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.
In this study, we developed two cancer-specific machine learning classifiers for prediction of driver mutations in cancer-associated genes that were validated on canonical data sets of functionally validated mutations and applied to a large cancer genomics data set. By examining sequence, structure, and ensemble-based integrated features, we have shown that evolutionary conservation scores play a critical role in classification of cancer drivers and provide the strongest signal in the machine learning prediction. Through extensive comparative analysis with structure−functional experiments and multicenter mutational calling data from Pan Cancer Atlas studies, we have demonstrated the robustness of our models and addressed the validity of computational predictions. To address the interpretability of cancer-specific classification models and obtain novel insights about molecular signatures of driver mutations, we have complemented machine learning predictions with structure−functional analysis of cancer driver mutations in several important oncogenes and tumor suppressor genes. By examining structural and dynamic signatures of known mutational hotspots and the predicted driver mutations, we have shown that the greater flexibility of specific functional regions targeted by driver mutations in oncogenes may facilitate activating conformational changes, while loss-of-function driver mutations in tumor suppressor genes can preferentially target structurally rigid positions that mediate allosteric communications in residue interaction networks and modulate protein binding interfaces. By revealing molecular signatures of cancer driver mutations, our results highlighted limitations of the binary driver/passenger classification, suggesting that functionally relevant cancer mutations may span a continuum spectrum of driverlike effects. Based on this analysis, we propose for experimental testing a group of novel potential driver mutations that can act by altering structure, global dynamics, and allosteric interaction networks in important cancer genes.
Introduction: Since 2014, the American Academy of Pediatrics has recommended that patients over two years with diabetes mellitus (DM) receive the 23-valent pneumococcal polysaccharide vaccine (PPSV23). Methods: Retrospective chart review was initiated by a quality improvement (QI) project to determine PPSV23 administration rates for inpatients with DM at Children's Hospital of Orange County (CHOC). The QI project included education for staff and families regarding need for PPSV23 in patients with DM. Electronic medical record (EMR) order sets for DM were updated with PPSV23 vaccine. Data were collected from EMR to identify differences in subjects who were vaccinated with PPSV23 and unvaccinated from April 2015 to April 2016. Results: Before April 2015, PPSV23 was not being given to inpatients with DM. There were 199 individual subjects admitted to CHOC with DM from April 2015 to April 2016. Of those, 78 subjects (39.1%) received vaccine. Data were categorized to identify if vaccine was ordered (n = 152) or not (n = 47). Univariate logistic regression analysis performed on whether PPSV23 was ordered showed age, location (pediatric intensive care unit [PICU] vs. floor), hemoglobin A1c (HbA1c), primary DM admission, and insulin pump vs. injection usage were significant factors (P < 0.05). Multivariate logistic regression showed that those with higher HbA1c (P = 0.014), new-onset DM (P = 0.009), and those admitted for primary DM-related issues (P = 0.007) were more likely to have PPSV23 ordered. No significant subject factors identified differences in why vaccine was not administered (n = 74) once ordered. Conclusion: PPSV23 rates for pediatric inpatients with DM increased from 0% to 39% during one year following education and EMR modifications.
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