scholarly journals Semantic Modeling for Exposomics with Exploratory Evaluation in Clinical Context

2017 ◽  
Vol 2017 ◽  
pp. 1-10 ◽  
Author(s):  
Jung-wei Fan ◽  
Jianrong Li ◽  
Yves A. Lussier
Keyword(s):  
Domain Knowledge ◽  
Critical Dimension ◽  
Clinical Context ◽  
Clinical Text ◽  
Unified Medical Language System ◽  
Rich Domain ◽  
Medical Language ◽  
Microbial Etiology ◽  
Effective Representation ◽  
Discharge Summaries

Exposome is a critical dimension in the precision medicine paradigm. Effective representation of exposomics knowledge is instrumental to melding nongenetic factors into data analytics for clinical research. There is still limited work in (1) modeling exposome entities and relations with proper integration to mainstream ontologies and (2) systematically studying their presence in clinical context. Through selected ontological relations, we developed a template-driven approach to identifying exposome concepts from the Unified Medical Language System (UMLS). The derived concepts were evaluated in terms of literature coverage and the ability to assist in annotating clinical text. The generated semantic model represents rich domain knowledge about exposure events (454 pairs of relations between exposure and outcome). Additionally, a list of 5667 disorder concepts with microbial etiology was created for inferred pathogen exposures. The model consistently covered about 90% of PubMed literature on exposure-induced iatrogenic diseases over 10 years (2001–2010). The model contributed to the efficiency of exposome annotation in clinical text by filtering out 78% of irrelevant machine annotations. Analysis into 50 annotated discharge summaries helped advance our understanding of the exposome information in clinical text. This pilot study demonstrated feasibility of semiautomatically developing a useful semantic resource for exposomics.

scholarly journals Ambiguity in medical concept normalization: An analysis of types and coverage in electronic health record datasets

2020 ◽  
Author(s):  
Denis Newman-Griffis ◽  
Guy Divita ◽  
Bart Desmet ◽  
Ayah Zirikly ◽  
Carolyn P Rosé ◽  
...  
Keyword(s):  
Biomedical Literature ◽  
Clinical Text ◽  
Unified Medical Language System ◽  
Medical Language ◽  
Medical Concept ◽  
Normalization Methods ◽  
The Rich ◽  
Medical Concept Normalization ◽  
Medical Concepts ◽  
Clinical Concept

Abstract Objectives Normalizing mentions of medical concepts to standardized vocabularies is a fundamental component of clinical text analysis. Ambiguity—words or phrases that may refer to different concepts—has been extensively researched as part of information extraction from biomedical literature, but less is known about the types and frequency of ambiguity in clinical text. This study characterizes the distribution and distinct types of ambiguity exhibited by benchmark clinical concept normalization datasets, in order to identify directions for advancing medical concept normalization research. Materials and Methods We identified ambiguous strings in datasets derived from the 2 available clinical corpora for concept normalization and categorized the distinct types of ambiguity they exhibited. We then compared observed string ambiguity in the datasets with potential ambiguity in the Unified Medical Language System (UMLS) to assess how representative available datasets are of ambiguity in clinical language. Results We found that <15% of strings were ambiguous within the datasets, while over 50% were ambiguous in the UMLS, indicating only partial coverage of clinical ambiguity. The percentage of strings in common between any pair of datasets ranged from 2% to only 36%; of these, 40% were annotated with different sets of concepts, severely limiting generalization. Finally, we observed 12 distinct types of ambiguity, distributed unequally across the available datasets, reflecting diverse linguistic and medical phenomena. Discussion Existing datasets are not sufficient to cover the diversity of clinical concept ambiguity, limiting both training and evaluation of normalization methods for clinical text. Additionally, the UMLS offers important semantic information for building and evaluating normalization methods. Conclusions Our findings identify 3 opportunities for concept normalization research, including a need for ambiguity-specific clinical datasets and leveraging the rich semantics of the UMLS in new methods and evaluation measures for normalization.

scholarly journals Assessing the role of a medication-indication resource in the treatment relation extraction from clinical text

2014 ◽  
Vol 22 (e1) ◽  
pp. e162-e176 ◽  
Author(s):  
Cosmin Adrian Bejan ◽  
Wei-Qi Wei ◽  
Joshua C Denny
Keyword(s):  
Simple Algorithm ◽  
Ensemble Methods ◽  
Relation Extraction ◽  
Superior Performance ◽  
High Agreement ◽  
Clinical Text ◽  
Unified Medical Language System ◽  
Knowledge Based ◽  
Medical Language

