Search

Background Medical information management for stroke patients is currently a very time-consuming endeavor. There are clear guidelines and procedures to treat patients having acute stroke, but it is not known how well these established practices are reflected in patient documentation. Objective This study compares a variety of documentation processes regarding stroke. The main objective of this work is to provide an overview of the most commonly occurring medical concepts in stroke documentation and identify overlaps between different documentation contexts to allow for the definition of a core data set that could be used in potential data interfaces. Methods Medical source documentation forms from different documentation contexts, including hospitals, clinical trials, registries, and international standards, regarding stroke treatment followed by rehabilitation were digitized in the operational data model. Each source data element was semantically annotated using the Unified Medical Language System. The concept codes were analyzed for semantic overlaps. A concept was considered common if it appeared in at least two documentation contexts. The resulting common concepts were extended with implementation details, including data types and permissible values based on frequent patterns of source data elements, using an established expert-based and semiautomatic approach. Results In total, 3287 data elements were identified, and 1051 of these emerged as unique medical concepts. The 100 most frequent medical concepts cover 9.51% (100/1051) of all concept occurrences in stroke documentation, and the 50 most frequent concepts cover 4.75% (50/1051). A list of common data elements was implemented in different standardized machine-readable formats on a public metadata repository for interoperable reuse. Conclusions Standardization of medical documentation is a prerequisite for data exchange as well as the transferability and reuse of data. In the long run, standardization would save time and money and extend the capabilities for which such data could be used. In the context of this work, a lack of standardization was observed regarding current information management. Free-form text fields and intricate questions complicate automated data access and transfer between institutions. This work also revealed the potential of a unified documentation process as a core data set of the 50 most frequent common data elements, accounting for 34% of the documentation in medical information management. Such a data set offers a starting point for standardized and interoperable data collection in routine care, quality management, and clinical research.

The Portal of Medical Data Models has been developed since 2011 by the University of Münster. Its main goals are transparency, standardization and secondary use of medical metadata. Via two online surveys feedback from stakeholders of German health research was collected regarding the portal's contents. The surveys confirmed great interest in secondary use of medical forms.

The current draft of the German Hospital Structure Law requires remuneration to incorporate quality indicators. For neurosurgery, several quality indicators have been discussed, such as 30-day readmission, reoperation, or mortality rates; the rates of infections; or the length of stay. When comparing neurosurgical departments regarding these indicators, very heterogeneous patient spectrums complicate benchmarking due to the lack of risk adjustment. In this study, we performed an analysis of quality indicators and possible risk adjustment, based only on administrative data. All adult patients that were treated as inpatients for a brain or spinal tumour at our neurosurgical department between 2013 and 2017 were assessed for the abovementioned quality indicators. DRG-related data such as relative weight, PCCL (patient clinical complexity level), ICD-10 major diagnosis category, secondary diagnoses, age and sex were obtained. The age-adjusted Charlson Comorbidity Index (CCI) was calculated. Logistic regression analyses were performed in order to correlate quality indicators with administrative data. Overall, 2623 cases were enrolled into the study. Most patients were treated for glioma (n = 1055, 40.2%). The CCI did not correlate with the quality indicators, whereas PCCL showed a positive correlation with 30-day readmission and reoperation, SSI and nosocomial infection rates. All previously discussed quality indicators are easily derived from administrative data. Administrative data alone might not be sufficient for adequate risk adjustment as they do not reflect the endogenous risk of the patient and are influenced by certain complications during inpatient stay. Appropriate concepts for risk adjustment should be compiled on the basis of prospectively designed registry studies.

