Berlanga, Rafael

Houlden, Henry

Jiménez-Ruiz, Ernesto

Jonvik, Hallgeir

Kudama, Shahad

Milward, Adam

Morris, Huw

Ryten, Mina

Saklatvala, Jake

Simpson, Michael A.

Wood, Nicholas

King's Research Portal

                       King’s Research Portal  Document VersionPublisher's PDF, also known as Version of recordLink to publication record in King's Research PortalCitation for published version (APA):Kudama, S., Berlanga, R., Houlden, H., Jiménez-Ruiz, E., Jonvik, H., Milward, A., Morris, H., Ryten, M.,Saklatvala, J., Simpson, M. A., & Wood, N. (2015). Towards enabling the semantic access of phenotypicinformation in clinical letters. CEUR Workshop Proceedings, 1546, 177-178.Citing this paperPlease note that where the full-text provided on King's Research Portal is the Author Accepted Manuscript or Post-Print version this maydiffer from the final Published version. If citing, it is advised that you check and use the publisher's definitive version for pagination,volume/issue, and date of publication details. And where the final published version is provided on the Research Portal, if citing you areagain advised to check the publisher's website for any subsequent corrections.General rightsCopyright and moral rights for the publications made accessible in the Research Portal are retained by the authors and/or other copyrightowners and it is a condition of accessing publications that users recognize and abide by the legal requirements associated with these rights.•Users may download and print one copy of any publication from the Research Portal for the purpose of private study or research.•You may not further distribute the material or use it for any profit-making activity or commercial gain•You may freely distribute the URL identifying the publication in the Research PortalTake down policyIf you believe that this document breaches copyright please contact librarypure@kcl.ac.uk providing details, and we will remove access tothe work immediately and investigate your claim.Download date: 08. Oct. 2023Towards Enabling the Semantic Access of PhenotypicInformation in Clinical LettersShahad Kudama1, Rafael Berlanga1, Henry Houlden2 Ernesto Jiménez-Ruiz3,Hallgeir Jonvik2, Adam Milward4, Huw Morris2, Mina Ryten4, Jake Saklatvala5,Michael A. Simpson5, and Nicholas Wood21Universitat Jaume I, Spain 2University College London, UK 3University of Oxford, UK4Genomics England, UK 5King’s College London, UK1 MotivationOver the past 3 years there has been a massive growth in genetic testing both in termsof the scope of testing and the numbers of individuals offered genetic testing. Targetedsequencing of small genomic regions has been replaced by panel testing, whole exomesequencing and most recently whole genome sequencing [4]. Furthermore, genetic test-ing in research, but also clinical settings has extended beyond small numbers of selectedindividuals with very rare, highly-defined disorders to cover larger populations.While this information presents new clinical opportunities and opens the way fordevelopment of novel therapies, it also presents major challenges. For clinicians, re-liable identification of disease-associated genetic variants from amongst the broaderbackground of variants present in all human genomes that are rare, but not actuallypathogenic, is a concern. It is likely that for many rare genetic disorders obtaining clar-ity will require a worldwide effort and so the ability to capture and share key clinicalinformation, as well as genetic information, is becoming increasingly important.However, at present there are major challenges with regard to the collection andstorage of clinical information, particularly in the context of rare genetic disorders. Theprocess of studying a patient with a possible rare genetic disorder typically involvesmany different clinical specialists with no “standard” patient route. During this process,it is very common to refer from one specialist to another in order to obtain a range ofopinions and access different tests. The output of this process is usually clinical let-ters which are used both to document the patient’s progress and communicate findingsbetween specialists (e.g. patient history, examination findings, investigation results andclinical impression).Since clinical letters are a key source of knowledge, their proper annotation andstorage would enable access to the information they contain in a systematic way.Currently, the creation and processing of clinical letters requires the following steps:1. Letters are dictated using a speech recognition system by the clinician.2. The recording is uploaded to a server.3. The voice data is transcribed using another application.4. The text is downloaded and checked by qualified personnel.5. The letter is tagged manually by a specialist responsible for reading and annotatingthe interesting terms.This final part of the process requires input from a qualified medical professionaland is often time-consuming. This has lead to the relatively limited use of clinical lettersand consequently the loss of potentially important information. Thus, suitable softwaresupport is required to assist the clinician in the process of annotating and processingclinic letters to make this a part of "normal" working practice.Fig. 1. PHENOTAG main interface2 Proposed solutionIn this paper we introduce PHENOTAG, a prototype software aimed at allowing the an-notation and storage of phenotypic information from clinical letters with the aim of im-proving the accuracy of genetic testing. PHENOTAG has the added innovation of allow-ing specialists to visualise the information content of their letters and potentially checkthe quality of annotation within their normal workflow. Figure 1 shows an overviewof PHENOTAG’s interface. The annotator underlying PHENOTAG is currently based onthe approaches presented in [2, 5] and it can be used in conjunction with a series ofcoordinated vocabularies (e.g., [3]) making special emphasis in HPO concepts [1].We envisage the following uses for this software:1. The annotation and storage of HPO terms contained within clinic letters for thepurposes of populating predefined disease-specific data models for a wide range ofdiseases (of the kind being developed by the 100,000 Genomes project).2. The annotation and storage of HPO/other pre-defined terms contained within clinicletters for patients consented for research into the genetics of a single disorder (e.g.Parkinson’s Disease).3. The annotation and storage of HPO/other predefined terms contained within clinicletters in order to assess the information content and quality of clinical documenta-tion within a clinical genetics service.References1. The Human Phenotype Ontology project: linking molecular biology and disease through phe-notype data. Nucleic Acids Research 42(D1), D966–D974 (Jan 2014)2. Berlanga, R., Nebot, V., Jiménez, E.: Semantic annotation of biomedical texts through conceptretrieval. Procesamiento del Lenguaje Natural 45, 247–250 (2010)3. Jiménez-Ruiz, E., Grau, B.C., Llavori, R.B., Rebholz-Schuhmann, D.: First steps in the logic-based assessment of post-composed phenotypic descriptions. In: SWAT4LS (2010)4. Katsanis, S.H., Katsanis, N.: Molecular genetic testing and the future of clinical genomics.Nat Rev Genet 14(6), 415–426 (Jun 2013)5. Nebot, V., Berlanga, R.: Exploiting semantic annotations for open information extraction: anexperience in the biomedical domain. Knowl. Inf. Syst. 38(2), 365–389 (2014)

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Towards enabling the semantic access of phenotypic information in clinical letters

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Towards enabling the semantic access of phenotypic information in clinical letters

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