Continuous Prediction of Mortality in the PICU: A Recurrent Neural Network Model in a Single-Center Dataset*

Aczon, Melissa; Ledbetter, David; Laksana, Eugene; Ho, Long Van; Wetzel, Randall C.

doi:10.1097/pcc.0000000000002682

Cited by 38 publications

(35 citation statements)

References 29 publications

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“…73 The clinical application of these models includes the prediction of kidney injury, 74 significant clinical deterioration, 75 and mortality. 76,77 These models are frequently derived using single-center data with validation performed on a separate cohort of patients admitted to the same center. [74][75][76][77] The rise in popularity of similar models has led to calls for greater rigor in their derivation to ensure true clinical utility.…”

Section: Clinical Decision Supportmentioning

confidence: 99%

“…76,77 These models are frequently derived using single-center data with validation performed on a separate cohort of patients admitted to the same center. [74][75][76][77] The rise in popularity of similar models has led to calls for greater rigor in their derivation to ensure true clinical utility. 78 Another group of models includes those created by EHR vendors that are available to use in hospitals that are paying for a particular EHR.…”

Section: Clinical Decision Supportmentioning

confidence: 99%

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The use of machine learning and artificial intelligence within pediatric critical care

et al. 2022

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The field of pediatric critical care has been hampered in the era of precision medicine by our inability to accurately define and subclassify disease phenotypes. This has been caused by heterogeneity across age groups that further challenges the ability to perform randomized controlled trials in pediatrics. One approach to overcome these inherent challenges include the use of machine learning algorithms that can assist in generating more meaningful interpretations from clinical data. This review summarizes machine learning and artificial intelligence techniques that are currently in use for clinical data modeling with relevance to pediatric critical care. Focus has been placed on the differences between techniques and the role of each in the clinical arena. The various forms of clinical decision support that utilize machine learning are also described. We review the applications and limitations of machine learning techniques to empower clinicians to make informed decisions at the bedside.

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Section: Clinical Decision Supportmentioning

confidence: 99%

Section: Clinical Decision Supportmentioning

confidence: 99%

The use of machine learning and artificial intelligence within pediatric critical care

et al. 2022

View full text Add to dashboard Cite

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“…This type of ML models can process entire sequence of data, thus allowing retention of information from previous times and integration with newly acquired data to make a new prediction. Aczon et al ( 41 , 42 ) developed a LSTM model using 430 distinct physiologic, demographic, laboratory, and therapeutic variables, to provide individual patient's mortality risk at any time during their ICU admission when a recorded measurement became available. The authors evaluated the performance of their model at various time points from 0 to 24 h after ICU admission, as well as, from 1 to 24 h prior to discharge.…”

Section: Machine Learning To Predict Mortalitymentioning

confidence: 99%

Machine Learning-Based Systems for the Anticipation of Adverse Events After Pediatric Cardiac Surgery

et al. 2022

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Pediatric congenital heart disease (CHD) patients are at higher risk of postoperative complications and clinical deterioration either due to their underlying pathology or due to the cardiac surgery, contributing significantly to mortality, morbidity, hospital and family costs, and poor quality of life. In current clinical practice, clinical deterioration is detected, in most of the cases, when it has already occurred. Several early warning scores (EWS) have been proposed to assess children at risk of clinical deterioration using vital signs and risk indicators, in order to intervene in a timely manner to reduce the impact of deterioration and risk of death among children. However, EWS are based on measurements performed at a single time point without incorporating trends nor providing information about patient's risk trajectory. Moreover, some of these measurements rely on subjective assessment making them susceptible to different interpretations. All these limitations could explain why the implementation of EWS in high-resource settings failed to show a significant decrease in hospital mortality. By means of machine learning (ML) based algorithms we could integrate heterogeneous and complex data to predict patient's risk of deterioration. In this perspective article, we provide a brief overview of the potential of ML technologies to improve the identification of pediatric CHD patients at high-risk for clinical deterioration after cardiac surgery, and present the CORTEX traffic light, a ML-based predictive system that Sant Joan de Déu Barcelona Children's Hospital is implementing, as an illustration of the application of an ML-based risk stratification system in a relevant hospital setting.

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“…Additionally, ML may be uniquely suited to analyzing the heterogeneous data generated during care and quantifying the complex determinants of the behavior of critically ill patients. Techniques, expectations, and infrastructures for developing and utilizing ML have matured, and there are many examples of ML algorithms published in the critically ill adult ( 4 – 8 ) and pediatric ( 9 – 16 ) literature that robustly predict morbidities and mortality.…”

Section: Introductionmentioning

confidence: 99%

Ignorance Isn't Bliss: We Must Close the Machine Learning Knowledge Gap in Pediatric Critical Care

et al. 2022

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Pediatric intensivists are bombarded with more patient data than ever before. Integration and interpretation of data from patient monitors and the electronic health record (EHR) can be cognitively expensive in a manner that results in delayed or suboptimal medical decision making and patient harm. Machine learning (ML) can be used to facilitate insights from healthcare data and has been successfully applied to pediatric critical care data with that intent. However, many pediatric critical care medicine (PCCM) trainees and clinicians lack an understanding of foundational ML principles. This presents a major problem for the field. We outline the reasons why in this perspective and provide a roadmap for competency-based ML education for PCCM trainees and other stakeholders.

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Continuous Prediction of Mortality in the PICU: A Recurrent Neural Network Model in a Single-Center Dataset*

Abstract: Supplemental Digital Content is available in the text.

Cited by 38 publications

References 29 publications

The use of machine learning and artificial intelligence within pediatric critical care

The use of machine learning and artificial intelligence within pediatric critical care

Machine Learning-Based Systems for the Anticipation of Adverse Events After Pediatric Cardiac Surgery

Ignorance Isn't Bliss: We Must Close the Machine Learning Knowledge Gap in Pediatric Critical Care

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