The aim of the study was to determine pre-operative factors associated with adverse events occurring within 30 days after neurosurgical tumor treatment in a German center, adjusting for their incidence in order to prospectively compare different centers. Adult patients that were hospitalized due to a benign or malignant brain were retrospectively assessed for quality indicators and adverse events. Analyses were performed in order to determine risk factors for adverse events and reasons for readmission and reoperation. A total of 2511 cases were enrolled. The 30 days unplanned readmission rate to the same hospital was 5.7%. The main reason for readmission was tumor progression. Every 10th patient had an unplanned reoperation. The incidence of surgical revisions due to infections was 2.3%. Taking together all monitored adverse events, male patients had a higher risk for any of these complications (OR 1.236, 95%CI 1.025–1.490, p = 0.027). Age, sex, and histological diagnosis were predictors of experiencing any complication. Adjusted by incidence, the increased risk ratios greater than 10.0% were found for male sex, age, metastatic tumor, and hemiplegia for various quality indicators. We found that most predictors of outcome rates are based on preoperative underlying medical conditions and are not modifiable by the surgeon. Comparing our results to the literature, we conclude that differences in readmission and reoperation rates are strongly influenced by standards in decision making and that comparison of outcome rates between different health-care providers on an international basis is challenging. Each health-care system has to develop own metrics for risk adjustment that require regular reassessment.

INTRODUCTION: Molecular subgrouping of Medulloblastoma (MB) has expanded our understanding of its biology and the impact on clinical parameters. However, detailed analysis of inter- and intratumoral heterogeneity on a metabolic level is currently lacking. Within this study, we aimed at improving our understanding of metabolic heterogeneity between the MB subgroups, between samples within these subgroups and how these differences affect prognosis. METHODS: We analyzed metabolic characteristics of four MB cohorts covering 1,804 samples in total. In 911 samples (ICGC and MAGIC cohort), we explored metabolic programs on RNA level. In two cohorts (ICGC and G3/G4 samples from the HIT cohort; n=1,035) we examined genetic alterations on DNA level. Furthermore, single-cell RNA-sequencing data of six samples were used to explore intratumoral metabolic heterogeneity. Inter- and intratumoral heterogeneity were correlated to clinical data. RESULTS: Using publicly available gene signatures, we discovered significant differences in metabolic gene expression comparing established MB subgroups. Three metabolically distinct clusters of G3/G4 samples could be defined by unsupervised analyses in two independent cohorts. We were able to confirm our finding of intertumoral metabolic differences on single-cell RNA level. Additionally, our analysis revealed the possibility of sample-specific metabolic features. On DNA level, we identified regulatory genes with known role in MB development to be predominantly associated with lipid metabolic processes. After all, lipid metabolism and metabolism of nucleotides in MB have prognostic value and correlate with the outcome of patients. CONCLUSION: Our data highlight the importance of metabolic properties in MB. We show the distinct metabolic signatures are clinically relevant and, thus, might provide opportunities for novel target-directed therapeutic options in the future.

Smartwatches provide technology-based assessments in Parkinson’s disease (PD). It is necessary to evaluate their reliability and accuracy in order to include those devices in an assessment. We present unique results for sensor validation and disease classification via machine learning (ML). A comparison setup was designed with two different series of Apple smartwatches, one Nanometrics seismometer and a high-precision shaker to measure tremor-like amplitudes and frequencies. Clinical smartwatch measurements were acquired from a prospective study including 450 participants with PD, differential diagnoses (DD) and healthy participants. All participants wore two smartwatches throughout a 15-min examination. Symptoms and medical history were captured on the paired smartphone. The amplitude error of both smartwatches reaches up to 0.005 g, and for the measured frequencies, up to 0.01 Hz. A broad range of different ML classifiers were cross-validated. The most advanced task of distinguishing PD vs. DD was evaluated with 74.1% balanced accuracy, 86.5% precision and 90.5% recall by Multilayer Perceptrons. Deep-learning architectures significantly underperformed in all classification tasks. Smartwatches are capable of capturing subtle tremor signs with low noise. Amplitude and frequency differences between smartwatches and the seismometer were under the level of clinical significance. This study provided the largest PD sample size of two-hand smartwatch measurements and our preliminary ML-evaluation shows that such a system provides powerful means for diagnosis classification and new digital biomarkers, but it remains challenging for distinguishing similar disorders.

