Big Data and Data Science in Critical Care

Sanchez-Pinto, L. Nelson; Luo, Yuan; Churpek, Matthew M.

doi:10.1016/j.chest.2018.04.037

Cited by 219 publications

(186 citation statements)

References 52 publications

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“…: 54). A recent publication indicates that the early definitions of these two concepts continue to be acceptable and, therefore, can be used in our study (Sanchez-Pinto et al 2018).…”

Section: Literature Reviewmentioning

confidence: 96%

The evolution of data science and big data research: A bibliometric analysis

Raban

Gordon

2020

Scientometrics

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In this study the evolution of Big Data (BD) and Data Science (DS) literatures and the relationship between the two are analyzed by bibliometric indicators that help establish the course taken by publications on these research areas before and after forming concepts. We observe a surge in BD publications along a gradual increase in DS publications. Interestingly, a new publications course emerges combining the BD and DS concepts. We evaluate the three literature streams using various bibliometric indicators including research areas and their origin, central journals, the countries producing and funding research and startup organizations, citation dynamics, dispersion and author commitment. We find that BD and DS have differing academic origin and different leading publications. Of the two terms, BD is more salient, possibly catalyzed by the strong acceptance of the pre-coordinated term by the research community, intensive citation activity, and also, we observe, by generous funding from Chinese sources. Overall, DS literature serves as a theory-base for BD publications.

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“…: 54). A recent publication indicates that the early definitions of these two concepts continue to be acceptable and, therefore, can be used in our study (Sanchez-Pinto et al 2018).…”

Section: Literature Reviewmentioning

confidence: 96%

The evolution of data science and big data research: A bibliometric analysis

Raban

Gordon

2020

Scientometrics

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“…This paper focuses on the classification method from the supervised learning. Classification techniques such as Naïve Bayesian classification, support vector machines (SVM), decision trees, logistic and linear regression, random forests and k-nearest neighbours are based on supervised learning algorithms [28][29][30].…”

Section: Related Workmentioning

confidence: 99%

Development of Rheumatoid Arthritis Classification from Electronic Image Sensor Using Ensemble Method

Sharon

Elamvazuthi

et al. 2019

Sensors

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Rheumatoid arthritis (RA) is an autoimmune illness that impacts the musculoskeletal system by causing chronic, inflammatory, and systemic effects. The disease often becomes progressive and reduces physical function, causes suffering, fatigue, and articular damage. Over a long period of time, RA causes harm to the bone and cartilage of the joints, weakens the joints’ muscles and tendons, eventually causing joint destruction. Sensors such as accelerometer, wearable sensors, and thermal infrared camera sensor are widely used to gather data for RA. In this paper, the classification of medical disorders based on RA and orthopaedics datasets using Ensemble methods are discussed. The RA dataset was gathered from the analysis of white blood cell classification using features extracted from the image of lymphocytes acquired from a digital microscope with an electronic image sensor. The orthopaedic dataset is a benchmark dataset for this study, as it posed a similar classification problem with several numerical features. Three ensemble algorithms such as bagging, Adaboost, and random subspace were used in the study. These ensemble classifiers use k-NN (K-nearest neighbours) and Random forest (RF) as the base learners of the ensemble classifiers. The data classification is accessed using holdout and 10-fold cross-validation evaluation methods. The assessment was based on set of performance measures such as precision, recall, F-measure, and receiver operating characteristic (ROC) curve. The performance was also measured based on the comparison of the overall classification accuracy rate between different ensembles classifiers and the base learners. Overall, it was found that for Dataset 1, random subspace classifier with k-NN shows the best results in terms of overall accuracy rate of 97.50% and for Dataset 2, bagging-RF shows the highest overall accuracy rate of 94.84% over different ensemble classifiers. The findings indicate that the efficiency of the base classifiers with ensemble classifier have substantially improved.

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“…El aprendizaje automático o Machine Learning se refiere a la capacidad de las computadoras de poder aprender de los datos. Esto está cimentado en el desarrollo de algoritmos que permiten que las máquinas puedan cambiar su comportamiento, basándose en el análisis de los datos nuevos que está recibiendo 17,19 . Actualmente, existen varias modalidades de este tipo de aprendizajes, entre los que se cuentan los aprendizajes supervisados, no supervisados y los profundos (deeplearning).…”

Section: Aprendizaje Automático Y Sus Modalidadesunclassified

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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Big Data and Data Science in Critical Care

Cited by 219 publications

References 52 publications

The evolution of data science and big data research: A bibliometric analysis

The evolution of data science and big data research: A bibliometric analysis

Development of Rheumatoid Arthritis Classification from Electronic Image Sensor Using Ensemble Method

Los Big Data: conceptos relacionados y algunas aplicaciones en pediatría

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