Research questions
This research aims to advance the early detection and mechanistic understanding of neurological critical diseases, as well as related cardio-cerebrovascular and systemic conditions, by addressing the following aspects:
1. Identifying key clinical, biological, electrophysiological, genetic, and lifestyle factors associated with the onset, progression, and outcomes of neurological critical diseases
2. Developing deep learning models to predict disease risk, early manifestations, and critical events using large-scale, multi-dimensional cohort data.
3.Utilizing large language models (LLMs) to generate automated, personalized diagnostic and prognostic reports, making predictive insights accessible and clinically actionable.
Research objectives
1. Develop predictive models using deep learning and diverse cohort data (electrophysiological signals, genetics, biomarkers, imaging, clinical records) to identify early indicators of neurological critical and cardiovascular diseases.
2. Improve risk stratification by integrating lifestyle, medical history, and multi-modal biomarkers with primary data sources.
3. Automate clinical reporting with LLMs to generate personalized diagnostic and prognostic reports, supporting efficient and informed care.
scientific rationale for the research
Neurological, cardiovascular, cerebrovascular, and sleep-related diseases are major causes of global morbidity and mortality, yet early detection remains limited. The UK Biobank offers large-scale, high-quality data from over 500,000 participants, including electrophysiological signals, genetics, imaging, biomarkers, and clinical records. This project will apply deep learning to identify early indicators, improve risk stratification, and generate mechanistic insights. Large language models will translate predictive outputs into automated reports, supporting precision medicine and reducing the public health burden of these diseases.