Smartwatches provide technology-based assessments in Parkinson’s disease (PD). We present results for sensor validation and disease classification via Machine Learning (ML). A comparison setup was designed with two different series of Apple smartwatches, one Nanometrics seismometer and a high-precision shaker to measure tremor-like amplitudes and frequencies. Clinical smartwatch measurements were acquired from a prospective study including 450 participants with PD, differential diagnoses (DD) and healthy participants. All participants wore two smartwatches and within a 15-min examination. Symptoms and medical history were captured on the paired smartphone. A broad range of different ML classifiers were cross-validated. Amplitude and frequency differences between smartwatches and the seismometer were under the level of clinical significance. The most advanced task of distinguishing PD vs DD was evaluated with 74,1% balanced accuracy, 86,5% precision and 90,5% recall by Multilayer Perceptrons. Deep Learning architectures significantly underperformed in all classification tasks. Smartwatches are capable of capturing subtle-tremor signs with low noise. This study provided the largest PD sample size of two-hand smartwatch measurements and our preliminary ML-evaluation shows that such a system provides powerful means for diagnosis classification and new digital biomarkers but it remains challenging for distinguishing similar disorders.

BACKGROUND Medical information management for stroke patients is currently a very time-consuming endeavor. There are clear guidelines and procedures to treat patients having acute stroke, but it is not known how well these established practices are reflected in patient documentation. OBJECTIVE This study compares a variety of documentation processes regarding stroke. The main objective of this work is to provide an overview of the most commonly occurring medical concepts in stroke documentation and identify overlaps between different documentation contexts to allow for the definition of a core data set that could be used in potential data interfaces. METHODS Medical source documentation forms from different documentation contexts, including hospitals, clinical trials, registries, and international standards, regarding stroke treatment followed by rehabilitation were digitized in the operational data model. Each source data element was semantically annotated using the Unified Medical Language System. The concept codes were analyzed for semantic overlaps. A concept was considered common if it appeared in at least two documentation contexts. The resulting common concepts were extended with implementation details, including data types and permissible values based on frequent patterns of source data elements, using an established expert-based and semiautomatic approach. RESULTS In total, 3287 data elements were identified, and 1051 of these emerged as unique medical concepts. The 100 most frequent medical concepts cover 9.51% (100/1051) of all concept occurrences in stroke documentation, and the 50 most frequent concepts cover 4.75% (50/1051). A list of common data elements was implemented in different standardized machine-readable formats on a public metadata repository for interoperable reuse. CONCLUSIONS Standardization of medical documentation is a prerequisite for data exchange as well as the transferability and reuse of data. In the long run, standardization would save time and money and extend the capabilities for which such data could be used. In the context of this work, a lack of standardization was observed regarding current information management. Free-form text fields and intricate questions complicate automated data access and transfer between institutions. This work also revealed the potential of a unified documentation process as a core data set of the 50 most frequent common data elements, accounting for 34% of the documentation in medical information management. Such a data set offers a starting point for standardized and interoperable data collection in routine care, quality management, and clinical research.

Background: With increasing numbers of patients recovering from COVID-19, there is increasing evidence for persisting symptoms and the need for follow-up studies. Methods: This retrospective study included patients without comorbidities, who recovered from COVID-19 and attended an outpatient clinic at a university hospital for follow-up care and potential convalescent plasma donation. Network analysis was applied to visualize symptom combinations and persistent symptoms. Comprehensive lab-testing was ascertained at each follow-up to analyze differences regarding patients with vs without persistent symptoms. Results: 116 patients were included, age range was 18-69 years (median: 41) with follow-ups ranging from 22 to 102 days. The three most frequent persisting symptoms were Fatigue (54%), Dyspnea (29%) and Anosmia (25%). Lymphopenia was present in 13 of 112 (12%) cases. Five of 35 cases (14%) had Lymphopenia in the later follow-up range of 80-102 days. Serum IgA concentration was the only lab parameter with significant difference between patients with vs without persistent symptoms with reduced serum IgA concentrations in the patient cohort of persisting symptoms (p=0.0219). Conclusion: Lymphopenia persisted in a noticeable percentage of recovered patients. Moreover, higher serum IgA concentration is associated with a positive effect regarding symptom persistence.

