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Patient-Patient Similarity-Based Screening of a Clinical Data Warehouse to Support Ciliopathy Diagnosis

A timely diagnosis is a key challenge for many rare diseases. As an expanding group of rare and severe monogenic disorders with a broad spectrum of clinical manifestations, ciliopathies, notably renal ciliopathies, suffer from important underdiagnosis issues. Our objective is to develop an approach...

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Autores principales: Chen, Xiaoyi, Faviez, Carole, Vincent, Marc, Briseño-Roa, Luis, Faour, Hassan, Annereau, Jean-Philippe, Lyonnet, Stanislas, Zaidan, Mohamad, Saunier, Sophie, Garcelon, Nicolas, Burgun, Anita
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8993144/
https://www.ncbi.nlm.nih.gov/pubmed/35401179
http://dx.doi.org/10.3389/fphar.2022.786710
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author Chen, Xiaoyi
Faviez, Carole
Vincent, Marc
Briseño-Roa, Luis
Faour, Hassan
Annereau, Jean-Philippe
Lyonnet, Stanislas
Zaidan, Mohamad
Saunier, Sophie
Garcelon, Nicolas
Burgun, Anita
author_facet Chen, Xiaoyi
Faviez, Carole
Vincent, Marc
Briseño-Roa, Luis
Faour, Hassan
Annereau, Jean-Philippe
Lyonnet, Stanislas
Zaidan, Mohamad
Saunier, Sophie
Garcelon, Nicolas
Burgun, Anita
author_sort Chen, Xiaoyi
collection PubMed
description A timely diagnosis is a key challenge for many rare diseases. As an expanding group of rare and severe monogenic disorders with a broad spectrum of clinical manifestations, ciliopathies, notably renal ciliopathies, suffer from important underdiagnosis issues. Our objective is to develop an approach for screening large-scale clinical data warehouses and detecting patients with similar clinical manifestations to those from diagnosed ciliopathy patients. We expect that the top-ranked similar patients will benefit from genetic testing for an early diagnosis. The dependence and relatedness between phenotypes were taken into account in our similarity model through medical concept embedding. The relevance of each phenotype to each patient was also considered by adjusted aggregation of phenotype similarity into patient similarity. A ranking model based on the best-subtype-average similarity was proposed to address the phenotypic overlapping and heterogeneity of ciliopathies. Our results showed that using less than one-tenth of learning sources, our language and center specific embedding provided comparable or better performances than other existing medical concept embeddings. Combined with the best-subtype-average ranking model, our patient-patient similarity-based screening approach was demonstrated effective in two large scale unbalanced datasets containing approximately 10,000 and 60,000 controls with kidney manifestations in the clinical data warehouse (about 2 and 0.4% of prevalence, respectively). Our approach will offer the opportunity to identify candidate patients who could go through genetic testing for ciliopathy. Earlier diagnosis, before irreversible end-stage kidney disease, will enable these patients to benefit from appropriate follow-up and novel treatments that could alleviate kidney dysfunction.
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spelling pubmed-89931442022-04-09 Patient-Patient Similarity-Based Screening of a Clinical Data Warehouse to Support Ciliopathy Diagnosis Chen, Xiaoyi Faviez, Carole Vincent, Marc Briseño-Roa, Luis Faour, Hassan Annereau, Jean-Philippe Lyonnet, Stanislas Zaidan, Mohamad Saunier, Sophie Garcelon, Nicolas Burgun, Anita Front Pharmacol Pharmacology A timely diagnosis is a key challenge for many rare diseases. As an expanding group of rare and severe monogenic disorders with a broad spectrum of clinical manifestations, ciliopathies, notably renal ciliopathies, suffer from important underdiagnosis issues. Our objective is to develop an approach for screening large-scale clinical data warehouses and detecting patients with similar clinical manifestations to those from diagnosed ciliopathy patients. We expect that the top-ranked similar patients will benefit from genetic testing for an early diagnosis. The dependence and relatedness between phenotypes were taken into account in our similarity model through medical concept embedding. The relevance of each phenotype to each patient was also considered by adjusted aggregation of phenotype similarity into patient similarity. A ranking model based on the best-subtype-average similarity was proposed to address the phenotypic overlapping and heterogeneity of ciliopathies. Our results showed that using less than one-tenth of learning sources, our language and center specific embedding provided comparable or better performances than other existing medical concept embeddings. Combined with the best-subtype-average ranking model, our patient-patient similarity-based screening approach was demonstrated effective in two large scale unbalanced datasets containing approximately 10,000 and 60,000 controls with kidney manifestations in the clinical data warehouse (about 2 and 0.4% of prevalence, respectively). Our approach will offer the opportunity to identify candidate patients who could go through genetic testing for ciliopathy. Earlier diagnosis, before irreversible end-stage kidney disease, will enable these patients to benefit from appropriate follow-up and novel treatments that could alleviate kidney dysfunction. Frontiers Media S.A. 2022-03-25 /pmc/articles/PMC8993144/ /pubmed/35401179 http://dx.doi.org/10.3389/fphar.2022.786710 Text en Copyright © 2022 Chen, Faviez, Vincent, Briseño-Roa, Faour, Annereau, Lyonnet, Zaidan, Saunier, Garcelon and Burgun. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Pharmacology
Chen, Xiaoyi
Faviez, Carole
Vincent, Marc
Briseño-Roa, Luis
Faour, Hassan
Annereau, Jean-Philippe
Lyonnet, Stanislas
Zaidan, Mohamad
Saunier, Sophie
Garcelon, Nicolas
Burgun, Anita
Patient-Patient Similarity-Based Screening of a Clinical Data Warehouse to Support Ciliopathy Diagnosis
title Patient-Patient Similarity-Based Screening of a Clinical Data Warehouse to Support Ciliopathy Diagnosis
title_full Patient-Patient Similarity-Based Screening of a Clinical Data Warehouse to Support Ciliopathy Diagnosis
title_fullStr Patient-Patient Similarity-Based Screening of a Clinical Data Warehouse to Support Ciliopathy Diagnosis
title_full_unstemmed Patient-Patient Similarity-Based Screening of a Clinical Data Warehouse to Support Ciliopathy Diagnosis
title_short Patient-Patient Similarity-Based Screening of a Clinical Data Warehouse to Support Ciliopathy Diagnosis
title_sort patient-patient similarity-based screening of a clinical data warehouse to support ciliopathy diagnosis
topic Pharmacology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8993144/
https://www.ncbi.nlm.nih.gov/pubmed/35401179
http://dx.doi.org/10.3389/fphar.2022.786710
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