1. 7. Two senses of the term “big” are often conflated. The sense of “big data” where, for example, Google and Facebook have enormous datasets, might more accurately be called “tall, narrow data,” wherein there are many independent observations of a few features. Put in terms of dimensionality and sample size, from earlier in the paper, there are many independent samples (“n”) for relatively few dimensions of feature space. This “tall narrow data” might be more correctly called “large n, low (or moderate) dimensionality data.” Medical data, especially at the molecular level, might better be called “short wide data,” with relatively few independent observations over a very large number of features, or, in the above parlance: “small n, high dimensionality data.” The “big data” problems that have seen success are the tall narrow ones. The short wide ones remain out of reach of current technology.
2. 11. Sweetnam et al. note that TRs should be captured not only for the recommended treatments (tests, etc.), but also for those actions that are considered but rejected, because they are incorrect, undesirable, or infeasible. These “contra treatment rationales” (“contra-TRs”) can carry as much, or in some cases more, information than the rationale supporting the final recommendation; often the final recommendation is a safe or possible choice, whereas physicians might like to do something that may be more effective if practical barriers, such as cost or side effects, could be surmounted. Contra TRs may also represent new treatment hypotheses, possibly worthy of testing.
3. The fiction of function
4. 15. Id.
5. 13. Hey, S.P. and Kesselheim, A. S. , “Countering Imprecision in Precision Medicine,” Science, July 29, 2016.