Ontological representation, classification and data-driven computing of phenotypes

Abstract

Background The successful determination and analysis of phenotypes plays a key role in the diagnostic process, the evaluation of risk factors and the recruitment of participants for clinical and epidemiological studies. The development of computable phenotype algorithms to solve these tasks is a challenging problem, caused by various reasons. Firstly, the term ‘phenotype’ has no generally agreed definition and its meaning depends on context. Secondly, the phenotypes are most commonly specified as non-computable descriptive documents. Recent attempts have shown that ontologies are a suitable way to handle phenotypes and that they can support clinical research and decision making. The SMITH Consortium is dedicated to rapidly establish an integrative medical informatics framework to provide physicians with the best available data and knowledge and enable innovative use of healthcare data for research and treatment optimisation. In the context of a methodological use case ‘phenotype pipeline’ (PheP), a technology to automatically generate phenotype classifications and annotations based on electronic health records (EHR) is developed. A large series of phenotype algorithms will be implemented. This implies that for each algorithm a classification scheme and its input variables have to be defined. Furthermore, a phenotype engine is required to evaluate and execute developed algorithms. Results In this article, we present a Core Ontology of Phenotypes (COP) and the software Phenotype Manager (PhenoMan), which implements a novel ontology-based method to model, classify and compute phenotypes from already available data. Our solution includes an enhanced iterative reasoning process combining classification tasks with mathematical calculations at runtime. The ontology as well as the reasoning method were successfully evaluated with selected phenotypes including SOFA score, socio-economic status, body surface area and WHO BMI classification based on available medical data. Conclusions We developed a novel ontology-based method to model phenotypes of living beings with the aim of automated phenotype reasoning based on available data. This new approach can be used in clinical context, e.g., for supporting the diagnostic process, evaluating risk factors, and recruiting appropriate participants for clinical and epidemiological studies.

The Leipzig Health Atlas—An Open Platform to Present, Archive, and Share Biomedical Data, Analyses, and Models Online

“…To model and represent phenotypes as well as for generating phenotypic queries in SDQL, we developed the Phenotype Manager (PhenoMan), an ontology-based phenotyping framework. 33 34 The PhenoMan imports the phenotype specifications (including generated queries) into the LHA using the extended REST interface of SEEK. After importing, the phenotype specification can be searched and referenced within the LHA platform.…”

“…They are used, e.g., for feasibility studies or to define study cohorts. To model and represent phenotypes as well as for generating phenotypic queries in SDQL, we developed the Phenotype Manager (PhenoMan), an ontology-based phenotyping framework 34,33 . The PhenoMan imports the phenotype specifications (including generated queries) into the LHA using the extended REST interface of SEEK.…”

Generation of a Fast Healthcare Interoperability Resources (FHIR)-based Ontology for Federated Feasibility Queries in the Context of COVID-19: Feasibility Study

“…Other existing solutions utilizing the FHIR standard for federated feasibility queries rely on computer scientists to transfer their research questions to FHIR Search, CQL, or SQL [ 24 , 25 ]. Existing FHIR-based federated feasibility query tools with a graphical UI, developed for health care professionals, rely on manual curation of search criteria [ 26 , 27 ]. Manual curation is a laborious task and can take years.…”

Pathling: analytics on FHIR

“…PhenoMan is a client application that can be used to specify phenotypes, calculate their values and write them back into a FHIR server in the form of Observation resources [31]. These Observation resources can then be queried alongside the other resources within the server using the FHIR Search API.…”