Using electronic health records to drive discovery in disease genomics

By Isaac S. Kohane. 2011

319 citations recorded by the source corpus.

Subfields: Genetics, Public Health, Environmental and Occupational Health

How researchers see this paper

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

In Genetics

In Public Health, Environmental and Occupational Health

Citations in the corpus

Natural Language Processing for EHR-Based Computational Phenotyping (2018, background): “itated the completion of genomic research in a timely and lower costmanner [66].”
Clustering Longitudinal Clinical Marker Trajectories from Electronic Health Data: Applications to Phenotyping and Endotype Discovery (2015, background): “…to improve the effectiveness of clinical trials (Gundlapalli et al. 2008), drive the design of new genome-wide association studies (Kho et al. 2011; Kohane 2011), and allow medical scientists to view related diseases through a more fine-grained lens that can lead to insights that connect their…”
Semi-supervised clinical text classification with Laplacian SVMs: an application to cancer case management (2013, background): “Automated text classification techniques extract structured information from narrative clinical text, empowering novel secondary uses of unstructured data [1,2].”
Predictive modeling of structured electronic health records for adverse drug event detection (2015, background): “Although the main function of EHRs is to archive and manage patient data efficiently - in comparison to paper-based health record systems - secondary use of EHR data is currently being widely explored for various medical research, such as disease discovery and patient stratification [12,13], among w”
Axes of a revolution: challenges and promises of big data in healthcare (2020, methodology): “…uses EHR-driven genomic research, which combines data available in the EHR and phenotypic characterizations, and enables calculation of the effect size of a genetic variant not for one disease or trait but for all diseases simultaneously, also called a ‘phenome-wide association study’ 100,101 .”
Human symptoms–disease network (2014, background): “To this date, however, clinical documentation is still highly variable and rife with errors and imprecision 54,55 .”
Mining electronic health records: towards better research applications and clinical care (2012, background): “Realizing the full potential of EHR-based recruitment of patients for genetic research requires a framework for the easy acquisition of matched DNA samples and for patient consent 4 .”
Extracting research-quality phenotypes from electronic health records to support precision medicine (2015, methodology): “While this approach provides quality phenotypes and high repeatability in the assessment of given traits, considerable challenges remain [12,13], such as slow patient accrual [14], inadequate sample size [15,16] and high cost [17].”
Using Electronic Health Records for Population Health Research: A Review of Methods and Applications (2015, background): “These efforts have been summarized elsewhere (12, 49, 66, 81, 95, 120) and are not covered in this review.”