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Uncertainty in Measurement: Accuracy and Precision

Uncertainty in Measurement: Accuracy and Precision

Scientists typically make repeated measurements of a quantity to ensure the quality of their findings and to evaluate both the precision and the accuracy of their results. Measurements are said to be precise if they yield very similar results when repeated in the same manner. A measurement is considered accurate if it yields a result that is very close to the true or the accepted value. Precise values agree with each other; accurate values agree with a true value.

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Prediction Intervals

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Analyte Adsorption and Distribution

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Data integration strategies for predictive analytics in precision medicine.

¹Biomedical Informatics Center, Medical University of South Carolina, 135 Cannon Street/Suite 405K, MSC 200, Charleston, SC 29425, USA.

Personalized Medicine

|November 3, 2018

Summary

Data integration strategies are crucial for big data analytics in precision medicine. Infrastructure-as-code can enhance data consistency and interoperability, accelerating research and adoption.

Keywords:

common data models data integration infrastructure infrastructure-as-code interoperability multiomics precision medicine predictive analytics sociotechnical virtual machines

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Area of Science:

Biomedical Informatics
Health Data Science
Translational Research

Background:

Health-related data (genomic, proteomic, imaging, clinical) is rapidly growing.
Integrating diverse and large datasets presents significant challenges for predictive analytics.
Current data integration methods may struggle with the complexity of big data in precision medicine.

Purpose of the Study:

To examine data integration strategies for big data predictive analytics in precision medicine.
To discuss Infrastructure-as-Code (IaC) as a method for data integration and management.
To identify how IaC can lower barriers and improve consistency and interoperability in medical research.

Main Methods:

Literature review and conceptual analysis of data integration strategies.
Exploration of Infrastructure-as-Code principles and applications.
Discussion on the applicability of IaC in precision medicine research environments.

Main Results:

IaC methodologies offer a systematic approach to managing and integrating complex health data.
IaC can enhance data consistency, reduce errors, and improve interoperability.
Implementing IaC can streamline the data pipeline for predictive analytics.

Conclusions:

Data integration strategies, particularly IaC, are vital for advancing big data analytics in precision medicine.
IaC adoption can accelerate translational research by improving data accessibility and usability.
Healthcare organizations can leverage IaC to foster the widespread adoption of precision medicine.