BACKGROUND Metadata are created to describe the corresponding data in a detailed and unambiguous way and is used for various applications in different research areas, for example, data identification and classification. However, a clear definition of metadata is crucial for further use. Unfortunately, extensive experience with the processing and management of metadata has shown that the term “metadata” and its use is not always unambiguous. OBJECTIVE This study aimed to understand the definition of metadata and the challenges resulting from metadata reuse. METHODS A systematic literature search was performed in this study following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines for reporting on systematic reviews. Five research questions were identified to streamline the review process, addressing metadata characteristics, metadata standards, use cases, and problems encountered. This review was preceded by a harmonization process to achieve a general understanding of the terms used. RESULTS The harmonization process resulted in a clear set of definitions for metadata processing focusing on data integration. The following literature review was conducted by 10 reviewers with different backgrounds and using the harmonized definitions. This study included 81 peer-reviewed papers from the last decade after applying various filtering steps to identify the most relevant papers. The 5 research questions could be answered, resulting in a broad overview of the standards, use cases, problems, and corresponding solutions for the application of metadata in different research areas. CONCLUSIONS Metadata can be a powerful tool for identifying, describing, and processing information, but its meaningful creation is costly and challenging. This review process uncovered many standards, use cases, problems, and solutions for dealing with metadata. The presented harmonized definitions and the new schema have the potential to improve the classification and generation of metadata by creating a shared understanding of metadata and its context.

Consumer wearables can provide objective monitoring of movement disorders and may identify new phenotypical biomarkers. We present a novel smartwatch-based prototype, which is implemented as a prospective study in neurology. A full-stack Machine Learning pipeline utilizing Artificial Neural Networks (ANN), Random Forests and Support Vector Machines (SVM) was established and optimized to train for two clinically relevant classification tasks: First, to distinguish neurodegenerative movement disorders such as Parkinson's Disease (PD) or Essential Tremor from healthy subjects. Second, to distinguish specifically PD from other movement disorders or healthy subjects. The system was trained by 318 samples, including 192 PD, 75 other movement disorders and 51 healthy participants. All models were trained and tested with hyperparameter optimization and nested cross-validation. Regarding the more general first task, the ANN succeeded best with precision of 0.94 (SD 0.03) and recall of 0.92 (SD 0.04). Concerning the more specific second task, the SVM performed best with precision of 0.81 (SD 0.01) and recall of 0.89 (SD 0.04). These preliminary results are promising as compared to the literature-reported diagnostic accuracy of neurologists. In addition, a new data foundation with highly structured and clinically annotated acceleration data was established, which enables future biomarker analyses utilizing consumer devices in movement disorders.

Background: Artificial intelligence (AI) applications that utilize machine learning are on the rise in clinical research and provide highly promising applications in specific use cases. However, wide clinical adoption remains far off. This review reflects on common barriers and current solution approaches. Summary: Key challenges are abbreviated as the RISE criteria: Regulatory aspects, Interpretability, interoperability, and the need for Structured data and Evidence. As reoccurring barriers of AI adoption, these concepts are delineated and complemented by points to consider and possible solutions for effective and safe use of AI applications. Key Messages: There is a fraction of AI applications with proven clinical benefits and regulatory approval. Many new promising systems are the subject of current research but share common issues for wide clinical adoption. The RISE criteria can support preparation for challenges and pitfalls when designing or introducing AI applications into clinical practice.

StudyPortal was implemented as the first multilingual search platform for geographic visualization of clinical trials and scientific articles. The platform queries information from ClinicalTrials.gov, PubMed, a geodatabase and geographic maps to enable geospatial study search and real-time rendering of study locations or research networks on a map. Thus, disease-specific clinical studies or whole research networks can be shown in a geographic proximity. Moreover, a semantic layer enables multilingual disease input and autosuggestion of medical terms based on the Unified Medical Language System. The portal is accessible on https://studyportal.uni-muenster.de. This paper presents details on implementation of the novel search platform, its search evaluation and future work.

ObjectivesTo systematically classify the clinical impact of computerized clinical decision support systems (CDSSs) in inpatient care.Materials and MethodsMedline, Cochrane Trials, and Cochrane Reviews were searched for CDSS studies that assessed patient outcomes in inpatient settings. For each study, 2 physicians independently mapped patient outcome effects to a predefined medical effect score to assess the clinical impact of reported outcome effects. Disagreements were measured by using weighted kappa and solved by consensus. An example set of promising disease entities was generated based on medical effect scores and risk of bias assessment. To summarize technical characteristics of the systems, reported input variables and algorithm types were extracted as well.ResultsSeventy studies were included. Five (7%) reported reduced mortality, 16 (23%) reduced life-threatening events, and 28 (40%) reduced non–life-threatening events, 20 (29%) had no significant impact on patient outcomes, and 1 showed a negative effect (weighted κ: 0.72, P Discussion and ConclusionMost of the included CDSS studies were associated with positive patient outcomes effects but with substantial differences regarding the clinical impact. A subset of 6 disease entities could be filtered in which CDSS should be given special consideration at sites where computer-assisted decision-making is deemed to be underutilized.Registration number on PROSPERO: CRD42016049946.

