Background: Biomedical data are usually collections of longitudinal data
assessed at certain points in time. Clinical observations assess the presences
and severity of symptoms, which are the basis for description and modeling of
disease progression. Deciphering potential underlying unknowns solely from the
distinct observation would substantially improve the understanding of
pathological cascades. Hidden Markov Models (HMMs) have been successfully
applied to the processing of possibly noisy continuous signals. The aim was to
improve the application HMMs to multivariate time-series of categorically
distributed data. Here, we used HHMs to study prediction of the loss of free
walking ability as one major clinical deterioration in the most common
autosomal dominantly inherited ataxia disorder worldwide. We used HHMs to
investigate the prediction of loss of the ability to walk freely, representing
a major clinical deterioration in the most common autosomal-dominant inherited
ataxia disorder worldwide.
Results: We present a prediction pipeline which processes data paired with a
configuration file, enabling to construct, validate and query a fully
parameterized HMM-based model. In particular, we provide a theoretical and
practical framework for multivariate time-series inference based on HMMs that
includes constructing multiple HMMs, each to predict a particular observable
variable. Our analysis is done on random data, but also on biomedical data
based on Spinocerebellar ataxia type 3 disease.
Conclusions: HHMs are a promising approach to study biomedical data that
naturally are represented as multivariate time-series. Our implementation of a
HHMs framework is publicly available and can easily be adapted for further
applications