Research Techniques Made Simple: An Introduction to Use and Analysis of Big Data in Dermatology

Abstract

Big data is a term used for any collection of datasets whose size and complexity exceeds the capabilities of traditional data processing applications. Big data repositories, including those for molecular, clinical, and epidemiology data, offer unprecedented research opportunities to help guide scientific advancement. Advantages of big data can include ease and low cost of collection, ability to approach prospectively and retrospectively, utility for hypothesis generation in addition to hypothesis testing, and the promise of precision medicine. Limitations include cost and difficulty of storing and processing data; need for advanced techniques for formatting and analysis; and concerns about accuracy, reliability, and security. We discuss sources of big data and tools for its analysis to help inform the treatment and management of dermatologic diseases.

Figure 1

The 3 V’s of big data: volume (amount of data), velocity (speed at which data is generated), and variety (number of types of data), all of which have been growing rapidly. After “The 3Vs that define Big Data,” Diya Soubra, Data Science Central, http://www.datasciencecentral.com/forum/topics/the-3vs-that-define-big-data.

Figure 2

Logarithmic scale depicting volume of big data.

Figure 3

Hypothetical example illustrating the utility of decision-tree learning for melanoma mortality prediction showing “leaves” (independent variables) such as tumor thickness, ulceration, and tumor location, and probability of survival (outcome).