An HMM-ensemble approach to predict severity progression of ICU treatment for hospitalized COVID-19 patients

Federica Mandreoli; Federico Motta; Paolo Missier

doi:10.1109/ICMLA52953.2021.00211

An HMM-ensemble approach to predict severity progression of ICU treatment for hospitalized COVID-19 patients

Federica Mandreoli, Federico Motta, Paolo Missier

Computer Science

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

Abstract

COVID-19-related pneumonia requires different modalities of Intensive Care Unit (ICU) interventions at different times to facilitate breathing, depending on severity progression. The ability for clinical staff to predict how patients admitted to hospital will require more or less ICU treatment on a daily basis is critical to ICU management. For real datasets that are sparse and incomplete and where the most important state transitions (dismissal, death) are rare, a standard Hidden Markov Model (HMM) approach is insufficient, as it is prone to overfitting. In this paper we propose a more sophisticated ensemble-based approach that involves training multiple HMMs, each specialized in a subset of the state transitions, and then selecting the more plausible predictions either by selecting or combining the models. We have validated the approach on a live dataset of about 1, 000 patients from a partner hospital. Our results show that rare events, as well as the transitions to the most severe treatments outperform state of the art approaches.

Original language	English
Title of host publication	Proceedings - 20th IEEE International Conference on Machine Learning and Applications, ICMLA 2021
Editors	M. Arif Wani, Ishwar K. Sethi, Weisong Shi, Guangzhi Qu, Daniela Stan Raicu, Ruoming Jin
Publisher	Institute of Electrical and Electronics Engineers (IEEE)
Pages	1299-1306
Number of pages	8
ISBN (Electronic)	9781665443371
ISBN (Print)	9781665443388
DOIs	https://doi.org/10.1109/ICMLA52953.2021.00211
Publication status	Published - 25 Jan 2022
Event	20th IEEE International Conference on Machine Learning and Applications, ICMLA 2021 - Virtual, Online, United States Duration: 13 Dec 2021 → 16 Dec 2021

Publication series

Name	Proceedings - 20th IEEE International Conference on Machine Learning and Applications, ICMLA 2021

Conference

Conference	20th IEEE International Conference on Machine Learning and Applications, ICMLA 2021
Country/Territory	United States
City	Virtual, Online
Period	13/12/21 → 16/12/21

Bibliographical note

Funding Information:
The authors would like to thank Prof. Giovanni Guaraldi (University Hospital of Modena) for providing the dataset which made this study possible. We would also like to thank M.Sc. Davide Ferrari (Universita di Modena e Reggio Emilia) and Ph.D. Jonathan Law (Newcastle University) for their precious contributions during the very early stages of this research.

Publisher Copyright:
© 2021 IEEE.

Keywords

COVID-19
Ensemble methods
Hidden Markov models
Imbalanced dataset
Machine learning
Medical expert systems

ASJC Scopus subject areas

Safety, Risk, Reliability and Quality
Health Informatics
Artificial Intelligence
Computer Science Applications

Access to Document

10.1109/ICMLA52953.2021.00211Licence: None: All rights reserved

Cite this

Mandreoli, F., Motta, F., & Missier, P. (2022). An HMM-ensemble approach to predict severity progression of ICU treatment for hospitalized COVID-19 patients. In M. A. Wani, I. K. Sethi, W. Shi, G. Qu, D. S. Raicu, & R. Jin (Eds.), Proceedings - 20th IEEE International Conference on Machine Learning and Applications, ICMLA 2021 (pp. 1299-1306). (Proceedings - 20th IEEE International Conference on Machine Learning and Applications, ICMLA 2021). Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1109/ICMLA52953.2021.00211

Mandreoli, Federica ; Motta, Federico ; Missier, Paolo. / An HMM-ensemble approach to predict severity progression of ICU treatment for hospitalized COVID-19 patients. Proceedings - 20th IEEE International Conference on Machine Learning and Applications, ICMLA 2021. editor / M. Arif Wani ; Ishwar K. Sethi ; Weisong Shi ; Guangzhi Qu ; Daniela Stan Raicu ; Ruoming Jin. Institute of Electrical and Electronics Engineers (IEEE), 2022. pp. 1299-1306 (Proceedings - 20th IEEE International Conference on Machine Learning and Applications, ICMLA 2021).

