Phenomics Exchange for Research and Diagnostics, published by HL7 International / Clinical Interoperability Council. This guide is not an authorized publication; it is the continuous build for version 0.1.0 built by the FHIR (HL7® FHIR® Standard) CI Build. This version is based on the current content of https://github.com/HL7/phenomics-exchange-ig/ and changes regularly. See the Directory of published versions

Deep phenotyping

Deep phenotyping

The word phenotype is used with many different meanings. In biology, the most widely accepted definition of phenotype is the observable traits of an organism. In medical contexts, however, the word phenotype is more often used to refer to some deviation from normal morphology, physiology, or behavior.

Phenotype in biology and medicine

In medical contexts, the word phenotype is used to refer to some deviation from normal morphology, physiology, or behavior. The analysis of phenotype plays a key role in clinical practice and medical research, and yet phenotypic descriptions in clinical notes and medical publications are often imprecise. Diseases can be defined by many things including their etiology, response to treatment, and by the set of phenotypic features that tend to occur in the disease. For instance, patients with the common cold tend to have the phenotypic features of fever, headachce, runny nose, and malaise. The word phenotype is used to denote different things in medical contexts, including phenotypic features, traits, or disease entities.

Deep phenotyping

Deep phenotyping can be defined as the precise and comprehensive analysis of phenotypic abnormalities in which the individual components of the phenotype are observed and described, generally in such a way as to be computationally accessible. Deep phenotyping has become a standard approach for computational support of the differential diagnostic process in rare disease, where algorithms typically take sets of Human Phenotype Ontology (HPO) terms corresponding to the signs, symptoms, and laboratory and imaging abnormalities observed in a patient and compare them to computational models of disease using a number of different algorithms (reviewed in Köhler et al, 2021).