Preprint
A Generic Framework for Hidden Markov Models on Biomedical Data
arXiv, Vol.25 July 2023
Cornell University
2023
Abstract
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.
Details
- Title
- A Generic Framework for Hidden Markov Models on Biomedical Data
- Authors
- Richard Fechner (Author) - Federal Institute for Vocational Education and TrainingJens Dörpinghaus (Corresponding Author) - Federal Institute for Vocational Education and TrainingRobert Rockenfeller (Author) - University of the Sunshine Coast, Queensland, School of Science, Technology and EngineeringJennifer Faber (Author) - German Center for Neurodegenerative Diseases
- Publication details
- arXiv, Vol.25 July 2023
- Publisher
- Cornell University
- Date published
- 2023
- DOI
- 10.48550/arxiv.2307.13288
- ISSN
- 2331-8422
- Grant note
- This work was supported by a postdoc fellowship of the German Academic Exchange Service (DAAD), granted to RR. JF receives funding of the National Ataxia Foundation (NAF) and as a fellow of the Hertie Network of Excellence in Clinical Neuroscience. This publication is an outcome of ESMI, an EU Joint Programme - Neurodegenerative Disease Research (JPND) project (see www.jpnd.eu). The project is supported through the following funding organisations under the aegis of JPND: Germany, Federal Ministry of Education and Research (BMBF; funding codes 01ED1602A/B); Netherlands, The Netherlands Organisation for Health Research and Development; Portugal, Foundation for Science and Technology and Regional Fund for Science and Technology of the Azores; United Kingdom, Medical Research Council. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 643417.
- Organisation Unit
- School of Science, Technology and Engineering
- Language
- English
- Record Identifier
- 99743798702621
- Output Type
- Preprint
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