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title = "An HMM-ensemble approach to predict severity progression of ICU treatment for hospitalized COVID-19 patients",

abstract = "COVID-19-related pneumonia requires different modalities of Intensive Care Unit (ICU) interventions at different times to facilitate breathing, depending on severity progression. The ability for clinical staff to predict how patients admitted to hospital will require more or less ICU treatment on a daily basis is critical to ICU management. For real datasets that are sparse and incomplete and where the most important state transitions (dismissal, death) are rare, a standard Hidden Markov Model (HMM) approach is insufficient, as it is prone to overfitting. In this paper we propose a more sophisticated ensemble-based approach that involves training multiple HMMs, each specialized in a subset of the state transitions, and then selecting the more plausible predictions either by selecting or combining the models. We have validated the approach on a live dataset of about 1, 000 patients from a partner hospital. Our results show that rare events, as well as the transitions to the most severe treatments outperform state of the art approaches.",

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author = "Federica Mandreoli and Federico Motta and Paolo Missier",

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Mandreoli, F, Motta, F & Missier, P 2022, An HMM-ensemble approach to predict severity progression of ICU treatment for hospitalized COVID-19 patients. in MA Wani, IK Sethi, W Shi, G Qu, DS Raicu & R Jin (eds), Proceedings - 20th IEEE International Conference on Machine Learning and Applications, ICMLA 2021. Proceedings - 20th IEEE International Conference on Machine Learning and Applications, ICMLA 2021, Institute of Electrical and Electronics Engineers (IEEE), pp. 1299-1306, 20th IEEE International Conference on Machine Learning and Applications, ICMLA 2021, Virtual, Online, United States, 13/12/21. https://doi.org/10.1109/ICMLA52953.2021.00211

An HMM-ensemble approach to predict severity progression of ICU treatment for hospitalized COVID-19 patients. / Mandreoli, Federica; Motta, Federico; Missier, Paolo.
Proceedings - 20th IEEE International Conference on Machine Learning and Applications, ICMLA 2021. ed. / M. Arif Wani; Ishwar K. Sethi; Weisong Shi; Guangzhi Qu; Daniela Stan Raicu; Ruoming Jin. Institute of Electrical and Electronics Engineers (IEEE), 2022. p. 1299-1306 (Proceedings - 20th IEEE International Conference on Machine Learning and Applications, ICMLA 2021).

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

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AU - Motta, Federico

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N1 - Funding Information: The authors would like to thank Prof. Giovanni Guaraldi (University Hospital of Modena) for providing the dataset which made this study possible. We would also like to thank M.Sc. Davide Ferrari (Universita di Modena e Reggio Emilia) and Ph.D. Jonathan Law (Newcastle University) for their precious contributions during the very early stages of this research. Publisher Copyright: © 2021 IEEE.

PY - 2022/1/25

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N2 - COVID-19-related pneumonia requires different modalities of Intensive Care Unit (ICU) interventions at different times to facilitate breathing, depending on severity progression. The ability for clinical staff to predict how patients admitted to hospital will require more or less ICU treatment on a daily basis is critical to ICU management. For real datasets that are sparse and incomplete and where the most important state transitions (dismissal, death) are rare, a standard Hidden Markov Model (HMM) approach is insufficient, as it is prone to overfitting. In this paper we propose a more sophisticated ensemble-based approach that involves training multiple HMMs, each specialized in a subset of the state transitions, and then selecting the more plausible predictions either by selecting or combining the models. We have validated the approach on a live dataset of about 1, 000 patients from a partner hospital. Our results show that rare events, as well as the transitions to the most severe treatments outperform state of the art approaches.

AB - COVID-19-related pneumonia requires different modalities of Intensive Care Unit (ICU) interventions at different times to facilitate breathing, depending on severity progression. The ability for clinical staff to predict how patients admitted to hospital will require more or less ICU treatment on a daily basis is critical to ICU management. For real datasets that are sparse and incomplete and where the most important state transitions (dismissal, death) are rare, a standard Hidden Markov Model (HMM) approach is insufficient, as it is prone to overfitting. In this paper we propose a more sophisticated ensemble-based approach that involves training multiple HMMs, each specialized in a subset of the state transitions, and then selecting the more plausible predictions either by selecting or combining the models. We have validated the approach on a live dataset of about 1, 000 patients from a partner hospital. Our results show that rare events, as well as the transitions to the most severe treatments outperform state of the art approaches.

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Mandreoli F, Motta F, Missier P. An HMM-ensemble approach to predict severity progression of ICU treatment for hospitalized COVID-19 patients. In Wani MA, Sethi IK, Shi W, Qu G, Raicu DS, Jin R, editors, Proceedings - 20th IEEE International Conference on Machine Learning and Applications, ICMLA 2021. Institute of Electrical and Electronics Engineers (IEEE). 2022. p. 1299-1306. (Proceedings - 20th IEEE International Conference on Machine Learning and Applications, ICMLA 2021). doi: 10.1109/ICMLA52953.2021.00211

An HMM-ensemble approach to predict severity progression of ICU treatment for hospitalized COVID-19 patients

Abstract

Publication series

Conference

Bibliographical note

Keywords

ASJC Scopus subject areas

Access to Document

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Cite this