Tracking trajectories of multiple long-term conditions using dynamic patient-cluster associations

Ron Kremer; Syed Mohib Raza; Fabiola Eto; John Casement; Christian Atallah; Sarah Finer; Dennis Lendrem; Michael Barnes; Nick J. Reynolds; Paolo Missier

doi:10.1109/BigData55660.2022.10021034

Tracking trajectories of multiple long-term conditions using dynamic patient-cluster associations

Ron Kremer^*, Syed Mohib Raza, Fabiola Eto, John Casement, Christian Atallah, Sarah Finer, Dennis Lendrem, Michael Barnes, Nick J. Reynolds, Paolo Missier

^*Corresponding author for this work

Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

Momentum has been growing into research to better understand the dynamics of multiple long-term conditions - multimorbidity (MLTC-M), defined as the co-occurrence of two or more long-term or chronic conditions within an individual. Several research efforts make use of Electronic Health Records (EHR), which represent patients' medical histories. These range from discovering patterns of multimorbidity, namely by clustering diseases based on their co-occurrence in EHRs, to using EHRs to predict the next disease or other specific outcomes. One problem with the former approach is that it discards important temporal information on the co-occurrence, while the latter requires "big"data volumes that are not always available from routinely collected EHRs, limiting the robustness of the resulting models.In this paper we take an intermediate approach, where initially we use about 143,000 EHRs from UK Biobank to perform time-independent clustering using topic modelling, and Latent Dirichlet Allocation specifically. We then propose a metric to measure how strongly a patient is "attracted"into any given cluster at any point through their medical history. By tracking how such gravitational pull changes over time, we may then be able to narrow the scope for potential interventions and preventative measures to specific clusters, without having to resort to full-fledged predictive modelling.In this preliminary work we show exemplars of these dynamic associations, which suggest that further exploration may lead to actionable insights into patients' medical trajectories.

Original language	English
Title of host publication	2022 IEEE International Conference on Big Data
Editors	Shusaku Tsumoto, Yukio Ohsawa, Lei Chen, Dirk Van den Poel, Xiaohua Hu, Yoichi Motomura, Takuya Takagi, Lingfei Wu, Ying Xie, Akihiro Abe, Vijay Raghavan
Publisher	Institute of Electrical and Electronics Engineers (IEEE)
Pages	4390-4399
Number of pages	10
ISBN (Electronic)	9781665480451
ISBN (Print)	9781665480468
DOIs	https://doi.org/10.1109/BigData55660.2022.10021034
Publication status	Published - 26 Jan 2023
Event	2022 IEEE International Conference on Big Data, Big Data 2022 - Osaka, Japan Duration: 17 Dec 2022 → 20 Dec 2022

Publication series

Name	Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022

Conference

Conference	2022 IEEE International Conference on Big Data, Big Data 2022
Country/Territory	Japan
City	Osaka
Period	17/12/22 → 20/12/22

Bibliographical note

Funding Information:
This study received funding from the National Institute for Health and Care Research (NIHR), Artificial Intelligence for Multiple Long-Term Conditions (AIM) Development grant. N.J.R. is also supported by the Newcastle Biomedical Research Centre, the Newcastle NIHR Medtech and In Vitro Diagnostics Cooperative and is a NIHR Senior Investigator.

Publisher Copyright:
© 2022 IEEE.

Keywords

electronic health records
MLTC-M
multi-morbidity
topic modelling

ASJC Scopus subject areas

Modelling and Simulation
Computer Networks and Communications
Information Systems
Information Systems and Management
Safety, Risk, Reliability and Quality
Control and Optimization

Access to Document

10.1109/BigData55660.2022.10021034

Cite this

Kremer, R., Raza, S. M., Eto, F., Casement, J., Atallah, C., Finer, S., Lendrem, D., Barnes, M., Reynolds, N. J., & Missier, P. (2023). Tracking trajectories of multiple long-term conditions using dynamic patient-cluster associations. In S. Tsumoto, Y. Ohsawa, L. Chen, D. Van den Poel, X. Hu, Y. Motomura, T. Takagi, L. Wu, Y. Xie, A. Abe, & V. Raghavan (Eds.), 2022 IEEE International Conference on Big Data (pp. 4390-4399). (Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022). Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1109/BigData55660.2022.10021034

Kremer, Ron ; Raza, Syed Mohib ; Eto, Fabiola et al. / Tracking trajectories of multiple long-term conditions using dynamic patient-cluster associations. 2022 IEEE International Conference on Big Data. editor / Shusaku Tsumoto ; Yukio Ohsawa ; Lei Chen ; Dirk Van den Poel ; Xiaohua Hu ; Yoichi Motomura ; Takuya Takagi ; Lingfei Wu ; Ying Xie ; Akihiro Abe ; Vijay Raghavan. Institute of Electrical and Electronics Engineers (IEEE), 2023. pp. 4390-4399 (Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022).