Compatible data models are key for data integration. Data transformation after data collection has many limitations. Therefore compatible data structures should be addressed already during the design of information systems. The portal of Medical Data Models (MDM), which contains 20.000+ models and 495.000+ data items, was enhanced with a web service to identify data elements, which are frequently collected together in real information systems. Using Apache Solr, a fast search functionality to identify those elements with semantic annotations was implemented. This service was integrated into the metadata registry (MDR) component of MDM to make it available to the scientific community. It can be used to build intelligent data model editors, which suggest and import frequent data element definitions according to the current medical context.

International trial databases as ClinicalTrials.gov or the EU Clinical Trials Register lack geographic visualization of clinical trials. Utilizing key requirements from patient support groups and clinical researchers, an interactive online platform called StudyPortal was designed. It enables patients, health providers and clinical researchers to find and localize suitable studies or whole research networks for selected diseases in a geographic proximity. A semantic layer enables multilingual disease input and autosuggestion. Trial information is pulled and processed from ClinicalTrials.gov. In addition, author affiliations of disease-related PubMed articles are retrievable in order to boost sensitivity of visualized research networks. The integration of a geodatabase and maps enables access to geospatial study search and visualization. A preliminary implementation of the platform is already accessible on the web: https://studyportal.uni-muenster.de. It showed that over 70% of trials and over 90% of scientific articles are visualized correctly by applying expert review and using Web of Science and the WHO trial database as external sources. Publication and trial-registration bias are significant issues that limit completeness of visualization. ClinicalTrials.gov, MEDLINE and geomaps are well-maintained but disconnected sources. StudyPortal integrates these sources to render a novel geospatial view of regional or global clinical research landscapes of US and European trials in real-time. Future work will focus on extensive search filters for recruitment status and intervention characteristics.

The Portal of Medical Data Models is an open-access platform for medical forms and data models. Annotation with UMLS codes enables semantic interoperability and secondary use of data. The number of forms and users are growing. The site has been updated and two analyzing tools have been added.

BACKGROUND Standardization in clinical documentation can increase efficiency and can save time and resources. OBJECTIVE The objectives of this work are to compare documentation forms for acute coronary syndrome (ACS), check for standardization, and generate a list of the most common data elements using semantic form annotation with the Unified Medical Language System (UMLS). METHODS Forms from registries, studies, risk scores, quality assurance, official guidelines, and routine documentation from four hospitals in Germany were semantically annotated using UMLS. This allowed for automatic comparison of concept frequencies and the generation of a list of the most common concepts. RESULTS A total of 3710 forms items from 86 sources were semantically annotated using 842 unique UMLS concepts. Half of all medical concept occurrences were covered by 60 unique concepts, which suggests the existence of a core dataset of relevant concepts. Overlap percentages between forms were relatively low, hinting at inconsistent documentation structures and lack of standardization. CONCLUSIONS This analysis shows a lack of standardized and semantically enriched documentation for patients with ACS. Efforts made by official institutions like the European Society for Cardiology have not yet been fully implemented. Utilizing a standardized and annotated core dataset of the most important data concepts could make export and automatic reuse of data easier. The generated list of common data elements is an exemplary implementation suggestion of the concepts to use in a standardized approach.

