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While using data standards can facilitate research by making it easier to share data, manually mapping to data standards creates an obstacle to their adoption. Semi-automated mapping strategies can reduce the manual mapping burden. Machine learning approaches, such as artificial neural networks, can predict mappings between clinical data standards but are limited by the need for training data. We developed a graph database that incorporates the Biomedical Research Integrated Domain Group (BRIDG) model, Common Data Elements (CDEs) from the National Cancer Institute's (NCI) cancer Data Standards Registry and Repository, and the NCI Thesaurus. We then used a shortest path algorithm to predict mappings from CDEs to classes in the BRIDG model. The resulting graph database provides a robust semantic framework for analysis and quality assurance testing. Using the graph database to predict CDE to BRIDG class mappings was limited by the subjective nature of mapping and data quality issues.

Phenome Wide Association Studies (PheWAS) enables phenome-wide scans to discover novel associations between genotype and clinical phenotypes via linking available genomic reports and large-scale Electronic Health Record (EHR). Data heterogeneity from different EHR systems and genetic reports has been a critical challenge that hinders meaningful validation. To address this, we propose an FHIR-based framework to model the PheWAS study in a standard manner. We developed an FHIR-based data model profile to enable the standard representation of data elements from genetic reports and EHR data that are used in the PheWAS study. As a proof-of-concept, we implemented the proposed method using a cohort of 1,595 pan-cancer patients with genetic reports from Foundation Medicine as well as the corresponding lab tests and diagnosis from Mayo EHRs. A PheWAS study is conducted and 81 significant genotype-phenotype associations are identified, in which 36 significant associations for cancers are validated based on a literature review.

With the increased adoption of electronic health records, data collected for routine clinical care is used for health outcomes and population sciences research, including the identification of phenotypes. In recent years, research networks, such as eMERGE, OHDSI and PCORnet, have been able to increase statistical power and population diversity by combining patient cohorts. These networks share phenotype algorithms that are executed at each participating site. Here we observe experiences with phenotype algorithm portability across seven research networks and propose a generalizable framework for phenotype algorithm portability. Several strategies exist to increase the portability of phenotype algorithms, reducing the implementation effort needed by each site. These include using a common data model, standardized representation of the phenotype algorithm logic, and technical solutions to facilitate federated execution of queries. Portability is achieved by tradeoffs across three domains: Data, Authoring and Implementation, and multiple approaches were observed in representing portable phenotype algorithms. Our proposed framework will help guide future research in operationalizing phenotype algorithm portability at scale.

Standards-based modeling of electronic health records (EHR) data holds great significance for data interoperability and large-scale usage. Integration of unstructured data into a standard data model, however, poses unique challenges partially due to heterogeneous type systems used in existing clinical NLP systems. We introduce a scalable and standards-based framework for integrating structured and unstructured EHR data leveraging the HL7 Fast Healthcare Interoperability Resources (FHIR) specification. We implemented a clinical NLP pipeline enhanced with an FHIR-based type system and performed a case study using medication data from Mayo Clinic's EHR. Two UIMA-based NLP tools known as MedXN and MedTime were integrated in the pipeline to extract FHIR MedicationStatement resources and related attributes from unstructured medication lists. We developed a rule-based approach for assigning the NLP output types to the FHIR elements represented in the type system, whereas we investigated the FHIR elements belonging to the source of the structured EMR data. We used the FHIR resource "MedicationStatement" as an example to illustrate our integration framework and methods. For evaluation, we manually annotated FHIR elements in 166 medication statements from 14 clinical notes generated by Mayo Clinic in the course of patient care, and used standard performance measures (precision, recall and f-measure). The F-scores achieved ranged from 0.73 to 0.99 for the various FHIR element representations. The results demonstrated that our framework based on the FHIR type system is feasible for normalizing and integrating both structured and unstructured EHR data.

