Name
Abstract | ||
|---|---|---|
Public health researchers increasingly recognize that to advance their field they must grapple with the availability of increasingly large (i.e., thousands of variables) traditional population-level datasets (e.g., electronic medical records), while at the same time integrating additional large datasets (e.g., data on genomics, the microbiome, environmental exposures, socioeconomic factors, and health behaviors). Leveraging these multiple forms of data might well provide unique and unexpected discoveries about the determinants of health and wellbeing. However, we are in the very early stages of advancing the techniques required to understand and analyze big population-level data for public health research. To address this problem, this paper describes how we propose that big data can be efficiently used for public health discoveries. We show that data analytics techniques traditionally employed in public health studies are not up to the task of the data we now have in hand. Instead we present techniques adapted from big data visualization and analytics approaches used in other domains that can be used to answer important public health questions utilizing these existing and new datasets. Our findings are based on an exploratory big data case study carried out in San Diego County, California where we analyzed thousands of variables related to health to gain interesting insights on the determinants of several health outcomes, including life expectancy and anxiety disorders. These findings provide a promising early indication that public health research will benefit from the larger set of activities in contemporary big data research. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/CHASE.2017.81 | 2017 IEEE/ACM International Conference on Connected Health: Applications, Systems and Engineering Technologies (CHASE) |
Keywords | Field | DocType |
public health,big data,machine learning,data exploration,data visualization | Data science,Public health,Data visualization,Data analysis,Sociology,Knowledge management,Medical record,Life expectancy,Analytics,Big data,Social determinants of health | Conference |
ISBN | Citations | PageRank |
978-1-5090-4723-9 | 0 | 0.34 |
References | Authors | |
5 | 17 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yannis Katsis | 1 | 43 | 8.53 |
| Natasha Balac | 2 | 72 | 14.25 |
| Derek Chapman | 3 | 0 | 0.34 |
| Madhur Kapoor | 4 | 0 | 0.34 |
| Jessica Block | 5 | 12 | 3.63 |
| William G. Griswold | 6 | 3174 | 359.11 |
| Jeannie Huang | 7 | 3 | 1.54 |
| Nikos Koulouris | 8 | 0 | 0.68 |
| Massimiliano Menarini | 9 | 97 | 10.22 |
| Viswanath Nandigam | 10 | 33 | 6.19 |
| Mandy Ngo | 11 | 0 | 0.34 |
| Kian Win Ong | 12 | 199 | 11.72 |
| Yannis Papakonstantinou | 13 | 5657 | 837.56 |
| Besa Smith | 14 | 0 | 0.34 |
| Konstantinos Zarifis | 15 | 0 | 0.34 |
| Steven H. Woolf | 16 | 14 | 2.54 |
| Kevin Patrick | 17 | 84 | 9.11 |