Current monitoring and innovative predictive modeling to improve care in the pediatric cardiac intensive care unit

Olive, Mary K; Owens, Gabe E.

doi:10.21037/tp.2018.04.03

Cited by 31 publications

(17 citation statements)

References 61 publications

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“…This, coupled with recent advances in machine learning, artificial intelligence techniques, and data archiving hardware, has facilitated the discovery of data-driven characteristics and patterns of diseases [18, 36, 46–48]. However, the numerous developmental stages, baseline age-related differences in physiologic parameters, and the wide range of underlying pathologic diversity present unique challenges for the analysis of PICU patient data [20, 21]. Moreover, physiological data of the patient is continuously influenced by clinical interventions such as oxygen supplement, volume resuscitation, and vasopressor use, given that the core principle of intensive care is to maintain the steady state [20].…”

Section: Discussionmentioning

confidence: 99%

“…The rapid development in machine learning, coupled with the richness of data from extensive patient monitoring in the intensive care unit (ICU), provides unprecedented opportunities for the development of new prediction scores in the field of critical care [17–19]. Challenges in the analytics of PICU data, including pathologic diversity and complexity [20] and the wide range of age and developmental stages, are anticipated to be addressed by the implementation of innovative predictive modeling [18, 21].…”

Section: Introductionmentioning

confidence: 99%

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A deep learning model for real-time mortality prediction in critically ill children

Kim

Kim²,

Cho

et al. 2019

Crit Care

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Background The rapid development in big data analytics and the data-rich environment of intensive care units together provide unprecedented opportunities for medical breakthroughs in the field of critical care. We developed and validated a machine learning-based model, the Pediatric Risk of Mortality Prediction Tool (PROMPT), for real-time prediction of all-cause mortality in pediatric intensive care units. Methods Utilizing two separate retrospective observational cohorts, we conducted model development and validation using a machine learning algorithm with a convolutional neural network. The development cohort comprised 1445 pediatric patients with 1977 medical encounters admitted to intensive care units from January 2011 to December 2017 at Severance Hospital (Seoul, Korea). The validation cohort included 278 patients with 364 medical encounters admitted to the pediatric intensive care unit from January 2016 to November 2017 at Samsung Medical Center. Results Using seven vital signs, along with patient age and body weight on intensive care unit admission, PROMPT achieved an area under the receiver operating characteristic curve in the range of 0.89–0.97 for mortality prediction 6 to 60 h prior to death. Our results demonstrated that PROMPT provided high sensitivity with specificity and outperformed the conventional severity scoring system, the Pediatric Index of Mortality, in predictive ability. Model performance was indistinguishable between the development and validation cohorts. Conclusions PROMPT is a deep model-based, data-driven early warning score tool that can predict mortality in critically ill children and may be useful for the timely identification of deteriorating patients. Electronic supplementary material The online version of this article (10.1186/s13054-019-2561-z) contains supplementary material, which is available to authorized users.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A deep learning model for real-time mortality prediction in critically ill children

Kim

Kim²,

Cho

et al. 2019

Crit Care

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“…Multiparameter models that integrate physiological, laboratory and/or clinical data to provide an early warning of patients progressing towards a critical event have the potential to improve physiological monitoring in applications from adult and pediatric intensive care units [[1], [2], [3], [4]] to telemedicine [5,6]. The utility of an early warning index to indicate an event of interest may be affected by characteristics such as 1) probability the event occurs when a warning is given, 2) probability to correctly detect true events and 3) probability to incorrectly detect non-events as events.…”

Section: Methods Detailsmentioning

confidence: 99%

A framework to characterize the performance of early warning index alarm systems for patient monitoring

2019

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“…The DEWS study (deep-learning-based early warning system) revealed that predictive artificial intelligence models using only four vital signs could lead to earlier detection of patients at risk for clinical deterioration in the paediatric ICU. 36 In a multicentre study, ICU researchers successfully developed a personalised algorithm to discriminate between sedation levels based on heart rate variability. 37 With further refinement, the potential exists for an automated cardiac ICU system that improves monitoring of sedation levels and reduces dose-related complications through algorithmic reading of electrocardiogram data.…”

Section: Hospital Monitoringmentioning

confidence: 99%

A primer on artificial intelligence for the paediatric cardiologist

2020

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AbstractThe combination of pediatric cardiology being both a perceptual and a cognitive subspecialty demands a complex decision-making model which makes artificial intelligence a particularly attractive technology with great potential. The prototypical artificial intelligence system would autonomously impute patient data into a collaborative database that stores, syncs, interprets and ultimately classifies the patient’s profile to specific disease phenotypes to compare against a large aggregate of shared peer health data and outcomes, the current medical body of literature and ongoing trials to offer morbidity and mortality prediction, drug therapy options targeted to each patient’s genetic profile, tailored surgical plans and recommendations for timing of sequential imaging. The focus of this review paper is to offer a primer on artificial intelligence and paediatric cardiology by briefly discussing the history of artificial intelligence in medicine, modern and future applications in adult and paediatric cardiology across selected concentrations, and current barriers to implementation of these technologies.

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Current monitoring and innovative predictive modeling to improve care in the pediatric cardiac intensive care unit

Cited by 31 publications

References 61 publications

A deep learning model for real-time mortality prediction in critically ill children

A deep learning model for real-time mortality prediction in critically ill children

A framework to characterize the performance of early warning index alarm systems for patient monitoring

A primer on artificial intelligence for the paediatric cardiologist

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