Extracting research-quality phenotypes from electronic health records to support precision medicine

By Wei-Qi Wei, Joshua Charles Denny. 2015

228 citations recorded by the source corpus.

The convergence of two rapidly developing technologies - high-throughput genotyping and electronic health records (EHRs) - gives scientists an unprecedented opportunity to utilize routine healthcare data to accelerate genomic discovery. Institutions and healthcare systems have been building EHR-linked DNA biobanks to enable such a vision. However, the precise extraction of detailed disease and drug-response phenotype information hidden in EHRs is not an easy task. EHR-based studies have successfully replicated known associations, made new discoveries for diseases and drug response traits, rapidly contributed cases and controls to large meta-analyses, and demonstrated the potential of EHRs for broad-based phenome-wide association studies. In this review, we summarize the advantages and challenges of repurposing EHR data for genetic research. We also highlight recent notable studies and novel approaches to provide an overview of advanced EHR-based phenotyping.

Subfields: Genetics, Molecular Biology

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Genetics

1. Yuan Luo
2. Xiaoyu Li
3. Yu Deng
4. Zexian Zeng
5. Tristan Naumann

In Molecular Biology

1. Zexian Zeng
2. Tristan Naumann
3. Xiaoyu Li
4. Yu Deng
5. Yuan Luo
6. Kayvan Najarian
7. Xiaoqian Jiang
8. Jing Ma

Citations in the corpus

• Privacy-Preserving Tensor Factorization for Collaborative Health Data Analysis (2019, background): “Phenotyping can be used to assist precision medicine, speedup biomedical discovery, and improve healthcare quality [25, 29].”
• Natural Language Processing for EHR-Based Computational Phenotyping (2018, methodology): “Cohorts obtained by computational phenotyping have benefited downstream genomic studies [65], using techniques such as Genome-wide association studies (GWAS) and phenome-wide association studies (PheWAS).”
• Deep Learning in Pharmacogenomics: From Gene Regulation to Patient Stratification (2018, background): “Despite not being designed for usage in research, substantial amounts of data within EHRs, such as surrogate disease phenotypes imputed from ICD codes, have effectively been proven for use through several notable studies in GWAS and PheWAS analysis [91].”