In this paper, we present a platform known as D2Refine for facilitating clinical research study data element harmonization and standardization. D2Refine is developed on top of OpenRefine (formerly Google Refine) and leverages simple interface and extensible architecture of OpenRefine. D2Refine empowers the tabular representation of clinical research study data element definitions by allowing it to be easily organized and standardized using reconciliation services. D2Refine builds on valuable built-in data transformation features of OpenRefine to bring source data sets to a finer state quickly. We implemented the reconciliation services and search capabilities based on the standard Common Terminology Services 2 (CTS2) and the serialization of clinical research study data element definitions into standard representation using clinical information modeling technology for semantic interoperability. We demonstrate that D2Refine is a useful and promising platform that would help address the emergent needs for clinical research study data element harmonization and standardization.

Background: Immunotherapy is seen as a promising new treatment for cancer but it may also cause immune-related adverse events (irAEs). Post-market surveillance of immunotherapy drugs highly depends on the ability to capture and standardize irAE data. The Common Terminology Criteria for Adverse Events (CTCAE) is a potential terminology that can be leveraged for irAEs standardization. However, the capability of the CTCAE in irAEs standardization needs to be evaluated. Methods: We investigated the irAEs of six FDA approved cancer immunotherapy monoclonal antibodies (mAbs) and evaluated the coverage of the CTCAE for capturing irAEs. We manually identified irAEs from drug labels of the 6 mAbs as the gold standard. We assessed the performance of two text mining pipelines using the dictionary lookup of the CTCAE terms and identified irAEs. In the coverage evaluation, the CTCAE was compared with MedDRA, a standard terminology for regulatory science, for irAE standardization. Results: We manually identified 510 unique irAEs from the drug labels. When using the CTCAE as a dictionary to run the text mining pipeline, the precision, recall and F-measure value was 100%, 10.78% and 19.47%. After adding manually identified irAE terms into the dictionary, the recall and F-measure value significantly improved, increased to 95.69% and 97.31%, respectively. In the coverage evaluation, compared with MedDRA, the coverage rate of the CTCAE is only 13.50% when taking all the mining results together into consideration. Conclusion: With some limitations in our study, we clearly demonstrated that the CTCAE needs an extension to meet the irAE standardization task.

Acute Kidney Injury (AKI) in critical care is often a quickly-evolving clinical event with high morbidity and mortality. Early prediction of AKI risk in critical care setting can facilitate early interventions that are likely to provide ben- efit. Recently there have been some research on AKI prediction with patient Electronic Health Records (EHR). The class imbalance problem is encountered in such prediction setting where the number of AKI cases is usually much smaller than the controls. This study systematically investigates the impact of class imbalance on the performance of AKI prediction. We systematically investigate several class-balancing strategies to address class imbalance, includ- ing traditional statistical approaches and the proposed methods (case-control matching approach and individualized prediction approach). Our results show that the proposed class-balancing strategies can effectively improve the AKI prediction performance. Additionally, some important predictors (e.g., creatinine, chloride, and urine) for AKI can be found based on the proposed methods.

Platinum-based chemotherapies are first-line treatments for ovarian cancer (OC) patients. Although chemotherapy has a high initial response rate, some patients exhibit inherent chemo-resistance. With advancements of molecular and genomic profiling, it is of high interest to identify molecular and genomic signatures predictive of chemo- sensitivity priori to treatment initiation in order to better personalize care decisions. Previous efforts have made use of mRNA expression levels of selected genes responsible for repairing DNA damage, under the hypothesis that chemo efficacy is associated with their proficiency. However, the resulting scores have been difficult to interpret. In this study, we designed a single-sample based approach known as eCARD to investigate chemo-sensitivity in ovarian cancer patients from The Cancer Genome Atlas. We demonstrated that the proposed single-sample based approach can lead to a molecular-based chemo-sensitivity score predictive of prognosis, which validates in 5 independent cohorts, and associates with increasing mutation burden and likelihood of BRCA1/2 mutation.

Increasing interest in and experience with electronic health record (EHR)-driven phenotyping has yielded multiple challenges that are at present only partially addressed. Many solutions require the adoption of a single software platform, often with an additional cost of mapping existing patient and phenotypic data to multiple representations. We propose a set of guiding design principles and a modular software architecture to bridge the gap to a standardized phenotype representation, dissemination and execution. Ongoing development leveraging this proposed architecture has shown its ability to address existing limitations.

