms in Alzheimer's disease

Alzheimer's disease (AD) is a progressive neurological condition characterized by cognitive decline, memory loss, and aberrant behaviour. It affects millions of people globally and is one of the main causes of dementia. The neurodegenerative condition known as AD has intricate, multifaceted mechanisms that make it difficult to comprehend and identify in its early stages. Conventional diagnostic techniques frequently fail to detect the disease in its early stages. By combining Natural Language Processing (NLP) and Large Language Models (LLMs), this research suggests a novel approach for identifying potential biomarkers and underlying mechanisms of AD. Clinical data is gathered from publicly accessible databases and healthcare facilities, including genetic information, neuroimaging scans, and medical records. The pre-processing of unstructured clinical notes involves tokenization and genetic profiles and neuroimaging data are normalized by Z-score normalization for consistency. Multi-Input Convolutional Neural Networks (MI-CNN) are employed to efficiently fuse diverse data sources, allowing for a thorough analysis. Key biomarkers linked to AD are identified and categorized using the Genetic Algorithm combined with Bidirectional Encoder Representations from Transformers (BERT) (GenBERT). By fine-tuning BERT's hyperparameters using genetic optimization approaches, GenBERT enables the effective analysis of large medical datasets, such as patient histories, genetic data, and clinical notes. The combination strategy increases feature selection and the model's capacity to identify minute genomic and linguistic patterns suggestive of AD. The goal of this integrated strategy is to provide early diagnostic tools and new insights into the pathogenesis of the disease, which could transform methods for detecting and treating AD. As it concerns early AD prediction, the GenBERT model performs better than current techniques, obtaining the highest accuracy (98.30%) and F1-score (0.97), as well as greater precision (0.95) and recall (0.92). Additionally, it demonstrates its capacity to reliably identify both positive and negative AD cases with sensitivity (98.65%) and specificity (99.73%). Overall, GenBERT offers a trustworthy and useful tool for AD early diagnosis.