PheKB: a catalog and workflow for creating electronic phenotype algorithms for transportability

By Jacqueline Kirby, Peter Speltz, L. Rasmussen, Melissa A. Basford, Omri Gottesman, P. Peissig, J. Pacheco, G. Tromp, Jyotishman Pathak, D. Carrell and 8 more. J. Am. Medical Informatics Assoc., 2016

347 citations recorded by the source corpus.

Subfields: Genetics, Health Information Management, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/70a51768cb897c2df7dbf7b6917c226cab4af0de

How researchers see this paper

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

In Health Information Management

1. Michael S. Steinbach
2. Vipin Kumar
3. György J. Simon
4. Pranjul Yadav

In Molecular Biology

1. Michael S. Steinbach
2. Vipin Kumar
3. Franck Dernoncourt
4. Sebastian Gehrmann

Citations in the corpus

• Comparing Rule-Based and Deep Learning Models for Patient Phenotyping (2017, background): “A different and more involved approach is to develop a rule-based algorithm for each condition.(20) Due to the tedious and laborious task required of clinicians to build a generalizable model for patient phenotyping, models for automated classification using NLP are rarely developed outside of the r”
• Comparing deep learning and concept extraction based methods for patient phenotyping from clinical narratives (2018, neutral): “A different approach is to develop a fully rule-based algorithm for each condition [21].”
• Mining Electronic Health Records: A Survey (2017, background): “Phenotyping algorithms characterize the disease in terms of patient characteristics observable from the EHR data and classify patients as likely having the disease, likely not having the disease, or disease status is uncertain [Kirby et al. 2016].”