BACKGROUND For cancer domains such as acute myeloid leukemia (AML), a large set of data elements is obtained from different institutions with heterogeneous data definitions within one patient course. The lack of clinical data harmonization impedes cross-institutional electronic data exchange and future meta-analyses. OBJECTIVE This study aimed to identify and harmonize a semantic core of common data elements (CDEs) in clinical routine and research documentation, based on a systematic metadata analysis of existing documentation models. METHODS Lists of relevant data items were collected and reviewed by hematologists from two university hospitals regarding routine documentation and several case report forms of clinical trials for AML. In addition, existing registries and international recommendations were included. Data items were coded to medical concepts via the Unified Medical Language System (UMLS) by a physician and reviewed by another physician. On the basis of the coded concepts, the data sources were analyzed for concept overlaps and identification of most frequent concepts. The most frequent concepts were then implemented as data elements in the standardized format of the Operational Data Model by the Clinical Data Interchange Standards Consortium. RESULTS A total of 3265 medical concepts were identified, of which 1414 were unique. Among the 1414 unique medical concepts, the 50 most frequent ones cover 26.98% of all concept occurrences within the collected AML documentation. The top 100 concepts represent 39.48% of all concepts’ occurrences. Implementation of CDEs is available on a European research infrastructure and can be downloaded in different formats for reuse in different electronic data capture systems. CONCLUSIONS Information management is a complex process for research-intense disease entities as AML that is associated with a large set of lab-based diagnostics and different treatment options. Our systematic UMLS-based analysis revealed the existence of a core data set and an exemplary reusable implementation for harmonized data capture is available on an established metadata repository.

Background For cancer domains such as acute myeloid leukemia (AML), a large set of data elements is obtained from different institutions with heterogeneous data definitions within one patient course. The lack of clinical data harmonization impedes cross-institutional electronic data exchange and future meta-analyses. Objective This study aimed to identify and harmonize a semantic core of common data elements (CDEs) in clinical routine and research documentation, based on a systematic metadata analysis of existing documentation models. Methods Lists of relevant data items were collected and reviewed by hematologists from two university hospitals regarding routine documentation and several case report forms of clinical trials for AML. In addition, existing registries and international recommendations were included. Data items were coded to medical concepts via the Unified Medical Language System (UMLS) by a physician and reviewed by another physician. On the basis of the coded concepts, the data sources were analyzed for concept overlaps and identification of most frequent concepts. The most frequent concepts were then implemented as data elements in the standardized format of the Operational Data Model by the Clinical Data Interchange Standards Consortium. Results A total of 3265 medical concepts were identified, of which 1414 were unique. Among the 1414 unique medical concepts, the 50 most frequent ones cover 26.98% of all concept occurrences within the collected AML documentation. The top 100 concepts represent 39.48% of all concepts’ occurrences. Implementation of CDEs is available on a European research infrastructure and can be downloaded in different formats for reuse in different electronic data capture systems. Conclusions Information management is a complex process for research-intense disease entities as AML that is associated with a large set of lab-based diagnostics and different treatment options. Our systematic UMLS-based analysis revealed the existence of a core data set and an exemplary reusable implementation for harmonized data capture is available on an established metadata repository.

OBJECT: The aim of the study was to determine pre-operative factors associated with adverse events occurring within 30 days after neurosurgical tumor treatment. METHODS: All adult patients that were hospitalized due to a benign or malignant brain tumor in our department between 2013 and 2017 were retrospectively assessed for quality indicators and adverse events. Administrative data were obtained, the age-adjusted Charlson Comorbidity Index (CCI) was calculated and logistic regression analyses were performed to determine risk factors for adverse events. Detailed analyses of all readmissions and reoperations were performed. RESULTS: Overall, 2511 cases were enrolled. The most common diagnosis was glioma (42%). The 30-days unplanned readmission rate was 5.7%. The main reason for readmission was tumor progression. Every 10(th)patient had an unplanned reoperation. The incidence of surgical revisions due to infections was 2.3%. Taking together all monitored adverse events, male patients had a higher risk for any of these complications (OR: 1.236, 95%CI: 1.025–1.490, p=0.027). Age, sex and histological diagnosis were predictors of experiencing any complication. The CCI was significantly higher in patients that were readmitted within 30 days (p= 0.045) and correlated with a higher 30-days mortality rate (p< 0.001), a higher nosocomial infection rate (p< 0.001), a higher SSI rate (p= 0.027) and a longer length of stay (p< 0.001) in univariate analysis. CONCLUSIONS: We found that most predictors of adverse events and outcome rates are based on preoperative underlying medical conditions of the patient and are not modifiable by the surgeon. Comparing our results to the literature, we conclude that differences in readmission and reoperation rates are strongly influenced by standards in surgical decision making and that comparison of outcome rates between different health-care providers on an international basis is challenging. Each health-care system has to develop own metrics for risk adjustment and assessment of outcome variables, that require regular reassessment.

