helpful for identifying connections that heretofore had not been identified and thus can be used as a predic-tive model to enable disease screening, early detection, and intervention.

Demonstrating this ability to identify relationships, the cosine distances in the github.com link show that the term “hypertension” is most closely related to “hyperlipidemia,” which, according to the American Heart Association, means “high levels of fat particles (lipids) in the blood.” Considering that this is a very common condition, estimated to afflict 31 million Americans, it might be expected. The terms “benign” and “ essential” are also close, as is the diagnostic code “4011,” or “icd4011,” which, in the ICD9 code, means “benign essential hypertension.” Hypertension is also, as expected, closely related to “obesity” and “mellitus” (diabetes), hardening arteries (“ atherosclerosis”), acid reflux (“gerd”), and high cholesterol (“hypercholesterolemia”).

However, hypertension is also seman-
ed of suffering from congestive heart
failure in 2013 and 2014. IBX removed
associated patient identifiers, demo-
graphics, and cost data. The IBX Privacy
office and Drexel University medical
institutional review board (IRB) both
approved the research protocol in ad-
vance. The medical records consisted
of the text portions of the medical re-
cord in one file and the ICD medical
codes in another file. An artificial pa-
tient ID key replaced the actual patient
ID in each medical report and each ICD
list of codes. The medical reports were
combined by that patient’s ID.

We analyzed the data as is. We did not correct the data for alternative equivalent spellings (such as “ catheterization” and “catheterisation”). Nor did we correct the data for obvious spelling mistakes and OCR errors (such as correcting “cardioverterdefibrillator” to “cardioverter-defibrillator”). This was done deliberately so the power of LSA could be shown even when run on untreated raw data. This was important because manually correcting medical reports is both costly and prone to introducing additional error. Manually checking these words revealed them to be mostly misspellings.

Analysis

We created the TDM after all the words were cast as lower case and punctuations and the standard set of stop words removed. Numbers were not removed from the raw data as they could have represented ICD codes. We then subjected the TDM to a TF-IDF transformation before a SVD was run on it, retaining 100 dimensions. There are no standard rules of thumb for how many dimensions to retain because dimensionality depends on context and corpora. 13 Adding more SVD dimensions inevitably results in more nuance and variance, as well as more noise.

Knowing the data concerned con-
gestive heart failure, we identified the
closest neighbor terms to “cardiac”
and “hypertension” after creating the
semantic space. The cosine distances
are listed in the github.com link men-
tioned earlier, omitting terms that ap-
peared in fewer than four patient re-
cords, as well as in strings with 10 or
more numeric digits (such as phone
numbers). Figure 1 and Figure 2 show
the heat-map clustering for the terms
“hypertension” and “cardiac,” respec-
tively, and Figure 3 outlines the LSA
process. It is important to emphasize
that LSA also reveals indirect associa-
tions among terms (such as when one
term is related to another only through
a third term), a key advantage of LSA
over manual inspection.

A researcher might correctly associate terms that appear together but could miss those related only indirectly. That is beside the obvious advantage of LSA in that the analysis can be done semi-automatically and on very large corpora quickly and might otherwise require an unrealistic investment of time if done manually. Moreover, and crucially important, LSA reveals not only that terms are related but also the degree of that relationship compared to other relationships as a set of ordinal cosine distance measures. These distances can be used for other analyses (such as clustering terms to determine structures within the text or to compare documents). The ability to run other analyses could be particularly

Figure 1. Clustering of the 40 terms and ICD-9-CM codes closest to “hypertension.”