OBJECTIVE: To construct a data quality (DQ) system that incorporates combinations of methods to evaluate data characteristics and analytic fitness across research questions for multiple uses. MATERIALS AND METHODS: Drawing from experience of other data quality programs, network data extraction needs, and recurring study requirements, we developed 5 standards to guide development of a modular, multifaceted data quality system. These included annotation and documentation, ability to measure research readiness, reproducibility across networks, flexibility for the user, and interpretability to research and project teams. Implementation of checks based on these principles focused on reusability and interactive visualization of results. RESULTS: We identified 10 check types producing over 444 check applications and deployed them in 2 multi-institutional networks. Check types span structural conformance to a data model, utility for common research needs, and study-specific customization. All check types are customizable without dependencies between them. A dashboard visualizes results, permitting adjustments based on number of data sources, need for source masking, and the user's focus. All components can be applied as written to any data source using OMOP and are readily modified for other data models. DISCUSSION: We have extended previous work through our novel and multifaceted approach to data quality assessment, addressing needs in both network data improvement and research usage. We developed a capable and deployable system rather than tailoring to specific use cases. CONCLUSION: Our novel DQ assessment system provides essential components for future standardization and collaboration to improve fitness of clinical data for intended use.

A vast amount of vital clinical data is available within unstructured texts such as discharge summaries and procedure notes in Electronic Medical Records (EMRs). Automatically transforming such unstructured data into structured units is crucial for effective data analysis in the field of clinical informatics. Recognizing phrases that reveal important medical information in a concise and thorough manner is a fundamental step in this process. Existing systems that are built for opendomain texts are designed to detect mostly non-medical phrases, while tools designed specifically for extracting concepts from clinical texts are not scalable to large corpora and often leave out essential context surrounding those detected clinical concepts. We address these issues by proposing a framework, CliniPhrase, which adapts domain-specific deep neural network based language models (such as ClinicalBERT) to effectively and efficiently extract high-quality phrases from clinical documents with a limited amount of training data. Experimental results on the MIMIC-III dataset show that our method can outperform the current state-of-the-art techniques by up to 18% in terms of F1measure while being very efficient (up to 48 times faster).

MOTIVATION: Graph embedding learning that aims to automatically learn low-dimensional node representations, has drawn increasing attention in recent years. To date, most recent graph embedding methods are evaluated on social and information networks and are not comprehensively studied on biomedical networks under systematic experiments and analyses. On the other hand, for a variety of biomedical network analysis tasks, traditional techniques such as matrix factorization (which can be seen as a type of graph embedding methods) have shown promising results, and hence there is a need to systematically evaluate the more recent graph embedding methods (e.g. random walk-based and neural network-based) in terms of their usability and potential to further the state-of-the-art. RESULTS: We select 11 representative graph embedding methods and conduct a systematic comparison on 3 important biomedical link prediction tasks: drug-disease association (DDA) prediction, drug-drug interaction (DDI) prediction, protein-protein interaction (PPI) prediction; and 2 node classification tasks: medical term semantic type classification, protein function prediction. Our experimental results demonstrate that the recent graph embedding methods achieve promising results and deserve more attention in the future biomedical graph analysis. Compared with three state-of-the-art methods for DDAs, DDIs and protein function predictions, the recent graph embedding methods achieve competitive performance without using any biological features and the learned embeddings can be treated as complementary representations for the biological features. By summarizing the experimental results, we provide general guidelines for properly selecting graph embedding methods and setting their hyper-parameters for different biomedical tasks. AVAILABILITY AND IMPLEMENTATION: As part of our contributions in the paper, we develop an easy-to-use Python package with detailed instructions, BioNEV, available at: https://github.com/xiangyue9607/BioNEV, including all source code and datasets, to facilitate studying various graph embedding methods on biomedical tasks. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Unstructured clinical texts contain rich health-related information. To better utilize the knowledge buried in clinical texts, discovering synonyms for a medical query term has become an important task. Recent automatic synonym discovery methods leveraging raw text information have been developed. However, to preserve patient privacy and security, it is usually quite difficult to get access to large-scale raw clinical texts. In this paper, we study a new setting named synonym discovery on privacy-aware clinical data (i.e., medical terms extracted from the clinical texts and their aggregated co-occurrence counts, without raw clinical texts). To solve the problem, we propose a new framework SurfCon that leverages two important types of information in the privacy-aware clinical data, i.e., the surface form information, and the global context information for synonym discovery. In particular, the surface form module enables us to detect synonyms that look similar while the global context module plays a complementary role to discover synonyms that are semantically similar but in different surface forms, and both allow us to deal with the OOV query issue (i.e., when the query is not found in the given data). We conduct extensive experiments and case studies on publicly available privacy-aware clinical data, and show that SurfCon can outperform strong baseline methods by large margins under various settings.

The increased adoption of clinical whole exome sequencing (WES) has improved the diagnostic yield for patients with complex genetic conditions. However, the informatics practice for handling information contained in whole exome reports is still in its infancy, as evidenced by the lack of a common vocabulary within clinical sequencing reports generated across genetic laboratories. Genetic testing results are mostly transmitted using portable document format, which can make secondary analysis and data extraction challenging. This paper reviews a sample of clinical exome reports generated by Clinical Laboratory Improvement Amendments-certified genetic testing laboratories at tertiary-care facilities to assess and identify common data elements. Like structured radiology reports, which enable faster information retrieval and reuse, structuring genetic information within clinical WES reports would help facilitate integration of genetic information into electronic health records and enable retrospective research on the clinical utility of WES. We identify elements listed as mandatory according to practice guidelines but are currently missing from some of the clinical reports, which might help to organize the data when stored within structured databases. We also highlight elements, such as patient consent, that, although they do not appear within any of the current reports, may help in interpreting some of the information within the reports. Integrating genetic and clinical information would assist the adoption of personalized medicine for improved patient care and outcomes.

Genetic linkage analysis is a recombinant technology used for mapping disease genes on the genome, based on genotypic and phenotypic data collected from families that have affected members. The LOD score is a commonly used statistic in genetic linkage analysis. LOD scores are computed assuming specific values for genetic parameters. However, for complex disorders the specified parameter values are often unknown. One way to address this issue is to maximize the LOD score over all genetic parameters to get a maximum LOD score, or MOD score. Another way is to integrate the LOD score across the genetic parameters to form a posterior probability of linkage, or PPL. Both methods require calculation of large numbers of LOD scores under different sets of parameter values. These calculations may be very time-consuming and can form a significant bottleneck in disease gene mapping. The motivation for this work is to speed up the computation of large numbers of LOD scores in linkage analysis. Instead of the usual LOD calculation where the likelihood of a pedigree under each set of parameter values is computed based on traversing the pedigree, the likelihood of the pedigree is computed here as an algebraic expression that can be optimized and reused. This optimized likelihood expression can be evaluated an arbitrary number of times for LOD scores under different values of the genetic parameters, resulting in much faster speeds. Our initial results show that this approach can speed up the traditional genetic linkage computation by 10~1200 times.