Kai Wang, PhD


Kai Wang, PhD
Associate Professor, Bioinfomatics
Children's Hospital of Philadelphia Research Institute



Kai Wang, PhD, faculty member with Children's Hospital of Philadelphia's (CHOP) Department of Biomedical and Health Informatics (DBHi) and the Raymond G. Perelman Center for Cellular and Molecular Therapeutics developed a new software tool that rapidly extracts phenotype information from electronic health records (EHRs) to facilitate genetic diagnoses. The tool, EHR-Phenolyzer, identifies relevant information from a patient’s medical history narrative in EHRs and translates the data into standardized terms that the tool then correlates with genes that underlie often-puzzling genetic diseases.

The EHR-Phenolyzer evolved from an earlier computational tool, Phenolyzer, which Wang developed at the University of Southern California. Both draw on phenotypes — the observable physical manifestations of disease — to help identify gene mutations that give rise to patients’ conditions. However, Phenolyzer requires clinical experts to manually input phenotypic data in specific formats. The EHR-Phenolyzer automates that process, extracting clinically relevant information from the text of a narrative patient history such as that written by a genetic counselor or clinical geneticist. To draw out that clinical information, the researchers used natural language processing, a computer science approach long used in analyzing literature and historical texts, but only recently applied to medical genetics.

The EHR-Phenolyzer then translates extracted descriptors into standardized terminology, called Human Phenotype Ontology, and matches those terms to candidate causal genes, prioritized by how strongly the genes correlate with a patient’s phenotype. The overall goal, said Wang, is to expedite and improve genetic diagnoses by efficiently bridging patient data in health records to the constantly growing mass of genomic data.

Working with the engineering team at DBHi, Wang and colleagues have begun a pilot project at CHOP to evaluate EHR-Phenolyzer using the hospital’s own EHR system. They have established an internal web server within CHOP to facilitate clinicians in using the tool via a web interface.

Jung Hoon Son et al, "Deep Phenotyping on Electronic Health Records Facilitates Genetic Diagnosis by Clinical Exomes," American Journal of Human Genetics, published online June 28, 2018.

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