Modeling the Dynamics of Multiple Disease Occurrence by Latent States

Marcos L. P. Bueno, Arjen Hommersom, Peter J. F. Lucas, Mariana Lobo, Pedro P. Rodrigues

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingAcademicpeer-review

Abstract

The current availability of large volumes of health care data makes it a promising data source to new views on disease interaction. Most of the times, patients have multiple diseases instead of a single one (also known as multimorbidity), but the small size of most clinical research data makes it hard to impossible to investigate this issue. In this paper, we propose a latent-based approach to expand patient evolution in temporal electronic health records, which can be uninformative due to its very general events. We introduce the notion of clusters of hidden states allowing for an expanded understanding of the multiple dynamics that underlie events in such data. Clusters are defined as part of hidden Markov models learned from such data, where the number of hidden states is not known beforehand. We evaluate the proposed approach based on a large dataset from Dutch practices of patients that had events on comorbidities related to atherosclerosis. The discovered clusters are further correlated to medical-oriented outcomes in order to show the usefulness of the proposed method.
Original languageEnglish
Title of host publicationScalable Uncertainty Management
Subtitle of host publication12th International Conference, SUM 2018, Milan, Italy, October 3-5, 2018, Proceedings
EditorsDavide Ciucci, Gabriella Pasi, Barbara Vantaggi
Place of PublicationCham
PublisherSpringer International Publishing AG
Pages93-107
Number of pages15
ISBN (Print)978-3-030-00461-3
DOIs
Publication statusPublished - 2018
EventInternational Conference on Scalable Uncertainty Management: Scalable Uncertainty Management - Milan, Italy
Duration: 3 Oct 20185 Oct 2018
https://link.springer.com/book/10.1007/978-3-030-00461-3

Publication series

NameLecture Notes in Computer Science
PublisherSpringer
Number11142

Conference

ConferenceInternational Conference on Scalable Uncertainty Management
Abbreviated titleSUM 2018
CountryItaly
CityMilan
Period3/10/185/10/18
Internet address

Fingerprint

Hidden Markov models
Health care
Health
Availability

Cite this

Bueno, M. L. P., Hommersom, A., Lucas, P. J. F., Lobo, M., & Rodrigues, P. P. (2018). Modeling the Dynamics of Multiple Disease Occurrence by Latent States. In D. Ciucci, G. Pasi, & B. Vantaggi (Eds.), Scalable Uncertainty Management: 12th International Conference, SUM 2018, Milan, Italy, October 3-5, 2018, Proceedings (pp. 93-107). (Lecture Notes in Computer Science; No. 11142). Cham: Springer International Publishing AG. https://doi.org/10.1007/978-3-030-00461-3_7
Bueno, Marcos L. P. ; Hommersom, Arjen ; Lucas, Peter J. F. ; Lobo, Mariana ; Rodrigues, Pedro P. / Modeling the Dynamics of Multiple Disease Occurrence by Latent States. Scalable Uncertainty Management: 12th International Conference, SUM 2018, Milan, Italy, October 3-5, 2018, Proceedings. editor / Davide Ciucci ; Gabriella Pasi ; Barbara Vantaggi. Cham : Springer International Publishing AG, 2018. pp. 93-107 (Lecture Notes in Computer Science; 11142).
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title = "Modeling the Dynamics of Multiple Disease Occurrence by Latent States",
abstract = "The current availability of large volumes of health care data makes it a promising data source to new views on disease interaction. Most of the times, patients have multiple diseases instead of a single one (also known as multimorbidity), but the small size of most clinical research data makes it hard to impossible to investigate this issue. In this paper, we propose a latent-based approach to expand patient evolution in temporal electronic health records, which can be uninformative due to its very general events. We introduce the notion of clusters of hidden states allowing for an expanded understanding of the multiple dynamics that underlie events in such data. Clusters are defined as part of hidden Markov models learned from such data, where the number of hidden states is not known beforehand. We evaluate the proposed approach based on a large dataset from Dutch practices of patients that had events on comorbidities related to atherosclerosis. The discovered clusters are further correlated to medical-oriented outcomes in order to show the usefulness of the proposed method.",
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Bueno, MLP, Hommersom, A, Lucas, PJF, Lobo, M & Rodrigues, PP 2018, Modeling the Dynamics of Multiple Disease Occurrence by Latent States. in D Ciucci, G Pasi & B Vantaggi (eds), Scalable Uncertainty Management: 12th International Conference, SUM 2018, Milan, Italy, October 3-5, 2018, Proceedings. Lecture Notes in Computer Science, no. 11142, Springer International Publishing AG, Cham, pp. 93-107, International Conference on Scalable Uncertainty Management, Milan, Italy, 3/10/18. https://doi.org/10.1007/978-3-030-00461-3_7

Modeling the Dynamics of Multiple Disease Occurrence by Latent States. / Bueno, Marcos L. P.; Hommersom, Arjen; Lucas, Peter J. F.; Lobo, Mariana; Rodrigues, Pedro P.

Scalable Uncertainty Management: 12th International Conference, SUM 2018, Milan, Italy, October 3-5, 2018, Proceedings. ed. / Davide Ciucci; Gabriella Pasi; Barbara Vantaggi. Cham : Springer International Publishing AG, 2018. p. 93-107 (Lecture Notes in Computer Science; No. 11142).

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingAcademicpeer-review

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AB - The current availability of large volumes of health care data makes it a promising data source to new views on disease interaction. Most of the times, patients have multiple diseases instead of a single one (also known as multimorbidity), but the small size of most clinical research data makes it hard to impossible to investigate this issue. In this paper, we propose a latent-based approach to expand patient evolution in temporal electronic health records, which can be uninformative due to its very general events. We introduce the notion of clusters of hidden states allowing for an expanded understanding of the multiple dynamics that underlie events in such data. Clusters are defined as part of hidden Markov models learned from such data, where the number of hidden states is not known beforehand. We evaluate the proposed approach based on a large dataset from Dutch practices of patients that had events on comorbidities related to atherosclerosis. The discovered clusters are further correlated to medical-oriented outcomes in order to show the usefulness of the proposed method.

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Bueno MLP, Hommersom A, Lucas PJF, Lobo M, Rodrigues PP. Modeling the Dynamics of Multiple Disease Occurrence by Latent States. In Ciucci D, Pasi G, Vantaggi B, editors, Scalable Uncertainty Management: 12th International Conference, SUM 2018, Milan, Italy, October 3-5, 2018, Proceedings. Cham: Springer International Publishing AG. 2018. p. 93-107. (Lecture Notes in Computer Science; 11142). https://doi.org/10.1007/978-3-030-00461-3_7