Improving Patient Subtyping on Longitudinal Data using Representations from Mamba-based Architecture
arXiv:2606.28623v1 Announce Type: new Abstract: Effective sub-typing (also known as grouping or clustering) of patients using their electronic health record (EHR) data can greatly inform precision medicine efforts. However, subtyping temporal EHR datasets is known to be challenging due to inherent EHR issues, including complexity and irregularity. In this study, we propose a self-supervised Mamba-based model that learns effective EHR representations and enables enhanced patient subtyping. We evaluate the proposed model on public and private real-world EHR datasets to classify the data based on
The proliferation of advanced AI architectures like Mamba is enabling new approaches to complex data types, making this an opportune time to apply them to challenging biomedical problems.
Improved patient subtyping through advanced AI could significantly accelerate precision medicine, leading to more effective and personalized treatments in healthcare.
The ability to more accurately group patients based on complex longitudinal EHR data enhances the potential for tailored medical interventions and drug development.
- · Healthcare providers
- · Pharmaceutical companies
- · AI in healthcare developers
- · Patients with complex conditions
- · One-size-fits-all treatment models
- · Inefficient clinical trial processes
More precise medical diagnoses and targeted therapies become feasible with better patient segmentation.
The development and approval of new drugs could be faster and more successful due to better identification of patient cohorts.
Personalized health insurance models might evolve, offering customized plans based on highly granular patient risk profiles.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.LG