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title = "Tracking trajectories of multiple long-term conditions using dynamic patient-cluster associations",

abstract = "Momentum has been growing into research to better understand the dynamics of multiple long-term conditions - multimorbidity (MLTC-M), defined as the co-occurrence of two or more long-term or chronic conditions within an individual. Several research efforts make use of Electronic Health Records (EHR), which represent patients' medical histories. These range from discovering patterns of multimorbidity, namely by clustering diseases based on their co-occurrence in EHRs, to using EHRs to predict the next disease or other specific outcomes. One problem with the former approach is that it discards important temporal information on the co-occurrence, while the latter requires {"}big{"}data volumes that are not always available from routinely collected EHRs, limiting the robustness of the resulting models.In this paper we take an intermediate approach, where initially we use about 143,000 EHRs from UK Biobank to perform time-independent clustering using topic modelling, and Latent Dirichlet Allocation specifically. We then propose a metric to measure how strongly a patient is {"}attracted{"}into any given cluster at any point through their medical history. By tracking how such gravitational pull changes over time, we may then be able to narrow the scope for potential interventions and preventative measures to specific clusters, without having to resort to full-fledged predictive modelling.In this preliminary work we show exemplars of these dynamic associations, which suggest that further exploration may lead to actionable insights into patients' medical trajectories.",

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author = "Ron Kremer and Raza, {Syed Mohib} and Fabiola Eto and John Casement and Christian Atallah and Sarah Finer and Dennis Lendrem and Michael Barnes and Reynolds, {Nick J.} and Paolo Missier",

note = "Funding Information: This study received funding from the National Institute for Health and Care Research (NIHR), Artificial Intelligence for Multiple Long-Term Conditions (AIM) Development grant. N.J.R. is also supported by the Newcastle Biomedical Research Centre, the Newcastle NIHR Medtech and In Vitro Diagnostics Cooperative and is a NIHR Senior Investigator. Publisher Copyright: {\textcopyright} 2022 IEEE.; 2022 IEEE International Conference on Big Data, Big Data 2022 ; Conference date: 17-12-2022 Through 20-12-2022",

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editor = "Shusaku Tsumoto and Yukio Ohsawa and Lei Chen and {Van den Poel}, Dirk and Xiaohua Hu and Yoichi Motomura and Takuya Takagi and Lingfei Wu and Ying Xie and Akihiro Abe and Vijay Raghavan",

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Kremer, R, Raza, SM, Eto, F, Casement, J, Atallah, C, Finer, S, Lendrem, D, Barnes, M, Reynolds, NJ & Missier, P 2023, Tracking trajectories of multiple long-term conditions using dynamic patient-cluster associations. in S Tsumoto, Y Ohsawa, L Chen, D Van den Poel, X Hu, Y Motomura, T Takagi, L Wu, Y Xie, A Abe & V Raghavan (eds), 2022 IEEE International Conference on Big Data. Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022, Institute of Electrical and Electronics Engineers (IEEE), pp. 4390-4399, 2022 IEEE International Conference on Big Data, Big Data 2022, Osaka, Japan, 17/12/22. https://doi.org/10.1109/BigData55660.2022.10021034

Tracking trajectories of multiple long-term conditions using dynamic patient-cluster associations. / Kremer, Ron; Raza, Syed Mohib; Eto, Fabiola et al.
2022 IEEE International Conference on Big Data. ed. / Shusaku Tsumoto; Yukio Ohsawa; Lei Chen; Dirk Van den Poel; Xiaohua Hu; Yoichi Motomura; Takuya Takagi; Lingfei Wu; Ying Xie; Akihiro Abe; Vijay Raghavan. Institute of Electrical and Electronics Engineers (IEEE), 2023. p. 4390-4399 (Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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T1 - Tracking trajectories of multiple long-term conditions using dynamic patient-cluster associations

AU - Kremer, Ron

AU - Raza, Syed Mohib

AU - Eto, Fabiola

AU - Casement, John

AU - Atallah, Christian

AU - Finer, Sarah

AU - Lendrem, Dennis

AU - Barnes, Michael

AU - Reynolds, Nick J.

