arXiv:2511.02340v3 Announce Type: replace Abstract: Chronic Kidney Disease (CKD) affects nearly 10\% of the global population and often progresses to end-stage renal failure. Accurate prognosis prediction is vital for timely interventions and resource optimization. We present a transformer-based framework for predicting CKD progression using multi-modal electronic health records (EHR) from the Seoul National University Hospital OMOP Common Data Model. Our approach (\textbf{ProQ-BERT}) integrates demographic, clinical, and laboratory data, employing quantization-based tokenization for continuou
The proliferation of advanced AI models like transformers, coupled with increasing access to comprehensive electronic health records, enables more sophisticated predictive analytics in healthcare now.
Accurate prediction of chronic disease progression can revolutionize healthcare delivery, enabling proactive interventions, optimizing resource allocation, and improving patient outcomes globally.
The ability to integrate multi-modal EHR data with transformer-based AI systems changes how chronic disease progression is forecasted, moving from reactive to highly predictive care models.
- · Healthcare AI developers
- · Patients with chronic diseases
- · Hospitals and healthcare systems
- · South Korea
- · Traditional diagnostic methods
- · Healthcare systems reactive to disease progression
Improved early intervention leading to better patient prognosis and reduced healthcare costs for chronic kidney disease.
Expansion of similar transformer-based AI models to predict progression for other chronic and degenerative diseases, accelerating personalized medicine.
Increased national investment in AI applications for public health, potentially leading to sovereign AI initiatives focused on medical data privacy and compute.
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Read at arXiv cs.AI