Heterogeneous drug data representation among different druggable genome knowledge resources and datasets delays effective cancer therapeutic target discovery within the broad scientific community. The objective of the present paper is to describe the challenges and lessons learned from our efforts in developing and evaluating a standards-based drug normalization framework targeting cancer druggable genome datasets. Our findings suggested that mechanisms need to be established to deal with spelling errors and irregularities in normalizing clinical drug data in The Cancer Genome Atlas (TCGA), whereas the annotations from NCI Thesaurus (NCIt) and PubChem are two layers of normalization that potentially bridge between the clinical phenotypes and the druggable genome knowledge for effective cancer therapeutic target discovery.

Terminology services serve an important role supporting clinical and research applications, and underpin a diverse set of processes and use cases. Through standardization efforts, terminology service-to-system interactions can leverage well-defined interfaces and predictable integration patterns. Often, however, users interact more directly with terminologies, and no such blueprints are available for describing terminology service-to-user interactions. In this work, we explore the main architecture principles necessary to build a user-centered terminology system, using an Extract-Transform-Load process as our primary usage scenario. To analyze our architecture, we present a prototype implementation based on the Common Terminology Services 2 (CTS2) standard using the Patient-Centered Network of Learning Health Systems (LHSNet) project as a concrete use case. We perform a preliminary evaluation of our prototype architecture using three architectural quality attributes: interoperability, adaptability and usability. We find that a design-time focus on user needs, cognitive models, and existing patterns is essential to maximize system utility.

Knowledge-driven text mining is becoming an important research area for identifying pharmacogenomics target genes. However, few of such studies have been focused on the pharmacogenomics targets of adverse drug events (ADEs). The objective of the present study is to build a framework of knowledge integration and discovery that aims to support pharmacogenomics target predication of ADEs. We integrate a semantically annotated literature corpus Semantic MEDLINE with a semantically coded ADE knowledgebase known as ADEpedia using a semantic web based framework. We developed a knowledge discovery approach combining a network analysis of a protein-protein interaction (PPI) network and a gene functional classification approach. We performed a case study of drug-induced long QT syndrome for demonstrating the usefulness of the framework in predicting potential pharmacogenomics targets of ADEs.

A standardized Adverse Drug Events (ADEs) knowledge base that encodes known ADE knowledge can be very useful in improving ADE detection for drug safety surveillance. In our previous study, we developed the ADEpedia that is a standardized knowledge base of ADEs based on drug product labels. The objectives of the present study are 1) to integrate normalized ADE knowledge from the Unified Medical Language System (UMLS) into the ADEpedia; and 2) to enrich the knowledge base with the drug-disorder co-occurrence data from a 51-million-document electronic medical records (EMRs) system. We extracted 266,832 drug-disorder concept pairs from the UMLS, covering 14,256 (1.69%) distinct drug concepts and 19,006 (3.53%) distinct disorder concepts. Of them, 71,626 (26.8%) concept pairs from UMLS co-occurred in the EMRs. We performed a preliminary evaluation on the utility of the UMLS ADE data. In conclusion, we have built an ADEpedia 2.0 framework that intends to integrate known ADE knowledge from disparate sources. The UMLS is a useful source for providing standardized ADE knowledge relevant to indications, contraindications and adverse effects, and complementary to the ADE data from drug product labels. The statistics from EMRs would enable the meaningful use of ADE data for drug safety surveillance.

In this paper, we introduce our case studies for representing clinical study meta-data models such as the HL7 Detailed Clinical Models (DCMs) and the ISO11179 model in a framework that is based on the Semantic-Web technology. We consider such a harmonization would provide computable semantics of the models, thus facilitate the model reuse, model harmonization and data integration.1.

Semantic interoperability among terminologies, data elements, and information models is fundamental and critical for sharing information from the scientific bench to the clinical bedside and back among systems. To meet this need, the vision for CDISC is to build a global, accessible electronic library, which enables precise and standardized data element definitions that can be used in applications and studies to improve biomedical research and its link with health care. As a pilot study, we propose a representation and harmonization framework for clinical study data elements and implement a prototype CDISC Shared Health and Research Electronic Library (CSHARE) using Semantic MediaWiki. We report the preliminary observations of how the components worked and the lessons learnt. In summary, the wiki provided a useful prototyping tool from a process standpoint.