Text Mining for Precision Medicine: Bringing Structure to EHRs and Biomedical Literature to Understand Genes and Health

Abstract

The key question of precision medicine is whether it is possible to find clinically actionable granularity in diagnosing disease and classifying patient risk. The advent of next-generation sequencing and the widespread adoption of electronic health records (EHRs) have provided clinicians and researchers a wealth of data and made possible the precise characterization of individual patient genotypes and phenotypes. Unstructured text-found in biomedical publications and clinical notes-is an important component of genotype and phenotype knowledge. Publications in the biomedical literature provide essential information for interpreting genetic data. Likewise, clinical notes contain the richest source of phenotype information in EHRs. Text mining can render these texts computationally accessible and support information extraction and hypothesis generation. This chapter reviews the mechanics of text mining in precision medicine and discusses several specific use cases, including database curation for personalized cancer medicine, patient outcome prediction from EHR-derived cohorts, and pharmacogenomic research. Taken as a whole, these use cases demonstrate how text mining enables effective utilization of existing knowledge sources and thus promotes increased value for patients and healthcare systems. Text mining is an indispensable tool for translating genotype-phenotype data into effective clinical care that will undoubtedly play an important role in the eventual realization of precision medicine.

Keywords: Biomedical literature; Cancer; Database curation; EHR; Genotype; NLP; Outcome prediction; Pharmacogenomics; Phenotype; Precision medicine; Text mining.

Figure 1

The structure of this chapter reflects the two core functions of text mining and the two foremost text sources related to precision medicine. Section 1 discusses how text mining published literature can facilitate curation of genotype-phenotype databases for support of personalized cancer medicine. Section 2 discusses how text mining is useful in defining patient phenotypes from EHRs. Section 3 is about using text mining of both text sources for hypothesis generation in pharmacogenomics.

Figure 2

Text mining brings unstructured information into focus to characterize genotypes and phenotypes in precision medicine.

Figure 3

Genotype data permits incredibly deep classification of individuals. The biomedical literature contains a wealth of information regarding how to clinically interpret genetic knowledge. Text mining can facilitate expert curation of this information into genotype-phenotype databases.

Figure 4

This abstract includes examples of each of the five bio-entities that PubTator identifies. Note the correct identification of mentions to non-small cell lung cancer regardless of whether the text uses the full term or its abbreviation, NSCLC. Likewise, PubTator correctly interprets the term “patients” as a reference to a species, Homo sapiens. Although this abstract uses protein-level nomenclature to describe gene variants (e.g. “XRCC1 Arg399Gln”), the authors distinguish genotypes with nucleotides rather than amino acids (e.g. “the XRCC1 399A/A genotype”). This variability is an example of the challenges inherent to named entity recognition of gene mutations.

Figure 5

EHRs are rich sources of phenotype information. Algorithms to extract phenotypes commonly incorporate text mining of clinical notes as well as billing codes and medications. In contrast to the deeply individual nature of genotype information, phenotype algorithms generate clinical insights by first looking broadly at aggregated populations of people with similar conditions and known health outcomes.

Figure 6

The Veterans Information Systems and Technology Architecture (VISTA) is the most widely used EHR in the United States. Like most EHRs it contains structured data and unstructured text.