Mixture of Input-Output Hidden Markov Models for Heterogeneous Disease Progression Modeling

Taha Ceritli, Andrew P. Creagh, David A. Clifton
Proceedings of the 1st Workshop on Healthcare AI and COVID-19, ICML 2022, PMLR 184:41-53, 2022.

Abstract

A particular challenge for disease progression modeling is the heterogeneity of a disease and its manifestations in the patients. Existing approaches often assume the presence of a single disease progression characteristics which is unlikely for neurodegenerative disorders such as Parkinson’s disease. In this paper, we propose a hierarchical time-series model that can discover multiple disease progression dynamics. The proposed model is an extension of an input-output hidden Markov model that takes into account the clinical assessments of patients’ health status and prescribed medications. We illustrate the benefits of our model using a synthetically generated dataset and a real-world longitudinal dataset for Parkinson’s disease.

Cite this Paper

BibTeX
@InProceedings{pmlr-v184-ceritli22a, title = {Mixture of Input-Output Hidden Markov Models for Heterogeneous Disease Progression Modeling}, author = {Ceritli, Taha and Creagh, Andrew P. and Clifton, David A.}, booktitle = {Proceedings of the 1st Workshop on Healthcare AI and COVID-19, ICML 2022}, pages = {41--53}, year = {2022}, editor = {Xu, Peng and Zhu, Tingting and Zhu, Pengkai and Clifton, David A. and Belgrave, Danielle and Zhang, Yuanting}, volume = {184}, series = {Proceedings of Machine Learning Research}, month = {22 Jul}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v184/ceritli22a/ceritli22a.pdf}, url = {https://proceedings.mlr.press/v184/ceritli22a.html}, abstract = {A particular challenge for disease progression modeling is the heterogeneity of a disease and its manifestations in the patients. Existing approaches often assume the presence of a single disease progression characteristics which is unlikely for neurodegenerative disorders such as Parkinson’s disease. In this paper, we propose a hierarchical time-series model that can discover multiple disease progression dynamics. The proposed model is an extension of an input-output hidden Markov model that takes into account the clinical assessments of patients’ health status and prescribed medications. We illustrate the benefits of our model using a synthetically generated dataset and a real-world longitudinal dataset for Parkinson’s disease.} }
Endnote
%0 Conference Paper %T Mixture of Input-Output Hidden Markov Models for Heterogeneous Disease Progression Modeling %A Taha Ceritli %A Andrew P. Creagh %A David A. Clifton %B Proceedings of the 1st Workshop on Healthcare AI and COVID-19, ICML 2022 %C Proceedings of Machine Learning Research %D 2022 %E Peng Xu %E Tingting Zhu %E Pengkai Zhu %E David A. Clifton %E Danielle Belgrave %E Yuanting Zhang %F pmlr-v184-ceritli22a %I PMLR %P 41--53 %U https://proceedings.mlr.press/v184/ceritli22a.html %V 184 %X A particular challenge for disease progression modeling is the heterogeneity of a disease and its manifestations in the patients. Existing approaches often assume the presence of a single disease progression characteristics which is unlikely for neurodegenerative disorders such as Parkinson’s disease. In this paper, we propose a hierarchical time-series model that can discover multiple disease progression dynamics. The proposed model is an extension of an input-output hidden Markov model that takes into account the clinical assessments of patients’ health status and prescribed medications. We illustrate the benefits of our model using a synthetically generated dataset and a real-world longitudinal dataset for Parkinson’s disease.
APA
Ceritli, T., Creagh, A.P. & Clifton, D.A.. (2022). Mixture of Input-Output Hidden Markov Models for Heterogeneous Disease Progression Modeling. Proceedings of the 1st Workshop on Healthcare AI and COVID-19, ICML 2022, in Proceedings of Machine Learning Research 184:41-53 Available from https://proceedings.mlr.press/v184/ceritli22a.html.

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