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Concept selection for phenotypes and diseases using learn to rank
2015
Journal of Biomedical Semantics
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We evaluate four pipelines as stand-alone systems and then attempt to optimise semantic-type based performance using several learn-to-rank (LTR) approaches -three pairwise and one listwise. ...
Phenotypes form the basis for determining the existence of a disease against the given evidence. ...
Acknowledgements We gratefully acknowledge the kind permission of the ShARE/CLEF eHealth evaluation organisers for facilitating access to the ShARE/CLEF eHealth corpus used in our evaluation. ...
doi:10.1186/s13326-015-0019-z
pmid:26034558
pmcid:PMC4450611
fatcat:ywypdbtuerdjphay3nd56yntvu
Diagnosis support systems for rare diseases: a scoping review
2020
Orphanet Journal of Rare Diseases
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Diagnosis targets varied from 1 rare disease to all rare diseases. Material used for diagnosis support consisted mostly of phenotype concepts, images or fluids. ...
Fourteen studies provided online tools, most of which aimed to support the diagnosis of all rare diseases by considering queries based on phenotype concepts. ...
Acknowledgments The authors acknowledge the ANR for the funding and would like to thank the C'IL-LICO members for their advice for the analysis and interpretation of the results. ...
doi:10.1186/s13023-020-01374-z
pmid:32299466
pmcid:PMC7164220
fatcat:irqxt4ps45frlp7s6lnzr2g46y
Automated disease cohort selection using word embeddings from Electronic Health Records
2018
Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
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Methodologies that automatically learn features from EHRs have been used for cohort selection as well. ...
Using medical concepts as a query, we then rank patients by their proximity in the embedding space and automatically extract putative disease cohorts via a distance threshold. ...
Acknowledgments We would like to thank the Mount Sinai Data Warehouse for facilitating data accessibility and the Mount Sinai Scientific Computing team for infrastructural support. ...
pmid:29218877
pmcid:PMC5788312
fatcat:fpydj3oyrjeh7ea3c7czhgjyj4
Automated disease cohort selection using word embeddings from Electronic Health Records
2017
Biocomputing 2018
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Methodologies that automatically learn features from EHRs have been used for cohort selection as well. ...
Using medical concepts as a query, we then rank patients by their proximity in the embedding space and automatically extract putative disease cohorts via a distance threshold. ...
Acknowledgments We would like to thank the Mount Sinai Data Warehouse for facilitating data accessibility and the Mount Sinai Scientific Computing team for infrastructural support. ...
doi:10.1142/9789813235533_0014
fatcat:3y2pdtmhvjbcfe7lc7xe4j6avy
Phe2vec: Automated Disease Phenotyping based on Unsupervised Embeddings from Electronic Health Records
[article]
2020
medRxiv
pre-print
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Objective: We introduce Phe2vec, an automated framework for disease phenotyping from electronic health records (EHRs) based on unsupervised learning. ...
Disease phenotypes are then derived from a seed concept and its neighbors in the embedding space. ...
Selection We used phenotypes based on embeddings to retrieve cohorts of patients for each disease. ...
doi:10.1101/2020.11.14.20231894
fatcat:hcdjknsc3jfnhn2wjy3nuwboui
Applying active learning to high-throughput phenotyping algorithms for electronic health records data
2013
JAMIA Journal of the American Medical Informatics Association
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study is supported in part by grants from the National Library of Medicine (R01-LM010685), the National Cancer Institute (R01-CA141307), the National Institute of General Medical Sciences (R01-GM102282), and ...
The synthetic derivative, from which the annotated records were derived, is supported by National Center for Research Resources UL1 RR024975, which is now at the National Center for Advancing Translational ...
medical language system (UMLS) concept unique identifiers from clinical notes; and refined features, which included billing codes and UMLS concepts highly relevant to the specific phenotypes, as selected ...
doi:10.1136/amiajnl-2013-001945
pmid:23851443
pmcid:PMC3861916
fatcat:6opdsgkds5ehlfrjcgw265psp4
Integrative disease classification based on cross-platform microarray data
2009
BMC Bioinformatics
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In addition, we systematically map textual annotations of datasets to concepts in Unified Medical Language System (UMLS), permitting quantitative analysis of the phenotype "distance" between datasets and ...
Using the leave one dataset out cross validation, ManiSVM achieved the overall accuracy of 70.7% (68.6% precision and 76.9% recall) with many disease classes achieving the accuracy higher than 80%. ...
Acknowledgements The authors thank Xuegong Zhang for helpful discussions and suggestions. ...
doi:10.1186/1471-2105-10-s1-s25
pmid:19208125
pmcid:PMC2648756
fatcat:romlcxl7xzgt3jdtzww5ks5tte
A Data-driven Framework for Learning and Visualizing Characteristics of Thrombotic Event Phenotypes from Clinical Texts
[article]
2021
medRxiv
pre-print
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We developed a generalizable, data-driven framework for learning, characterizing, and visualizing clinical concepts from both radiology and discharge summaries predictive of thrombotic phenotypes. ...
