Applying Machine Learning to Pediatric Critical Care Data*

Williams, John D.; Ghosh, Debjit; Wetzel, Randall C.

doi:10.1097/pcc.0000000000001567

Cited by 38 publications

(32 citation statements)

References 21 publications

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“…Descriptive data were presented as frequencies (percentages) for categorical variables, as means ± standard deviation ( SD ) for the normally distributed continuous variables, and as medians and interquartile ranges for the abnormally distributed continuous variables. Differences between the subgroups were analysed using a one‐way analysis of variance or the Kruskal–Wallis test, and the Chi‐square test was used for analysing categorical variables and for determining the corresponding 95% confidence intervals (Saukkoriipi et al., 2020; Williams et al., 2018). These methods were used to test whether patient classification subgroups achieved clinically significant levels.…”

Section: Methodsmentioning

confidence: 99%

Machine learning‐based patient classification system for adult patients in intensive care units: A cross‐sectional study

Chang

Fan

et al. 2021

J Nurs Manag

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Aim This study aimed to develop a patient classification system that stratifies patients admitted to the intensive care unit based on their disease severity and care needs. Background Classifying patients into homogenous groups based on clinical characteristics can optimize nursing care. However, an objective method for determining such groups remains unclear. Methods Predictors representing disease severity and nursing workload were considered. Patients were clustered into subgroups with different characteristics based on the results of a clustering algorithm. A patient classification system was developed using a partial least squares regression model. Results Data of 300 patients were analysed. Cluster analysis identified three subgroups of critically patients with different levels of clinical trajectories. Except for blood potassium levels (p = .29), the subgroups were significantly different according to disease severity and nursing workload. The predicted value ranges of the regression model for Classes A, B and C were <1.44, 1.44–2.03 and >2.03. The model was shown to have good fit and satisfactory prediction efficiency using 200 permutation tests. Conclusions Classifying patients based on disease severity and care needs enables the development of tailored nursing management for each subgroup. Implications for Nursing Management The patient classification system can help nurse managers identify homogeneous patient groups and further improve the management of critically ill patients.

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Section: Methodsmentioning

confidence: 99%

Machine learning‐based patient classification system for adult patients in intensive care units: A cross‐sectional study

Chang

Fan

et al. 2021

J Nurs Manag

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“…In a large cohort of critically ill children, k-means clustering was recently used to develop clusters with separable signatures of illness severity and mortality. 129 Convolutional neural networks, a form of deep learning, have recently been used to identify physiomarkers that predict severe sepsis in children. 130 In a cohort of patients with shunted cyanotic heart disease in the cardiac ICU, a classification algorithm built on continuous high-resolution physiologic data can detect impending deterioration events 1-2 hours early.…”

Section: Predictive Analytics Using Frequently Measured Datamentioning

confidence: 99%

Data Science for Child Health

Bennett

Callahan

Feinstein

et al. 2019

The Journal of Pediatrics

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Data science has revolutionized industry and academic fields including marketing, 1 astronomy, 2 and computer vision. 3 It has not yet impacted medicine and biomedical research to the same degree. However, many observers 4-6 believe that data science will improve the ability of health care systems to deliver personalized medicine, 7 population health, 8 and public health. 9 The US National Institutes of Health (NIH) Strategic Plan for Data Science defines it as "the interdisciplinary field of inquiry in which quantitative and analytical approaches, processes, and systems are developed and used to extract knowledge and insights from increasingly large and/or complex sets of data." 10 One interpretation of this definition is that data science is possible because of relatively cheap data storage and computing, efficient algorithms and computational tools for complex tasks such as optimizing neural network models and analyzing whole exome sequence (WES) data, and the availability of enormous amounts of data. This includes biological (eg, genomic, proteomic, and metabolomic), clinical (eg, laboratory results, digital images, text notes, and monitor signals), lifestyle (mobile sensor output, interactions with applications), and other data with which to train and evaluate models.

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“…En el ámbito de cuidados intensivos pediátricos, se han reportado varios estudios donde se han utilizado herramientas de análisis, tales como redes neuronales y aprendizajes automatizados con el fin de darle uso a los volumen y variabilidad de datos que se producen en las Unidades de Cuidado Intensivo Pediátricas. Así es como se han identificado patrones predictores de riesgo de mortalidad, como también información relevante como para el pronóstico, diagnóstico y la terapia de niños en cuidados críticos, entre otros 30,31 .…”

Section: Aplicaciones En Pediatríaunclassified

Los Big Data: conceptos relacionados y algunas aplicaciones en pediatría

Zepeda

2019

Rev Chil Pediatr

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El avance de la tecnología médica, el registro de salud electrónico (EHR, por sus siglas en inglés) y la producción compleja de datos biomoleculares están generando grandes volúmenes de información, en varios formatos y de múltiples fuentes, que se conocen como “Big Data”. El análisis integrado de estos datos ha abierto una amplia posibilidad para explorar respuestas a problemas de salud. En pediatría, se han incrementado los estudios se analizan Big Data o se utilizan las herramientas informáticas que se han desarrollado para analizar estos datos. Los propósitos de esos estudios han sido variados, por ejemplo: en la detección y prevención temprana de una amplia gama de afecciones médicas, mejoramiento de los diagnósticos, para especificar tratamientos o anticipar el resultado de alguna patología, etc. El presente documento tiene como objetivo revisar los conceptos principales involucrados en el análisis de Big Data o en las tecnologías informáticas asociadas, así como también examinar sus aplicaciones, potencialidades y limitaciones actuales. Este estudio se realizó sobre la base de una revisión bibliográfica no sistemática, centrada en el campo de la pediatría. En la selección de los ejemplos de aplicación, se consideró que eran fuentes primarias, publicadas en los últimos cinco años y con poblaciones infanto-juveniles.

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Applying Machine Learning to Pediatric Critical Care Data*

Cited by 38 publications

References 21 publications

Machine learning‐based patient classification system for adult patients in intensive care units: A cross‐sectional study

Machine learning‐based patient classification system for adult patients in intensive care units: A cross‐sectional study

Data Science for Child Health

Los Big Data: conceptos relacionados y algunas aplicaciones en pediatría

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