Dataset Information

The Use of Inter-terminology Maps for the Creation and Maintenance of Value Sets.

ABSTRACT: Value sets are essential in activities such as electronic clinical quality measures (eCQM) and patient cohort definition. Creation and maintenance of value sets is labor intensive and error prone. Our method aims to use existing inter-terminology maps to improve the quality of value sets that are defined in more than one terminology. For 197 eCQM value sets defined in SNOMED CT plus ICD-9-CM and/or ICD-10-CM, the map-generated codes showed good overlap with the value set codes. Manual review showed that some new codes identified by mapping should probably be included in the value sets. This could potentially augment the ICD-9-CM codes by 45% (1.5 codes), ICD-10-CM codes by 25% (1.8 codes) and SNOMED CT codes by up to 42% (4.8 codes) per value set on average. The mapping between SNOMED CT and ICD-10-PCS did not perform as well because of the granularity discrepancy in the map.

SUBMITTER: Fung KW

PROVIDER: S-EPMC7153132 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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The Use of Inter-terminology Maps for the Creation and Maintenance of Value Sets.

Fung Kin Wah KW Xu Julia J Gold Sigfried S

AMIA ... Annual Symposium proceedings. AMIA Symposium 20190101

Value sets are essential in activities such as electronic clinical quality measures (eCQM) and patient cohort definition. Creation and maintenance of value sets is labor intensive and error prone. Our method aims to use existing inter-terminology maps to improve the quality of value sets that are defined in more than one terminology. For 197 eCQM value sets defined in SNOMED CT plus ICD-9-CM and/or ICD-10-CM, the map-generated codes showed good overlap with the value set codes. Manual review sho ...[more]

PMID: 32308837

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Project description:IntroductionCoproduction introduces a fundamental shift in how healthcare service is conceptualised. The mechanistic idea of healthcare being a 'product' generated by the healthcare system and delivered to patients is replaced by that of a service co-created by the healthcare system and the users of healthcare services. Fjeldstad et al offer an approach for conceptualising value creation in complex service contexts that we believe is applicable to coproduction of healthcare service. We have adapted Fjeldstad's value creation model based on a detailed case study of a renal haemodialysis service in Jonkoping, Sweden, which demonstrates coproduction characteristics and key elements of Fjeldstad's model.Methods and analysisWe propose a five-part coproduction value creation model for healthcare service: (1) value chain, characterised by a standardised set of processes that serve a commonly occurring need; (2) value shop, which offers a customised response for unique cases; (3) a facilitated value network, which involves groups of individuals struggling with similar challenges; (4) interconnection between shop, chain and network elements and (5) leadership. We will seek to articulate and assess the value creation model through the work of a community of practice comprised of a diverse international workgroup with representation from executive, financial and clinical leaders as well as other key stakeholders from multiple health systems. We then will conduct pilot studies of a qualitative self-assessment process in participating health systems, and ultimately develop and test quantitative measures for assessing coproduction value creation.Ethics and disseminationThis study has been approved by the Dartmouth-Hitchcock Health Institutional Review Board (D-HH IRB) as a minimal risk research study. Findings and scholarship will be disseminated broadly through continuous engagement with health system stakeholders, national and international academic presentations and publications and an internet-based electronic platform for publicly accessible study information.

Project description:BackgroundEpistatic miniarray profiling (E-MAPs) is a high-throughput approach capable of quantifying aggravating or alleviating genetic interactions between gene pairs. The datasets resulting from E-MAP experiments typically take the form of a symmetric pairwise matrix of interaction scores. These datasets have a significant number of missing values - up to 35% - that can reduce the effectiveness of some data analysis techniques and prevent the use of others. An effective method for imputing interactions would therefore increase the types of possible analysis, as well as increase the potential to identify novel functional interactions between gene pairs. Several methods have been developed to handle missing values in microarray data, but it is unclear how applicable these methods are to E-MAP data because of their pairwise nature and the significantly larger number of missing values. Here we evaluate four alternative imputation strategies, three local (Nearest neighbor-based) and one global (PCA-based), that have been modified to work with symmetric pairwise data.ResultsWe identify different categories for the missing data based on their underlying cause, and show that values from the largest category can be imputed effectively. We compare local and global imputation approaches across a variety of distinct E-MAP datasets, showing that both are competitive and preferable to filling in with zeros. In addition we show that these methods are effective in an E-MAP from a different species, suggesting that pairwise imputation techniques will be increasingly useful as analogous epistasis mapping techniques are developed in different species. We show that strongly alleviating interactions are significantly more difficult to predict than strongly aggravating interactions. Finally we show that imputed interactions, generated using nearest neighbor methods, are enriched for annotations in the same manner as measured interactions. Therefore our method potentially expands the number of mapped epistatic interactions. In addition we make implementations of our algorithms available for use by other researchers.ConclusionsWe address the problem of missing value imputation for E-MAPs, and suggest the use of symmetric nearest neighbor based approaches as they offer consistently accurate imputations across multiple datasets in a tractable manner.

Project description:BackgroundSeveral tools have been developed to perform global gene expression profile data analysis, to search for specific chromosomal regions whose features meet defined criteria as well as to study neighbouring gene expression. However, most of these tools are tailored for a specific use in a particular context (e.g. they are species-specific, or limited to a particular data format) and they typically accept only gene lists as input.ResultsTRAM (Transcriptome Mapper) is a new general tool that allows the simple generation and analysis of quantitative transcriptome maps, starting from any source listing gene expression values for a given gene set (e.g. expression microarrays), implemented as a relational database. It includes a parser able to assign univocal and updated gene symbols to gene identifiers from different data sources. Moreover, TRAM is able to perform intra-sample and inter-sample data normalization, including an original variant of quantile normalization (scaled quantile), useful to normalize data from platforms with highly different numbers of investigated genes. When in 'Map' mode, the software generates a quantitative representation of the transcriptome of a sample (or of a pool of samples) and identifies if segments of defined lengths are over/under-expressed compared to the desired threshold. When in 'Cluster' mode, the software searches for a set of over/under-expressed consecutive genes. Statistical significance for all results is calculated with respect to genes localized on the same chromosome or to all genome genes. Transcriptome maps, showing differential expression between two sample groups, relative to two different biological conditions, may be easily generated. We present the results of a biological model test, based on a meta-analysis comparison between a sample pool of human CD34+ hematopoietic progenitor cells and a sample pool of megakaryocytic cells. Biologically relevant chromosomal segments and gene clusters with differential expression during the differentiation toward megakaryocyte were identified.ConclusionsTRAM is designed to create, and statistically analyze, quantitative transcriptome maps, based on gene expression data from multiple sources. The release includes FileMaker Pro database management runtime application and it is freely available at http://apollo11.isto.unibo.it/software/, along with preconfigured implementations for mapping of human, mouse and zebrafish transcriptomes.

Dataset Information

The Use of Inter-terminology Maps for the Creation and Maintenance of Value Sets.

Publications

The Use of Inter-terminology Maps for the Creation and Maintenance of Value Sets.

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