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

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

Adv Exp Med Biol. 2016;939:139-166.

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

Simmons M1, Singhal A1, Lu Z2.

Author information

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

PMID:

 

27807747

 

DOI:

 

10.1007/978-981-10-1503-8_7

[PubMed - in process]

Public Health Genomics Knowledge Base (v1.2)

 Electronic Health Records expanded expanded

Text Mining for Precision Medicine: Bringing Structure to EHRs and Biomedical Literature to Understand Genes and Health.
Simmons Michael et al. Advances in experimental medicine and biology 2016 939139-166

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Last Updated: Nov 09, 2016

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Relevant Resources

• Genetics and Genomics Competencies Center (G2C2)
• NIH Genetic Testing Registry
• NIH MedGen Database
• Online Mendelian Inheritance in Man (OMIM)
• PharmGKB