Leveraging text data for causal inference using electronic health
  records
Reagan Mozer Aaron R. Kaufman Leo A. Celi Luke Miratrix
Text is a ubiquitous component of medical data, containing valuable
information about patient characteristics and care that are often missing from
structured chart data. Despite this richness, it is rarely used in clinical
research, owing partly to its complexity. Using a large database of patient
records and treatment histories accompanied by extensive notes by attendant
physicians and nurses, we show how text data can be used to support causal
inference with electronic health data in all stages, from conception and design
to analysis and interpretation, with minimal additional effort. We focus on
studies using matching for causal inference. We augment a classic matching
analysis by incorporating text in three ways: by using text to supplement a
multiple imputation procedure, we improve the fidelity of imputed values to
handle missing data; by incorporating text in the matching stage, we strengthen
the plausibility of the matching procedure; and by conditioning on text, we can
estimate easily interpretable text-based heterogeneous treatment effects that
may be stronger than those found across categories of structured covariates.
Using these techniques, we hope to expand the scope of secondary analysis of
clinical data to domains where quantitative data is of poor quality or
nonexistent, but where text is available, such as in developing countries.