scholarly journals Leveraging national claim and hospital big data integration: a cohort study on a statin-drug interaction use case. (Preprint)

10.2196/29286 ◽  
2021 ◽  
Author(s):  
Aurélie Bannay ◽  
Mathilde Bories ◽  
Pascal Le Corre ◽  
Christine Riou ◽  
Pierre Lemordant ◽  
...  
Keyword(s):  
Big Data ◽  
Cohort Study ◽  
Drug Interaction ◽  
Data Integration ◽  
Use Case ◽  
Statin Drug

Leveraging national claim and hospital big data integration: a cohort study on a statin-drug interaction use case. (Preprint)

2021 ◽  
Author(s):  
Aurélie Bannay ◽  
Mathilde Bories ◽  
Pascal Le Corre ◽  
Christine Riou ◽  
Pierre Lemordant ◽  
...  
Keyword(s):  
Big Data ◽  
Drug Interaction ◽  
Drug Interactions ◽  
Hospital Stay ◽  
Data Warehouse ◽  
Record Linkage ◽  
Claim Data ◽  
Use Case ◽  
Academic Hospital ◽  
Statin Drug

BACKGROUND Linking different sources of medical data is a promising approach to analyse care trajectories. The INSHARE project aim was to provide the blueprint of a technological platform that facilitates integration, sharing and reuse of data from two sources: the eHOP clinical data warehouse (CDW) of Rennes academic hospital, and a dataset extracted from the French national claim data warehouse (SNDS). OBJECTIVE Using a pharmacovigilance use case based on statin consumption and statin-drug interactions, the present work demonstrates how the INSHARE platform can support big data analytical tasks in the health field. METHODS A Spark distributed cluster-computing framework was used for the record linkage procedure and all the analyses. A semi-deterministic record-linkage method based on the variables common between the chosen data sources was developed to identify all patients discharged after at least one hospital stay at Rennes academic hospital between 2015 and 2017. The use case study focused on a cohort of patients treated with statins prescribed by their general practitioner and/or during their hospital stay. RESULTS The whole process (record-linkage procedure and use case analyses) required 88 minutes. Among the 161,532 and 164,316 patients from the SNDS dataset and eHOP CDW, respectively, 159,495 patients were successfully linked (98.7% and 97.0% of patients from SNDS and eHOP CDW, respectively). Among the 16,806 patients with at least one statin delivery, 8,293 patients started the consumption before and continued during the hospital stay, 6,382 patients stopped statin consumption at hospital admission, and 2,131 patients initiated taking statins in hospital. Statin-drug interactions occurred more frequently during hospitalization than in the community (36.4% and 22.2%, respectively). Only 121 patients had the most severe level of statin-drug interaction. Hospital stay burden (length of stay and in-hospital mortality) was more severe in patients with statin-drug interactions during hospitalization. CONCLUSIONS This study demonstrates the added value of combining and re-using clinical and claim data to provide large-scale measures of drug-drug interaction prevalence and care pathways outside hospitals. It builds the path to move the current healthcare system towards a Learning Health System using knowledge generated from research on real-world health data.

scholarly journals Exploiting Semantics for Big Data Integration

AI Magazine ◽  
2015 ◽  
Vol 36 (1) ◽  
pp. 25-38 ◽  
Author(s):  
Craig A. Knoblock ◽  
Pedro Szekely
Keyword(s):  
Big Data ◽  
Data Integration ◽  
Cultural Heritage ◽  
Use Case ◽  
Heterogeneous Datasets ◽  
Dataset Size ◽  
Integrate Data

There is a great deal of interest in big data, focusing mostly on dataset size. An equally important dimension of big data is variety, where the focus is to process highly heterogeneous datasets. We describe how we use semantics to address the problem of big data variety.  We also describe Karma, a system that implements our approach and show how Karma can be applied to integrate data in the cultural heritage domain. In this use case, Karma integrates data across many museums even though the datasets from different museums are highly heterogeneous.

scholarly journals Drug‐drug interaction between warfarin and statins: A Danish cohort study

2020 ◽  
Author(s):  
Anna E. Engell ◽  
Andreas L.O. Svendsen ◽  
Bent S. Lind ◽  
Christen L. Andersen ◽  
John S. Andersen ◽  
...  
Keyword(s):  
Cohort Study ◽  
Drug Interaction ◽  
Danish Cohort ◽  
Drug Drug Interaction

Evaluation Model for Big Data Integration Tools

2019 ◽  
pp. 601-610 ◽  
Author(s):  
Ângela Alpoim ◽  
Tiago Guimarães ◽  
Filipe Portela ◽  
Manuel Filipe Santos
Keyword(s):  
Big Data ◽  
Data Integration ◽  
Evaluation Model

scholarly journals Big Data in HEP: A comprehensive use case study

2017 ◽  
Vol 898 ◽  
pp. 072012
Author(s):  
Oliver Gutsche ◽  
Matteo Cremonesi ◽  
Peter Elmer ◽  
Bo Jayatilaka ◽  
Jim Kowalkowski ◽  
...  
Keyword(s):  
Big Data ◽  
Use Case

scholarly journals Big Data Usage Patterns in the Health Care Domain: A Use Case Driven Approach Applied to the Assessment of Vaccination Benefits and Risks

2014 ◽  
Vol 23 (01) ◽  
pp. 27-35 ◽  
Author(s):  
S. de Lusignan ◽  
S-T. Liaw ◽  
C. Kuziemsky ◽  
F. Mold ◽  
P. Krause ◽  
...  
Keyword(s):  
Big Data ◽  
Data Processing ◽  
Real Time ◽  
Predictive Analytics ◽  
Regulatory Compliance ◽  
Use Cases ◽  
Data Sources ◽  
Use Case ◽  
Crowd Sourcing ◽  
Big Data Processing

Summary Background: Generally benefits and risks of vaccines can be determined from studies carried out as part of regulatory compliance, followed by surveillance of routine data; however there are some rarer and more long term events that require new methods. Big data generated by increasingly affordable personalised computing, and from pervasive computing devices is rapidly growing and low cost, high volume, cloud computing makes the processing of these data inexpensive. Objective: To describe how big data and related analytical methods might be applied to assess the benefits and risks of vaccines. Method: We reviewed the literature on the use of big data to improve health, applied to generic vaccine use cases, that illustrate benefits and risks of vaccination. We defined a use case as the interaction between a user and an information system to achieve a goal. We used flu vaccination and pre-school childhood immunisation as exemplars. Results: We reviewed three big data use cases relevant to assessing vaccine benefits and risks: (i) Big data processing using crowd-sourcing, distributed big data processing, and predictive analytics, (ii) Data integration from heterogeneous big data sources, e.g. the increasing range of devices in the “internet of things”, and (iii) Real-time monitoring for the direct monitoring of epidemics as well as vaccine effects via social media and other data sources. Conclusions: Big data raises new ethical dilemmas, though its analysis methods can bring complementary real-time capabilities for monitoring epidemics and assessing vaccine benefit-risk balance.

Declarative Big Data Analysis for High-Energy Physics: TOTEM Use Case

2019 ◽  
pp. 241-255
Author(s):  
Valentina Avati ◽  
Milosz Blaszkiewicz ◽  
Enrico Bocchi ◽  
Luca Canali ◽  
Diogo Castro ◽  
...  
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
High Energy Physics ◽  
High Energy ◽  
Big Data Analysis ◽  
Use Case ◽  
Energy Physics
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