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. 2020 Jun 19;20(1):115.
doi: 10.1186/s12911-020-01153-7.

HealtheDataLab - a cloud computing solution for data science and advanced analytics in healthcare with application to predicting multi-center pediatric readmissions

Louis Ehwerhemuepha  1   2   3 , Gary Gasperino  4 , Nathaniel Bischoff  5   6 , Sharief Taraman  5   6   7 , Anthony Chang  5   6 , William Feaster  5   6
Affiliations

HealtheDataLab - a cloud computing solution for data science and advanced analytics in healthcare with application to predicting multi-center pediatric readmissions

Louis Ehwerhemuepha et al. BMC Med Inform Decis Mak. .

Abstract

Background: There is a shortage of medical informatics and data science platforms using cloud computing on electronic medical record (EMR) data, and with computing capacity for analyzing big data. We implemented, described, and applied a cloud computing solution utilizing the fast health interoperability resources (FHIR) standardization and state-of-the-art parallel distributed computing platform for advanced analytics.

Methods: We utilized the architecture of the modern predictive analytics platform called Cerner® HealtheDataLab and described the suite of cloud computing services and Apache Projects that it relies on. We validated the platform by replicating and improving on a previous single pediatric institution study/model on readmission and developing a multi-center model of all-cause readmission for pediatric-age patients using the Cerner® Health Facts Deidentified Database (now updated and referred to as the Cerner Real World Data). We retrieved a subset of 1.4 million pediatric encounters consisting of 48 hospitals' data on pediatric encounters in the database based on a priori inclusion criteria. We built and analyzed corresponding random forest and multilayer perceptron (MLP) neural network models using HealtheDataLab.

Results: Using the HealtheDataLab platform, we developed a random forest model and multi-layer perceptron model with AUC of 0.8446 (0.8444, 0.8447) and 0.8451 (0.8449, 0.8453) respectively. We showed the distribution in model performance across hospitals and identified a set of novel variables under previous resource utilization and generic medications that may be used to improve existing readmission models.

Conclusion: Our results suggest that high performance, elastic cloud computing infrastructures such as the platform presented here can be used for the development of highly predictive models on EMR data in a secure and robust environment. This in turn can lead to new clinical insights/discoveries.

Keywords: Amazon web Services; Cloud computing; Pediatric hospital readmissions.

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Conflict of interest statement

Gary Gasperino is employed by and promote the use of HealtheDataLab for Cerner Corporation and as a result has competing interest. However, evaluation of the platform and the three use cases presented here were carried out independently by the CHOC Children’s Hospital. The decision to publish was made independently by the corresponding author’s institution.

Figures

Fig. 1
Fig. 1
The HealtheDataLab architecture
Fig. 2
Fig. 2
Area under the receiver operator characteristics of a the random forest and b the MLP models
Fig. 3
Fig. 3
Top 30 important variables by the random forest model
Fig. 4
Fig. 4
Performance distribution of model across all 48 hospitals
Fig. 5
Fig. 5
Comparison of random forest models of a single center unplanned readmission model (Model M1), a single center planned and unplanned readmission model (Model M2), and a multi-center MLP model of readmission (Model M3:MLP)
See this image and copyright information in PMC

References

    1. Thrall JH, Li X, Li Q, et al. Artificial intelligence and machine learning in radiology: opportunities, challenges, pitfalls, and criteria for success. J Am Coll Radiol. 2018;15(3):504–508. doi: 10.1016/j.jacr.2017.12.026. - DOI - PubMed
    1. Dilsizian SE, Siegel EL. Artificial intelligence in medicine and cardiac imaging: harnessing big data and advanced computing to provide personalized medical diagnosis and treatment. Curr Cardiol Rep. 2014;16(1):441. doi: 10.1007/s11886-013-0441-8. - DOI - PubMed
    1. Raghupathi W, Raghupathi V. Big data analytics in healthcare: promise and potential. Heal Inf Sci Syst. 2014;2(1):3. doi: 10.1186/2047-2501-2-3. - DOI - PMC - PubMed
    1. Mauch V, Kunze M, Hillenbrand M. High performance cloud computing. Futur Gener Comput Syst. 2013;29(6):1408–1416. doi: 10.1016/j.future.2012.03.011. - DOI
    1. Al-Aqrabi H, Liu L, Hill R, Antonopoulos N, Cloud BI. Future of business intelligence in the cloud. J Comput Syst Sci. 2015;81(1):85–96. doi: 10.1016/j.jcss.2014.06.013. - DOI