Although many investigators have developed targeted information extraction methods for identifying thrombotic phenotypes from radiology notes, these methods can be time consuming to train, require large ...
We extend our gratitude to the open-source community for making their resources available. The cui embeddings can be found at: https://figshare.com/s/00d69861786cd0156d81. ...
doi:10.1101/2021.03.09.21253233
fatcat:p5fm7lzf4raa3lqfy3o3no46ce
Next generation phenotyping using narrative reports in a rare disease clinical data warehouse
2018
Orphanet Journal of Rare Diseases
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We have developed a method to detect phenotypes associated with a group of patients using medical concepts extracted from free-text clinical narratives. ...
Methods: We leveraged the frequency and TF-IDF to explore the association between clinical phenotypes and rare diseases. ...
and agree to be accountable for all aspects of the work. ...
doi:10.1186/s13023-018-0830-6
pmid:29855327
pmcid:PMC5984368
fatcat:vgagwi6sabgnbcctpx2uz3rrka
Learning to Recognize Phenotype Candidates in the Auto-Immune Literature Using SVM Re-Ranking
2013
PLoS ONE
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We observed the advantage of using SVM-based learn-to-rank for sequence label combination over maximum entropy and a priority list approach. ...
Using partial matching the best micro-averaged F-score for phenotypes and five other entity classes was 79.9%. ...
We observed the advantage of using SVM learn-to-rank for hypothesis resolution and using all resources. ...
doi:10.1371/journal.pone.0072965
pmid:24155869
pmcid:PMC3796529
fatcat:jcy2fr7yrjgwngtfqvr4vhczdq
Identification of Similar Patients Through Medical Concept Embedding from Electronic Health Records: A Feasibility Study for Rare Disease Diagnosis
[chapter]
2021
Studies in Health Technology and Informatics
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To identify patients with similar clinical profiles and derive insights from the records and outcomes of similar patients can help fast and precise diagnosis and other clinical decisions for rare diseases ...
In this paper, we introduce the methods developed in the context of rare disease screening/diagnosis from clinical data warehouse using medical concept embedding and adjusted aggregations. ...
As we considered the task of identifying NPH-related ciliopathies from other diseases, we selected NPH patients with sufficient information in EHRs, and re used the control patients with overlapping phenotypes ...
doi:10.3233/shti210241
pmid:34042646
fatcat:pbviuy5szzfxxgh7v4hbhsf4ti
Integrating biomedical research and electronic health records to create knowledge-based biologically meaningful machine-readable embeddings
2019
Nature Communications
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Then an unsupervised machine-learning algorithm creates Propagated SPOKE Entry Vectors (PSEVs) that encode the importance of each SPOKE node for any code in the EHRs. ...
In order to advance precision medicine, detailed clinical features ought to be described in a way that leverages current knowledge. ...
In addition, we would like to thank Achievement Rewards for College Scientists (ARCS) Scholarship and the NHI BMI Training Grant (T32 GM067547/ 4T32GM067547-14). ...
doi:10.1038/s41467-019-11069-0
pmid:31292438
pmcid:PMC6620318
fatcat:b7pc3osxrrgefb34a4du2or42m
Comparison of automated candidate gene prediction systems using genes implicated in type 2 diabetes by genome-wide association studies
2009
BMC Bioinformatics
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Furthermore, they can be used to filter statistically less-well-supported genetic data to select more likely candidates. ...
Automated candidate gene prediction systems allow geneticists to hone in on disease genes more rapidly by identifying the most probable candidate genes linked to the disease phenotypes under investigation ...
Acknowledgements The authors wish to acknowledge funding from the Ronald Geoffrey Arnott Foundation. ...
doi:10.1186/1471-2105-10-s1-s69
pmid:19208173
pmcid:PMC2648789
fatcat:jo6drnkmvncthg5aqhdxfstxp4
Mimvec: a deep learning approach for analyzing the human phenome
2017
BMC Systems Biology
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We finally used the derived phenotype similarities with genomic data to prioritize candidate genes and demonstrated advantages of this method over existing ones. ...
We further derived pairwise phenotype similarities between 7988 human inherited diseases using their vector presentations. ...
Acknowledgements We thank the Institute for Data Science at Tsinghua University for providing a computer cluster that makes this research possible. ...
doi:10.1186/s12918-017-0451-z
pmid:28950906
pmcid:PMC5615244
fatcat:uwthfufnjfhoveenkh6lsa3gfm
SoftPanel: a website for grouping diseases and related disorders for generation of customized panels
2016
BMC Bioinformatics
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Various methods of retrieval including a keyword search, browsing of an arborized list of International Classification of Diseases, 10th revision (ICD-10) codes or using disorder phenotypic similarities ...
Although tools to prioritize candidate disease genes have been developed, the great majority of these require prior knowledge and a set of seed genes as input, which is only possible for diseases with ...
All of these scores are then used to construct an eight-dimensional vector, which is used as the input for performing machine learning. ...
doi:10.1186/s12859-016-0998-5
pmid:27044653
pmcid:PMC4820874
fatcat:uowugoekybcernuvnjifxifjke
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