Literature DB >> 29752973

Big Data and Data Science in Critical Care.

L Nelson Sanchez-Pinto1, Yuan Luo2, Matthew M Churpek3.   

Abstract

The digitalization of the health-care system has resulted in a deluge of clinical big data and has prompted the rapid growth of data science in medicine. Data science, which is the field of study dedicated to the principled extraction of knowledge from complex data, is particularly relevant in the critical care setting. The availability of large amounts of data in the ICU, the need for better evidence-based care, and the complexity of critical illness makes the use of data science techniques and data-driven research particularly appealing to intensivists. Despite the increasing number of studies and publications in the field, thus far there have been few examples of data science projects that have resulted in successful implementations of data-driven systems in the ICU. However, given the expected growth in the field, intensivists should be familiar with the opportunities and challenges of big data and data science. The present article reviews the definitions, types of algorithms, applications, challenges, and future of big data and data science in critical care.
Copyright © 2018 American College of Chest Physicians. Published by Elsevier Inc. All rights reserved.

Keywords:  big data; critical care; data science; machine learning; prediction models

Mesh:

Year:  2018        PMID: 29752973      PMCID: PMC6224705          DOI: 10.1016/j.chest.2018.04.037

Source DB:  PubMed          Journal:  Chest        ISSN: 0012-3692            Impact factor:   9.410


  48 in total

1.  The inevitable application of big data to health care.

Authors:  Travis B Murdoch; Allan S Detsky
Journal:  JAMA       Date:  2013-04-03       Impact factor: 56.272

2.  Can You Read Me Now? Unlocking Narrative Data with Natural Language Processing.

Authors:  Michael W Sjoding; Vincent X Liu
Journal:  Ann Am Thorac Soc       Date:  2016-09

3.  Identifying Distinct Subgroups of ICU Patients: A Machine Learning Approach.

Authors:  Kelly C Vranas; Jeffrey K Jopling; Timothy E Sweeney; Meghan C Ramsey; Arnold S Milstein; Christopher G Slatore; Gabriel J Escobar; Vincent X Liu
Journal:  Crit Care Med       Date:  2017-10       Impact factor: 7.598

4.  Robust monitoring of hypovolemia in intensive care patients using photoplethysmogram signals.

Authors:  Alexander Roederer; James Weimer; Joseph DiMartino; Jacob Gutsche
Journal:  Conf Proc IEEE Eng Med Biol Soc       Date:  2015-08

5.  APACHE-acute physiology and chronic health evaluation: a physiologically based classification system.

Authors:  W A Knaus; J E Zimmerman; D P Wagner; E A Draper; D E Lawrence
Journal:  Crit Care Med       Date:  1981-08       Impact factor: 7.598

6.  Multicenter development and validation of a risk stratification tool for ward patients.

Authors:  Matthew M Churpek; Trevor C Yuen; Christopher Winslow; Ari A Robicsek; David O Meltzer; Robert D Gibbons; Dana P Edelson
Journal:  Am J Respir Crit Care Med       Date:  2014-09-15       Impact factor: 21.405

7.  Multicenter Comparison of Machine Learning Methods and Conventional Regression for Predicting Clinical Deterioration on the Wards.

Authors:  Matthew M Churpek; Trevor C Yuen; Christopher Winslow; David O Meltzer; Michael W Kattan; Dana P Edelson
Journal:  Crit Care Med       Date:  2016-02       Impact factor: 7.598

8.  Big data in health care: using analytics to identify and manage high-risk and high-cost patients.

Authors:  David W Bates; Suchi Saria; Lucila Ohno-Machado; Anand Shah; Gabriel Escobar
Journal:  Health Aff (Millwood)       Date:  2014-07       Impact factor: 6.301

9.  A targeted real-time early warning score (TREWScore) for septic shock.

Authors:  Katharine E Henry; David N Hager; Peter J Pronovost; Suchi Saria
Journal:  Sci Transl Med       Date:  2015-08-05       Impact factor: 17.956

