Semantic integration of clinical laboratory tests from electronic health records for deep phenotyping and biomarker discovery

Zhang, Xingmin Aaron; Yates, Amy; Vasilevsky, Nicole; Gourdine, Jean-Philippe; Callahan, Tiffany J.; Carmody, Leigh C.; Daniš, Daniel; Joachimiak, Marcin P.; Ravanmehr, Vida; Pfaff, Emily R.; Champion, James; Robasky, Kimberly; Xu, Hao; Fecho, Karamarie; Walton, Nephi; Zhu, Richard L.; Ramsdill, Justin W; Mungall, Christopher J.; Köhler, Sebastian; Haendel, Melissa; McDonald, Clement J.; Vreeman, Daniel J.; Peden, David B.; Bennett, Tellen D.; Feinstein, James A.; Martin, Blake; Stefanski, Adrianne L.; Hunter, Lawrence; Chute, Christopher G.; Robinson, Peter N.

doi:10.1038/s41746-019-0110-4

Cited by 38 publications

(30 citation statements)

References 37 publications

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“…In a study on 15,681 patients with respiratory complaints, our approach allowed us to convert the majority of the laboratory tests into HPO terms and assign an average of 57.7 unique phenotypes to each patient. A number of previously described asthma biomarkers were found to have statistically significant overrepresentation in individuals diagnosed with this disease [17]. Approaches like these are likely to be influential as means of improving portability and interoperability of ML-based phenotyping algorithms across different institutions and EHR systems.…”

Section: Ontology-based Extraction Of Structured Data From Ehrsmentioning

confidence: 97%

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Ontologies, Knowledge Representation, and Machine Learning for Translational Research: Recent Contributions

Robinson

Haendel

2020

Yearb Med Inform

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Objectives: To select, present, and summarize the most relevant papers published in 2018 and 2019 in the field of Ontologies and Knowledge Representation, with a particular focus on the intersection between Ontologies and Machine Learning. Methods: A comprehensive review of the medical informatics literature was performed to select the most interesting papers published in 2018 and 2019 and that document the utility of ontologies for computational analysis, including machine learning. Results: Fifteen articles were selected for inclusion in this survey paper. The chosen articles belong to three major themes: (i) the identification of phenotypic abnormalities in electronic health record (EHR) data using the Human Phenotype Ontology ; (ii) word and node embedding algorithms to supplement natural language processing (NLP) of EHRs and other medical texts; and (iii) hybrid ontology and NLP-based approaches to extracting structured and unstructured components of EHRs. Conclusion: Unprecedented amounts of clinically relevant data are now available for clinical and research use. Machine learning is increasingly being applied to these data sources for predictive analytics, precision medicine, and differential diagnosis. Ontologies have become an essential component of software pipelines designed to extract, code, and analyze clinical information by machine learning algorithms. The intersection of machine learning and semantics is proving to be an innovative space in clinical research.

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Section: Ontology-based Extraction Of Structured Data From Ehrsmentioning

confidence: 97%

“…In some cases, additional works are cited to provide context. A total of 15 articles were finally selected for inclusion [4][5][6][7][8][9][10][11][12][13][14][15][16][17][18].…”

Section: About the Paper Selectionmentioning

confidence: 99%

Ontologies, Knowledge Representation, and Machine Learning for Translational Research: Recent Contributions

Robinson

Haendel

2020

Yearb Med Inform

Self Cite

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“…Moreover, by looking at the patients that contribute to these clusters and their affected genes, we see that in many cases we can form novel hypotheses and suggest further phenotypes and FunSys to study. Such approaches might be combined with exciting new methods that connect laboratory test result data from patient records with HPO phenotypes [52].…”

Section: Plos Geneticsmentioning

confidence: 99%

Phenotype-genotype comorbidity analysis of patients with rare disorders provides insight into their pathological and molecular bases

et al. 2020

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Genetic and molecular analysis of rare disease is made difficult by the small numbers of affected patients. Phenotypic comorbidity analysis can help rectify this by combining information from individuals with similar phenotypes and looking for overlap in terms of shared genes and underlying functional systems. However, few studies have combined comorbidity analysis with genomic data. We present a computational approach that connects patient phenotypes based on phenotypic co-occurence and uses genomic information related to the patient mutations to assign genes to the phenotypes, which are used to detect enriched functional systems. These phenotypes are clustered using network analysis to obtain functionally coherent phenotype clusters. We applied the approach to the DECIPHER database, containing phenotypic and genomic information for thousands of patients with heterogeneous rare disorders and copy number variants. Validity was demonstrated through overlap with known diseases, co-mention within the biomedical literature, semantic similarity measures, and patient cluster membership. These connected pairs formed multiple phenotype clusters, showing functional coherence, and mapped to genes and systems involved in similar pathological processes. Examples include claudin genes from the 22q11 genomic region associated with a cluster of phenotypes related to DiGeorge syndrome and genes related to the GO term anterior/posterior pattern specification associated with abnormal development. The clusters generated can help with the diagnosis of rare diseases, by suggesting additional phenotypes for a given patient and potential underlying functional systems. Other tools to find causal genes based on phenotype were also investigated. The approach has been implemented as a workflow, named PhenCo, which can be adapted to any set of patients for which phenomic and genomic data is available. Full details of the analysis, including the clusters formed, their constituent functional systems and underlying genes are given. Code to implement the workflow is available from GitHub.

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“…A "Patient" was used as the main resource of the platform. To provide semantic interoperability, the platform supports the following FHIR R4 [35] resources as input and output data: Each service has an independent release cycle, so the public interfaces support versioning to provide consistent operation of the system.…”

Section: Clinical Modellingmentioning

confidence: 99%

“…A "Patient" was used as the main resource of the platform. To provide semantic interoperability, the platform supports the following FHIR R4 [35] resources as input and output data:…”

Section: Clinical Modellingmentioning

confidence: 99%

Experience in Developing an FHIR Medical Data Management Platform to Provide Clinical Decision Support

Semenov

Osenev

Герасимов

et al. 2019

IJERPH

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This paper is an extension of work originally presented to pHealth 2019—16th International Conference on Wearable, Micro and Nano Technologies for Personalized Health. To provide an efficient decision support, it is necessary to integrate clinical decision support systems (CDSSs) in information systems routinely operated by healthcare professionals, such as hospital information systems (HISs), or by patients deploying their personal health records (PHR). CDSSs should be able to use the semantics and the clinical context of the data imported from other systems and data repositories. A CDSS platform was developed as a set of separate microservices. In this context, we implemented the core components of a CDSS platform, namely its communication services and logical inference components. A fast healthcare interoperability resources (FHIR)-based CDSS platform addresses the ease of access to clinical decision support services by providing standard-based interfaces and workflows. This type of CDSS may be able to improve the quality of care for doctors who are using HIS without CDSS features. The HL7 FHIR interoperability standards provide a platform usable by all HISs that are FHIR enabled. The platform has been implemented and is now productive, with a rule-based engine processing around 50,000 transactions a day with more than 400 decision support models and a Bayes Engine processing around 2000 transactions a day with 128 Bayesian diagnostics models.

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Semantic integration of clinical laboratory tests from electronic health records for deep phenotyping and biomarker discovery

Cited by 38 publications

References 37 publications

Ontologies, Knowledge Representation, and Machine Learning for Translational Research: Recent Contributions

Ontologies, Knowledge Representation, and Machine Learning for Translational Research: Recent Contributions

Phenotype-genotype comorbidity analysis of patients with rare disorders provides insight into their pathological and molecular bases

Experience in Developing an FHIR Medical Data Management Platform to Provide Clinical Decision Support

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