Annotation with semantic codes helps to overcome interoperability issues for electronic documentation in medicine. However, the manual annotation process is laborious and semantic codes are ambiguous. We developed a publicly accessible web service to alleviate these drawbacks with a sophisticated and fast search mechanism based on more than 330,000 semantic code suggestions. These suggestions are derived from semantically annotated data elements contained in the Portal of Medical Data Models manually curated by medical professionals. Integrating this suggestion service can support the manual annotation process and strengthen uniform coding. Integration is demonstrated for two separate data model editors. Usage statistics show over 5,500 suggestion requests per month for semantic annotation of items. The web service may also prove helpful for automatic semantic coding.

Julian Varghese,1 Michael Fujarski,2 Stefan Hegselmann,1 Philipp Neuhaus,1 Martin Dugas1,3 1Institute of Medical Informatics, University of Münster, 2Faculty of Mathematics and Computer Sciences, University of Münster, 3Institute of Medical Informatics, European Research Center for Information Systems (ERCIS), Münster, Germany Objective: Best-practice data models harmonize semantics and data structure of medical variables in clinical or epidemiological studies. While there exist several published data sets, it remains challenging to find and reuse published eligibility criteria or other data items that match specific needs of a newly planned study or registry. A novel Internet-based method for rapid comparison of published data models was implemented to enable reuse, customization, and harmonization of item catalogs for the early planning and development phase of research databases. Methods: Based on prior work, a European information infrastructure with a large collection of medical data models was established. A newly developed analysis module called CDEGenerator provides systematic comparison of selected data models and user-tailored creation of minimum data sets or harmonized item catalogs. Usability was assessed by eight external medical documentation experts in a workshop by the umbrella organization for networked medical research in Germany with the System Usability Scale. Results: The analysis and item-tailoring module provides multilingual comparisons of semantically complex eligibility criteria of clinical trials. The System Usability Scale yielded “good usability” (mean 75.0, range 65.0–92.5). User-tailored models can be exported to several data formats, such as XLS, REDCap or Operational Data Model by the Clinical Data Interchange Standards Consortium, which is supported by the US Food and Drug Administration and European Medicines Agency for metadata exchange of clinical studies. Conclusion: The online tool provides user-friendly methods to reuse, compare, and thus learn from data items of standardized or published models to design a blueprint for a harmonized research database. Keywords: common data elements, semantic interoperability, metadata repositories, Unified Medical Language System

CDISC's Operational Data Model (ODM) is a flexible standard for exchanging and archiving metadata and subject clinical data in clinical trials. The Portal of Medical Data Models (MDM-Portal) uses ODM to store more than 15000 medical forms. As not every electronic health system accepts ODM as input format, there is a need for conversion between ODM and other data standards and formats. This research proposes a standardised template-based process to develop ODM converters. So far, ten converters have been developed and integrated in the MDM-Portal following this process and new ones should be included soon. The template, programming utilities and an ODM test suite have been made online available and can be used to easily develop new converters.

Computerized Clinical Decision Support Systems (CDSS) are implemented in hospitals to improve prevention of Venous Thromboembolisms (VTE). A physician-driven review was conducted to assess extent of patient outcome effects of recently published CDSS studies. To facilitate future re-implementations within existing hospital information systems, input variables of included systems were extracted, standardized and annotated with semantic codes. Item category coverages of the different systems were then compared. 73% of studies showed positive medical effect. Of these, 53% showed strong positive medical effect by reducing incidence of deep vein thromboses and pulmonary embolisms. Outcome-improving systems tend to cover more item categories. A broad set of clinically relevant input variables should be taken into account or reused from the electronic health record if considering CDSS implementation. Input data models are provided for download in different standardized formats. Site-specific organizational factors that determine how systems are introduced, implemented and tested are also crucial for success.