10.  Machine Learning and Decision Support in Critical Care.

Authors:  Alistair E W Johnson; Mohammad M Ghassemi; Shamim Nemati; Katherine E Niehaus; David A Clifton; Gari D Clifford
Journal:  Proc IEEE Inst Electr Electron Eng       Date:  2016-01-25       Impact factor: 10.961

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  45 in total

1.  Application of Machine Learning in Intensive Care Unit (ICU) Settings Using MIMIC Dataset: Systematic Review.

Authors:  Mahanazuddin Syed; Shorabuddin Syed; Kevin Sexton; Hafsa Bareen Syeda; Maryam Garza; Meredith Zozus; Farhanuddin Syed; Salma Begum; Abdullah Usama Syed; Joseph Sanford; Fred Prior
Journal:  Informatics (MDPI)       Date:  2021-03-03

Review 2.  Gut microbiome, big data and machine learning to promote precision medicine for cancer.

Authors:  Giovanni Cammarota; Gianluca Ianiro; Anna Ahern; Carmine Carbone; Andriy Temko; Marcus J Claesson; Antonio Gasbarrini; Giampaolo Tortora
Journal:  Nat Rev Gastroenterol Hepatol       Date:  2020-07-09       Impact factor: 46.802

3.  Reporting of demographic data and representativeness in machine learning models using electronic health records.

Authors:  Selen Bozkurt; Eli M Cahan; Martin G Seneviratne; Ran Sun; Juan A Lossio-Ventura; John P A Ioannidis; Tina Hernandez-Boussard
Journal:  J Am Med Inform Assoc       Date:  2020-12-09       Impact factor: 4.497

4.  Using Machine Learning to Predict Hyperchloremia in Critically Ill Patients.

Authors:  Pete Yeh; Yiheng Pan; L Nelson Sanchez-Pinto; Yuan Luo
Journal:  Proceedings (IEEE Int Conf Bioinformatics Biomed)       Date:  2020-02-06

5.  A High-Fidelity Model to Predict Length-of-Stay in the Neonatal Intensive Care Unit (NICU).

Authors:  Kanix Wang; Walid Hussain; John R Birge; Michael D Schreiber; Daniel Adelman
Journal:  INFORMS J Comput       Date:  2021-08-30       Impact factor: 3.288

Review 6.  Artificial intelligence-based clinical decision support in pediatrics.

Authors:  Sriram Ramgopal; L Nelson Sanchez-Pinto; Christopher M Horvat; Michael S Carroll; Yuan Luo; Todd A Florin
Journal:  Pediatr Res       Date:  2022-07-29       Impact factor: 3.953

7.  Three Data-Driven Phenotypes of Multiple Organ Dysfunction Syndrome Preserved from Early Childhood to Middle Adulthood.

Authors:  Jiancheng Ye; L Nelson Sanchez-Pinto
Journal:  AMIA Annu Symp Proc       Date:  2021-01-25

Review 8.  Neurocritical Care: Bench to Bedside (Eds. Claude Hemphill, Michael James) Integrating and Using Big Data in Neurocritical Care.

Authors:  Brandon Foreman
Journal:  Neurotherapeutics       Date:  2020-04       Impact factor: 7.620

Review 9.  Data Science for Child Health.

Authors:  Tellen D Bennett; Tiffany J Callahan; James A Feinstein; Debashis Ghosh; Saquib A Lakhani; Michael C Spaeder; Stanley J Szefler; Michael G Kahn
Journal:  J Pediatr       Date:  2019-01-25       Impact factor: 4.406

10.  Impact of Different Approaches to Preparing Notes for Analysis With Natural Language Processing on the Performance of Prediction Models in Intensive Care.

Authors:  Malini Mahendra; Yanting Luo; Hunter Mills; Gundolf Schenk; Atul J Butte; R Adams Dudley
Journal:  Crit Care Explor       Date:  2021-06-11
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