Asset Details
Do you wish to reserve the book?
Why we need a small data paradigm
Hey, we have placed the reservation for you!
By the way, why not check out events that you can attend while you pick your title.
Oops! Something went wrong.
Looks like we were not able to place the reservation. Kindly try again later.
Are you sure you want to remove the book from the shelf?
Why we need a small data paradigm
Do you wish to request the book?
Why we need a small data paradigm
Please be aware that the book you have requested cannot be checked out. If you would like to checkout this book, you can reserve another copy
We have requested the book for you!
Your request is successful and it will be processed during the Library working hours. Please check the status of your request in My Requests.
Oops! Something went wrong.
Looks like we were not able to place your request. Kindly try again later.
Journal Article
Why we need a small data paradigm
2019
Overview
Background
There is great interest in and excitement about the concept of personalized or precision medicine and, in particular, advancing this vision via various ‘big data’ efforts. While these methods are necessary, they are insufficient to achieve the full personalized medicine promise. A rigorous, complementary ‘small data’ paradigm that can function both autonomously from and in collaboration with big data is also needed. By ‘small data’ we build on Estrin’s formulation and refer to the rigorous use of data by and for a specific N-of-1 unit (i.e., a single person, clinic, hospital, healthcare system, community, city, etc.) to facilitate improved individual-level description, prediction and, ultimately, control for that specific unit.
Main body
The purpose of this piece is to articulate why a small data paradigm is needed and is valuable in itself, and to provide initial directions for future work that can advance study designs and data analytic techniques for a small data approach to precision health. Scientifically, the central value of a small data approach is that it can uniquely manage complex, dynamic, multi-causal, idiosyncratically manifesting phenomena, such as chronic diseases, in comparison to big data. Beyond this, a small data approach better aligns the goals of science and practice, which can result in more rapid agile learning with less data. There is also, feasibly, a unique pathway towards transportable knowledge from a small data approach, which is complementary to a big data approach. Future work should (1) further refine appropriate methods for a small data approach; (2) advance strategies for better integrating a small data approach into real-world practices; and (3) advance ways of actively integrating the strengths and limitations from both small and big data approaches into a unified scientific knowledge base that is linked via a robust science of causality.
Conclusion
Small data is valuable in its own right. That said, small and big data paradigms can and should be combined via a foundational science of causality. With these approaches combined, the vision of precision health can be achieved.
Publisher
BioMed Central,BioMed Central Ltd,BMC
Subject
/ Beyond Big Data to new Biomedical and Health Data Science: moving to next century precision health
/ Big data
/ Data Interpretation, Statistical
/ Datasets as Topic - standards
/ Datasets as Topic - statistics & numerical data
/ Datasets as Topic - supply & distribution
/ Debate
/ Delivery of Health Care - methods
/ Delivery of Health Care - statistics & numerical data
/ Health
/ High-Throughput Screening Assays - methods
/ High-Throughput Screening Assays - statistics & numerical data
/ Humans
/ Learning
/ Medicine
/ Precision Medicine - methods
/ Precision Medicine - statistics & numerical data
