Hidden Markov Models for Sepsis Classification

Parente, Jacquelyn D.; Möller, Knut; Chase, J. Geoffrey

doi:10.1016/j.ifacol.2018.11.658

Cited by 3 publications

(2 citation statements)

References 14 publications

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“…The work [10] uses sequential clinical events such as Bradycardia-Desaturation events and does not use raw physiological signals. On the other hand, the work [11] considers raw physiological data for adults, where HMM state distributions are modelled using kernel density estimators. Our contributions:…”

Section: Introductionmentioning

confidence: 99%

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Hidden Markov Models for Sepsis Detection in Preterm Infants

Honoré

Liu

Forsberg

et al. 2020

ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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We explore the use of traditional and contemporary hidden Markov models (HMMs) for sequential physiological data analysis and sepsis prediction in preterm infants. We investigate the use of classical Gaussian mixture model based HMM, and a recently proposed neural network based HMM. To improve the neural network based HMM, we propose a discriminative training approach. Experimental results show the potential of HMMs over logistic regression, support vector machine and extreme learning machine.

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Section: Introductionmentioning

confidence: 99%

“…They do not explore dynamical systems such as HMM. There are two main works [10,11] where HMM was explored for sepsis prediction. The work [10] uses sequential clinical events such as Bradycardia-Desaturation events and does not use raw physiological signals.…”

Section: Introductionmentioning

confidence: 99%

Hidden Markov Models for Sepsis Detection in Preterm Infants

Honoré

Liu

Forsberg

et al. 2020

ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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Predictive modeling of bacterial infections and antibiotic therapy needs in critically ill adults

Eickelberg

Sanchez-Pinto

Luo

2020

Journal of Biomedical Informatics

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Predicting Infections Using Computational Intelligence – A Systematic Review

et al. 2020

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Infections encompass a set of medical conditions of very diverse kinds that can pose a significant risk to health, and even death. As with many other diseases, early diagnosis can help to provide patients with proper care to minimize the damage produced by the disease, or to isolate them to avoid the risk of spread. In this context, computational intelligence can be useful to predict the risk of infection in patients, raising early alarms that can aid medical teams to respond as quick as possible. In this paper, we survey the state of the art on infection prediction using computer science by means of a systematic literature review. The objective is to find papers where computational intelligence is used to predict infections in patients using physiological data as features. We have posed one major research question along with nine specific subquestions. The whole review process is thoroughly described, and eight databases are considered which index most of the literature published in different scholarly formats. A total of 101 relevant documents have been found in the period comprised between 2003 and 2019, and a detailed study of these documents is carried out to classify the works and answer the research questions posed, resulting to our best knowledge in the most comprehensive study of its kind. We conclude that the most widely addressed infection is by far sepsis, followed by Clostridium difficile infection and surgical site infections. Most works use machine learning techniques, from which logistic regression, support vector machines, random forest and naive Bayes are the most common. Some machine learning works provide some ideas on the problems of small data and class imbalance, which can be of interest. The current systematic literature review shows that automatic diagnosis of infectious diseases using computational intelligence is well documented in the medical literature.

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Hidden Markov Models for Sepsis Classification

Cited by 3 publications

References 14 publications

Hidden Markov Models for Sepsis Detection in Preterm Infants

Hidden Markov Models for Sepsis Detection in Preterm Infants

Predictive modeling of bacterial infections and antibiotic therapy needs in critically ill adults

Predicting Infections Using Computational Intelligence – A Systematic Review

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