HealtheDataLab - a cloud computing solution for data science and advanced analytics in healthcare with application to predicting multi-center pediatric readmissions.
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 interoperab...
| Publicado en: | BMC Medical Informatics & Decision Making Vol. 20; no. 1; pp. 1 - 13 |
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| Autores principales: | , , , , , |
| Formato: | Journal Article |
| Publicado: |
BioMed Central
6/19/2020
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=143853831&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 143853831 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 14726947 1CI0 jtl: BMC Medical Informatics & Decision Making issn: 14726947 maglogo: N pubinfo: dt: 6/19/2020 vid: 20 iid: 1 pid: 24147 pub: BioMed Central artinfo: ui: 143853831 143853831 NLM32560653 10.1186/s12911-020-01153-7 NLM32560653 143853831 ppf: 1 ppct: 12 formats: tig: atl: HealtheDataLab - a cloud computing solution for data science and advanced analytics in healthcare with application to predicting multi-center pediatric readmissions. aug: au: Ehwerhemuepha, Louis Gasperino, Gary Bischoff, Nathaniel Taraman, Sharief Chang, Anthony Feaster, William affil: CHOC Children's Hospital, 92868, Orange, CA, USA sug: subj: Readmission Infant Male Infant, Newborn Child Female Child, Preschool Health Care Delivery Solutions APACHE (Acute Physiology and Chronic Health Evaluation) Clinical Assessment Tools Scales Infant: 1-23 months Infant, Newborn: birth-1 month Child: 6-12 years Child, Preschool: 2-5 years Male Female ab: 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. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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