ArticleBMC medical informatics and decision making2020
HealtheDataLab - a cloud computing solution for data science and advanced analytics in healthcare with application to predicting multi-center pediatric readmissions.
Article in BMC medical informatics and decision making, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers, 1 of them a synthesis that pooled it.
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Who cites it
29 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Applicability of predictive models for 30-day unplanned hospital readmission risk in paediatrics: a systematic review.BMJ open · 2022Pooled it
- Pediatrics 4.0: the Transformative Impacts of the Latest Industrial Revolution on Pediatrics.Health care analysis : HCA : journal of health philosophy and policy · 2026Article
- Advancing Real-World Evidence Generation: Growth and Lessons Learned from the Early Years of Cosmos.ACI open · 2026Review
- Early Prediction of Mechanical Ventilation Needs in Very Preterm Neonates Using Machine Learning.Pediatric pulmonology · 2025Article
- Validation of 30-Day Pediatric Hospital Readmission Risk Prediction Models.JAMA network open · 2025Article
- Enhancing healthcare outcome with scalable processing and predictive analytics via cloud healthcare API.Frontiers in digital health · 2025Review
- Congenital anomalies and predisposition to severe COVID-19 among pediatric patients in the United States.Pediatric research · 2024Article
- Birth Weight and Gestational Age as Modifiers of Rehospitalization after Neonatal Intensive Care Unit Admission.American journal of perinatology · 2024Article
- Chronic Cough: Characterizing and Quantifying Burden in Adults Using a Nationwide Electronic Health Records Database.Journal of healthcare informatics research · 2024Article
- A Methodology for a Scalable, Collaborative, and Resource-Efficient Platform, MERLIN, to Facilitate Healthcare AI Research.IEEE journal of biomedical and health informatics · 2023Article
- Association rule mining of real-world data: Uncovering links between race, glycemic control, lipid profiles, and suicide attempts in individuals with diabetes.Informatics in medicine unlocked · 2023Article
- Liver injury on admission linked to worse outcomes in COVID-19: an analysis of 14,138 patients.Translational gastroenterology and hepatology · 2023Article
- Systematic analysis of healthcare big data analytics for efficient care and disease diagnosing.Scientific reports · 2022Article
- Impact of diabetes status and related factors on COVID-19-associated hospitalization: A nationwide retrospective cohort study of 116,370 adults with SARS-CoV-2 infection.Diabetes research and clinical practice · 2022Article
- Cerner real-world data (CRWD) - A de-identified multicenter electronic health records database.Data in brief · 2022Article
- Association of Congenital and Acquired Cardiovascular Conditions With COVID-19 Severity Among Pediatric Patients in the US.JAMA network open · 2022Article
- Standardized Health data and Research Exchange (SHaRE): promoting a learning health system.JAMIA open · 2022Article
- Race, Ethnicity, and Insurance: the Association with Opioid Use in a Pediatric Hospital Setting.Journal of racial and ethnic health disparities · 2021Article
- Electronic Health Record Network Research in Infectious Diseases.Clinical therapeutics · 2021Review
- Long-term effects of malnutrition on severity of COVID-19.Scientific reports · 2021Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThere 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.
methodsWe 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.
resultsUsing 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.
conclusionOur 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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.