Abstract
Background: Deep Phenotyping is the precise and comprehensive analysis of phenotypic features in which the individual components of the phenotype are observed and described. In UK mental health clinical practice, most clinically relevant information is recorded as free text in the Electronic Health Record, and offers a granularity of information beyond what is expressed in most medical knowledge bases. The SNOMED CT nomenclature potentially offers the means to model such information at scale, yet given a sufficiently large body of clinical text collected over many years, it is difficult to identify the language that clinicians favour to express concepts. Methods: By utilising a large corpus of healthcare data, we sought to make use of semantic modelling and clustering techniques to represent the relationship between the clinical vocabulary of internationally recognised SMI symptoms and the preferred language used by clinicians within a care setting. We explore how such models can be used for discovering novel vocabulary relevant to the task of phenotyping Serious Mental Illness (SMI) with only a small amount of prior knowledge. Results: 20 403 terms were derived and curated via a two stage methodology. The list was reduced to 557 putative concepts based on eliminating redundant information content. These were then organised into 9 distinct categories pertaining to different aspects of psychiatric assessment. 235 concepts were found to be expressions of putative clinical significance. Of these, 53 were identified having novel synonymy with existing SNOMED CT concepts. 106 had no mapping to SNOMED CT. Conclusions: We demonstrate a scalable approach to discovering new concepts of SMI symptomatology based on real-world clinical observation. Such approaches may offer the opportunity to consider broader manifestations of SMI symptomatology than is typically assessed via current diagnostic frameworks, and create the potential for enhancing nomenclatures such as SNOMED CT based on real-world expressions.
Highlights
The dramatic decrease of genetic sequencing costs, coupled with the growth of our understanding of the molecular basis of diseases, has led to the identification of increasingly granular subsets of disease populations that were once thought of as homogenous groups
Precision medicine has arisen in response to the fact that the real-world application of many treatments have a lower efficacy and a differential safety profile compared to clinical trials, most likely due to genetic and environmental differences in the disease population
A complete description of the structure and challenges of SNOMED CT are beyond the scope of this paper, we describe how aspects of these problems manifest themselves in accordance with the task of phenotyping serious mental illness (SMI) from a real-world Electronic Health Record (EHR) system
Summary
The dramatic decrease of genetic sequencing costs, coupled with the growth of our understanding of the molecular basis of diseases, has led to the identification of increasingly granular subsets of disease populations that were once thought of as homogenous groups. Precision medicine seeks to obtain deeper genotypic and phenotypic knowledge of the disease population, in order to offer tailored care plans with evidence-based outcomes. Methods: By utilising a large corpus of healthcare data, we sought to make use of semantic modelling and clustering techniques to represent the relationship between the clinical vocabulary of internationally recognised SMI symptoms and the preferred language used by clinicians within a care setting. We explore how such models can be used for discovering novel vocabulary relevant to the task of phenotyping Serious Mental Illness (SMI) with only a small amount of prior knowledge. Conclusions: We demonstrate a scalable approach to discovering new concepts of SMI symptomatology based on real-world clinical version 2
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
