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.

使用大量患者记录和治疗历史的文本数据，可以在临床数据的全部阶段中以最小的额外工作量支持因果推断，通过三种方式利用文本数据增强经典匹配分析，改善缺失数据的模拟值的准确性、增强匹配过程的合理性，并结合文本变量来估计易于解释的基于文本的治疗效应。希望利用这些技术拓展临床数据的次级分析范围，特别是在定量数据质量差或不存在的情况下，但有文本数据可用，例如在发展中国家。

利用电子健康记录进行因果推断的文本数据挖掘