Mapping Partners Master Drug Dictionary to RxNorm using an NLP-based approach

Zhou, Li; Plasek, Joseph M.; Mahoney, Lisa M.; Chang, Frank Y.; Dimaggio, Dana; Rocha, Roberto A.

doi:10.1016/j.jbi.2011.11.006

Cited by 29 publications

(17 citation statements)

References 11 publications

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“…The protocols to accurately extract medications, clinical problems, allergies, and symptoms from clinical text have been published previously, and our methodological approach to customizing MTERMS for social factor identification is presented separately (Zhou et al. ; Goss et al. ; Zhou et al.…”

Section: Methodsmentioning

confidence: 99%

Hospital Readmission and Social Risk Factors Identified from Physician Notes

Navathe

Zhong

Lei

et al. 2017

Health Services Research

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122

118

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Objective To evaluate the prevalence of seven social factors using physician notes as compared to claims and structured electronic health records (EHRs) data and the resulting association with 30‐day readmissions. Study Setting A multihospital academic health system in southeastern Massachusetts. Study Design An observational study of 49,319 patients with cardiovascular disease admitted from January 1, 2011, to December 31, 2013, using multivariable logistic regression to adjust for patient characteristics. Data Collection/Extraction Methods All‐payer claims, EHR data, and physician notes extracted from a centralized clinical registry. Principal Findings All seven social characteristics were identified at the highest rates in physician notes. For example, we identified 14,872 patient admissions with poor social support in physician notes, increasing the prevalence from 0.4 percent using ICD‐9 codes and structured EHR data to 16.0 percent. Compared to an 18.6 percent baseline readmission rate, risk‐adjusted analysis showed higher readmission risk for patients with housing instability (readmission rate 24.5 percent; p < .001), depression (20.6 percent; p < .001), drug abuse (20.2 percent; p = .01), and poor social support (20.0 percent; p = .01). Conclusions The seven social risk factors studied are substantially more prevalent than represented in administrative data. Automated methods for analyzing physician notes may enable better identification of patients with social needs.

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Section: Methodsmentioning

confidence: 99%

Hospital Readmission and Social Risk Factors Identified from Physician Notes

Navathe

Zhong

Lei

et al. 2017

Health Services Research

Self Cite

122

118

View full text Add to dashboard Cite

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“…Lexical and contextual or structural similarity computing have been used in several approaches of automated ontology matching at large scale in the biomedical and life sciences domain [ 62 – 65 ]. In this domain, the UMLS is widely used as a resource.…”

Section: Discussionmentioning

confidence: 99%

“…Zhou and colleagues used Natural Language Processing techniques to map a drug dictionary to RxNorm [ 65 ]. They mapped about 6,000 terms from Partners Master Drug Dictionary and 99 of the top prescribed medications to RxNorm.…”

Section: Discussionmentioning

confidence: 99%

An effective method of large scale ontology matching

Diallo

2014

J Biomed Sem

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BackgroundWe are currently facing a proliferation of heterogeneous biomedical data sources accessible through various knowledge-based applications. These data are annotated by increasingly extensive and widely disseminated knowledge organisation systems ranging from simple terminologies and structured vocabularies to formal ontologies. In order to solve the interoperability issue, which arises due to the heterogeneity of these ontologies, an alignment task is usually performed. However, while significant effort has been made to provide tools that automatically align small ontologies containing hundreds or thousands of entities, little attention has been paid to the matching of large sized ontologies in the life sciences domain.ResultsWe have designed and implemented ServOMap, an effective method for large scale ontology matching. It is a fast and efficient high precision system able to perform matching of input ontologies containing hundreds of thousands of entities. The system, which was included in the 2012 and 2013 editions of the Ontology Alignment Evaluation Initiative campaign, performed very well. It was ranked among the top systems for the large ontologies matching.ConclusionsWe proposed an approach for large scale ontology matching relying on Information Retrieval (IR) techniques and the combination of lexical and machine learning contextual similarity computing for the generation of candidate mappings. It is particularly adapted to the life sciences domain as many of the ontologies in this domain benefit from synonym terms taken from the Unified Medical Language System and that can be used by our IR strategy. The ServOMap system we implemented is able to deal with hundreds of thousands entities with an efficient computation time.

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“…Since its creation, RxNorm has been increasingly recognized by the biomedical informatics community as an emerging standard for clinical information exchange [20]. RxNorm is organized by concepts, in which each concept consists of drug names sharing the same meaning at a specific level of abstraction and is assigned a concept unique identifier (RxCUI).…”

Section: Methodsmentioning

confidence: 99%

Standardizing adverse drug event reporting data

Wang

Jiang

et al. 2014

J Biomed Sem

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BackgroundThe Adverse Event Reporting System (AERS) is an FDA database providing rich information on voluntary reports of adverse drug events (ADEs). Normalizing data in the AERS would improve the mining capacity of the AERS for drug safety signal detection and promote semantic interoperability between the AERS and other data sources. In this study, we normalize the AERS and build a publicly available normalized ADE data source. The drug information in the AERS is normalized to RxNorm, a standard terminology source for medication, using a natural language processing medication extraction tool, MedEx. Drug class information is then obtained from the National Drug File-Reference Terminology (NDF-RT) using a greedy algorithm. Adverse events are aggregated through mapping with the Preferred Term (PT) and System Organ Class (SOC) codes of Medical Dictionary for Regulatory Activities (MedDRA). The performance of MedEx-based annotation was evaluated and case studies were performed to demonstrate the usefulness of our approaches.ResultsOur study yields an aggregated knowledge-enhanced AERS data mining set (AERS-DM). In total, the AERS-DM contains 37,029,228 Drug-ADE records. Seventy-one percent (10,221/14,490) of normalized drug concepts in the AERS were classified to 9 classes in NDF-RT. The number of unique pairs is 4,639,613 between RxNorm concepts and MedDRA Preferred Term (PT) codes and 205,725 between RxNorm concepts and SOC codes after ADE aggregation.ConclusionsWe have built an open-source Drug-ADE knowledge resource with data being normalized and aggregated using standard biomedical ontologies. The data resource has the potential to assist the mining of ADE from AERS for the data mining research community.

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Mapping Partners Master Drug Dictionary to RxNorm using an NLP-based approach

Cited by 29 publications

References 11 publications

Hospital Readmission and Social Risk Factors Identified from Physician Notes

Hospital Readmission and Social Risk Factors Identified from Physician Notes

An effective method of large scale ontology matching

Standardizing adverse drug event reporting data

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