Viral load drop is a sensitive indicator of sustained response to
therapy. Our analysis shows that progressive lowering of viral load over the
course of treatment is a powerful prognostic.
In addition to viral load, other clinical factors
impact clinical outcome. These other factors become especially important when a
patient's viral-load profile is non-optimal for sustained response prognostics. The SLSTM
model leverages these other factors effectively and efficiently with
the help of DOE concepts.
In general new clinical diagnostics are becoming increasingly sensitive for final
clinical outcome, but specificity tends to remain mediocre. The SLSTM
system produces prognostic models that not only promotes this inherent
sensitivity but also advances specificity to acceptable levels by utilizing all the
information content in the patient's clinical profile.
For the first time one trains (optimizes/fits) clinical outcome on 30
patients and then can predict accurately
the outcome on 150 other patients. These "critical" patients define the
boundary of successful clinical outcome.
The application/impact of SLSTM to other fields such as
toxicology would be evolutionary.
The SLSTM process applied to available clinical
- Build model including functions that cover important combinations among
clinical variables, ie, the super vectors.
- Identify critical patients, a DOE-based process
- Produce a stable model trained on the critical patients
- Validate the model