Abstract Objective To evaluate the contribution of the MEDication Indication (MEDI) resource and SemRep for identifying treatment relations in clinical text. Materials and methods We first processed clinical documents with SemRep to extract the Unified Medical Language System (UMLS) concepts and the treatment relations between them. Then, we incorporated MEDI into a simple algorithm that identifies treatment relations between two concepts if they match a medication-indication pair in this resource. For a better coverage, we expanded MEDI using ontology relationships from RxNorm and UMLS Metathesaurus. We also developed two ensemble methods, which combined the predictions of SemRep and the MEDI algorithm. We evaluated our selected methods on two datasets, a Vanderbilt corpus of 6864 discharge summaries and the 2010 Informatics for Integrating Biology and the Bedside (i2b2)/Veteran's Affairs (VA) challenge dataset. Results The Vanderbilt dataset included 958 manually annotated treatment relations. A double annotation was performed on 25% of relations with high agreement (Cohen's κ = 0.86). The evaluation consisted of comparing the manual annotated relations with the relations identified by SemRep, the MEDI algorithm, and the two ensemble methods. On the first dataset, the best F1-measure results achieved by the MEDI algorithm and the union of the two resources (78.7 and 80, respectively) were significantly higher than the SemRep results (72.3). On the second dataset, the MEDI algorithm achieved better precision and significantly lower recall values than the best system in the i2b2 challenge. The two systems obtained comparable F1-measure values on the subset of i2b2 relations with both arguments in MEDI. Conclusions Both SemRep and MEDI can be used to extract treatment relations from clinical text. Knowledge-based extraction with MEDI outperformed use of SemRep alone, but superior performance was achieved by integrating both systems. The integration of knowledge-based resources such as MEDI into information extraction systems such as SemRep and the i2b2 relation extractors may improve treatment relation extraction from clinical text.

scholarly journals MetaMap Lite: an evaluation of a new Java implementation of MetaMap

2017 ◽  
Vol 24 (4) ◽  
pp. 841-844 ◽  
Author(s):  
Dina Demner-Fushman ◽  
Willie J Rogers ◽  
Alan R Aronson
Keyword(s):  
Text Analysis ◽  
Text Processing ◽  
Named Entity Recognition ◽  
Biomedical Literature ◽  
Entity Recognition ◽  
Biomedical Text ◽  
Clinical Text ◽  
Unified Medical Language System ◽  
Named Entity ◽  
Medical Language

Abstract MetaMap is a widely used named entity recognition tool that identifies concepts from the Unified Medical Language System Metathesaurus in text. This study presents MetaMap Lite, an implementation of some of the basic MetaMap functions in Java. On several collections of biomedical literature and clinical text, MetaMap Lite demonstrated real-time speed and precision, recall, and F1 scores comparable to or exceeding those of MetaMap and other popular biomedical text processing tools, clinical Text Analysis and Knowledge Extraction System (cTAKES) and DNorm.

Leveraging the UMLS As a Data Standard for Rare Disease Data Normalization and Harmonization

2020 ◽  
Author(s):  
Qian Zhu ◽  
Dac-Trung Nguyen ◽  
Eric Sid ◽  
Anne Pariser
Keyword(s):  
Rare Disease ◽  
Rare Diseases ◽  
Mendelian Inheritance ◽  
Data Normalization ◽  
Community Settings ◽  
Unified Medical Language System ◽  
Data Standard ◽  
Medical Language ◽  
Disease Concepts ◽  
High Level

Abstract Objective In this study, we aimed to evaluate the capability of the Unified Medical Language System (UMLS) as one data standard to support data normalization and harmonization of datasets that have been developed for rare diseases. Through analysis of data mappings between multiple rare disease resources and the UMLS, we propose suggested extensions of the UMLS that will enable its adoption as a global standard in rare disease. Methods We analyzed data mappings between the UMLS and existing datasets on over 7,000 rare diseases that were retrieved from four publicly accessible resources: Genetic And Rare Diseases Information Center (GARD), Orphanet, Online Mendelian Inheritance in Men (OMIM), and the Monarch Disease Ontology (MONDO). Two types of disease mappings were assessed, (1) curated mappings extracted from those four resources; and (2) established mappings generated by querying the rare disease-based integrative knowledge graph developed in the previous study. Results We found that 100% of OMIM concepts, and over 50% of concepts from GARD, MONDO, and Orphanet were normalized by the UMLS and accurately categorized into the appropriate UMLS semantic groups. We analyzed 58,636 UMLS mappings, which resulted in 3,876 UMLS concepts across these resources. Manual evaluation of a random set of 500 UMLS mappings demonstrated a high level of accuracy (99%) of developing those mappings, which consisted of 414 mappings of synonyms (82.8%), 76 are subtypes (15.2%), and five are siblings (1%). Conclusion The mapping results illustrated in this study that the UMLS was able to accurately represent rare disease concepts, and their associated information, such as genes and phenotypes, and can effectively be used to support data harmonization across existing resources developed on collecting rare disease data. We recommend the adoption of the UMLS as a data standard for rare disease to enable the existing rare disease datasets to support future applications in a clinical and community settings.

Sign in / Sign up

Export Citation Format

Share Document