AU - Missier, Paolo

N1 - Funding Information: This study received funding from the National Institute for Health and Care Research (NIHR), Artificial Intelligence for Multiple Long-Term Conditions (AIM) Development grant. N.J.R. is also supported by the Newcastle Biomedical Research Centre, the Newcastle NIHR Medtech and In Vitro Diagnostics Cooperative and is a NIHR Senior Investigator. Publisher Copyright: © 2022 IEEE.

PY - 2023/1/26

Y1 - 2023/1/26

N2 - Momentum has been growing into research to better understand the dynamics of multiple long-term conditions - multimorbidity (MLTC-M), defined as the co-occurrence of two or more long-term or chronic conditions within an individual. Several research efforts make use of Electronic Health Records (EHR), which represent patients' medical histories. These range from discovering patterns of multimorbidity, namely by clustering diseases based on their co-occurrence in EHRs, to using EHRs to predict the next disease or other specific outcomes. One problem with the former approach is that it discards important temporal information on the co-occurrence, while the latter requires "big"data volumes that are not always available from routinely collected EHRs, limiting the robustness of the resulting models.In this paper we take an intermediate approach, where initially we use about 143,000 EHRs from UK Biobank to perform time-independent clustering using topic modelling, and Latent Dirichlet Allocation specifically. We then propose a metric to measure how strongly a patient is "attracted"into any given cluster at any point through their medical history. By tracking how such gravitational pull changes over time, we may then be able to narrow the scope for potential interventions and preventative measures to specific clusters, without having to resort to full-fledged predictive modelling.In this preliminary work we show exemplars of these dynamic associations, which suggest that further exploration may lead to actionable insights into patients' medical trajectories.

AB - Momentum has been growing into research to better understand the dynamics of multiple long-term conditions - multimorbidity (MLTC-M), defined as the co-occurrence of two or more long-term or chronic conditions within an individual. Several research efforts make use of Electronic Health Records (EHR), which represent patients' medical histories. These range from discovering patterns of multimorbidity, namely by clustering diseases based on their co-occurrence in EHRs, to using EHRs to predict the next disease or other specific outcomes. One problem with the former approach is that it discards important temporal information on the co-occurrence, while the latter requires "big"data volumes that are not always available from routinely collected EHRs, limiting the robustness of the resulting models.In this paper we take an intermediate approach, where initially we use about 143,000 EHRs from UK Biobank to perform time-independent clustering using topic modelling, and Latent Dirichlet Allocation specifically. We then propose a metric to measure how strongly a patient is "attracted"into any given cluster at any point through their medical history. By tracking how such gravitational pull changes over time, we may then be able to narrow the scope for potential interventions and preventative measures to specific clusters, without having to resort to full-fledged predictive modelling.In this preliminary work we show exemplars of these dynamic associations, which suggest that further exploration may lead to actionable insights into patients' medical trajectories.

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M3 - Conference contribution

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T3 - Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022

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BT - 2022 IEEE International Conference on Big Data

A2 - Tsumoto, Shusaku

A2 - Ohsawa, Yukio

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Kremer R, Raza SM, Eto F, Casement J, Atallah C, Finer S et al. Tracking trajectories of multiple long-term conditions using dynamic patient-cluster associations. In Tsumoto S, Ohsawa Y, Chen L, Van den Poel D, Hu X, Motomura Y, Takagi T, Wu L, Xie Y, Abe A, Raghavan V, editors, 2022 IEEE International Conference on Big Data. Institute of Electrical and Electronics Engineers (IEEE). 2023. p. 4390-4399. (Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022). doi: 10.1109/BigData55660.2022.10021034

Tracking trajectories of multiple long-term conditions using dynamic patient-cluster associations

Abstract

Publication series

Conference

Bibliographical note

Keywords

ASJC Scopus subject areas

Access to Document

Fingerprint

Cite this