To address current key problems of medical documentation: lack of transparency, overwhelming amount of medical contents to be documented and missing interoperability, the Portal of Medical Data Models (http://medical-data-models.org/) was established in 2012. Constantly evolving, four years later, the portal displays more than 8900 medical data models with more than 250000 items, of which 84 % have been semantically annotated with UMLS codes to support interoperability. Giving an update on new functions and contents of the portal, two additional export formats have been implemented, allowing the reuse of forms such as HL7's framework Fast Health Interoperability Resources (FHIR) Questionnaires, as well as the OpenDataKit format. Future projects include the implementation of an ODMtoOpenClinica converter, as well as supporting the reuse of forms with Apple's ResearchKit and Android's ResearchStack.

Motivation The application of next-generation sequencing in research and particularly in clinical routine requires valid variant calling results. However, evaluation of several commonly used tools has pointed out that not a single tool meets this requirement. False positive as well as false negative calls necessitate additional experiments and extensive manual work. Intelligent combination and output filtration of different tools could significantly improve the current situation. Results We developed appreci8, an automatic variant calling pipeline for calling single nucleotide variants and short indels by combining and filtering the output of eight open-source variant calling tools, based on a novel artifact- and polymorphism score. Appreci8 was trained on two data sets from patients with myelodysplastic syndrome, covering 165 Illumina samples. Subsequently, appreci8’s performance was tested on five independent data sets, covering 513 samples. Variation in sequencing platform, target region and disease entity was considered. All calls were validated by re-sequencing on the same platform, a different platform or expert-based review. Sensitivity of appreci8 ranged between 0.93 and 1.00, while positive predictive value ranged between 0.65 and 1.00. In all cases, appreci8 showed superior performance compared to any evaluated alternative approach. Availability and implementation Appreci8 is freely available at https://hub.docker.com/r/wwuimi/appreci8/. Sequencing data (BAM files) of the 678 patients analyzed with appreci8 have been deposited into the NCBI Sequence Read Archive (BioProjectID: 388411; https://www.ncbi.nlm.nih.gov/bioproject/PRJNA388411). Supplementary information Supplementary data are available at Bioinformatics online.

BACKGROUND Medical coding is essential for standardized communication and integration of clinical data. The Unified Medical Language System by the National Library of Medicine is the largest clinical terminology system for medical coders and natural language processing tools. However, abundance of ambiguous codes leads to low rates of uniform coding among different coders. OBJECTIVE To measure uniform coding among different medical experts in terms of inter-rater reliability (IR) and analyze the effect on IR by using an expert-based online code suggestion system. METHODS A quasi-experimental study was conducted. Six medical experts coded 602 medical items from structured quality assurance forms (QA) or free-text eligibility criteria (EC) of 20 different clinical trials. Medical item content was selected based on mortality-leading diseases according to WHO data. The intervention consisted of using a semi-automatic code suggestion tool that is linked to a European information infrastructure providing a large medical text corpus of more than 300,000 medical form items with expert-assigned semantic codes. Krippendorff’s alpha (Kalpha) with bootstrap analysis was used for IR analysis and coding times were measured before and after intervention. RESULTS The intervention improved IR in structured QA form items (from Kalpha= 0.50, 95%-CI [0.43-0.57] to Kalpha = 0.62 [0.55-0.69]) and free-text eligibility criteria (from Kalpha = 0.19 [0.14-0.24] to Kalpha = 0.43 [0.37-0.50]) while preserving or slightly reducing mean coding time per item for all six coders. Regardless of intervention, pre-coordination and structured items were associated with significant higher IR, but the proportion of items that were pre-coordinated significantly increased after intervention (EC: Odds ratio: 4.92 [2.78 - 8.72]; QA: Odds ratio: 1.96 [1.19-3.25]). CONCLUSIONS The online code suggestion mechanism improved IR towards moderate or even substantial inter coder agreement. Pre-coordination and use of structured vs. free-text data elements are key drivers for higher IR.

Summary Background: Eligibility criteria (EC) of clinical trials play a key role in selecting appropriate study candidates and the validity of the outcome of a clinical trial. However, in most cases EC are provided in unstandardised ways such as free text, which raises significant challenges for machine-readability. Objectives: To establish a list of most frequent medical concepts in clinical trials with semantic annotations. This concept list contributes to standardisation of EC and identifies relevant data items in electronic health records (EHRs) for clinical research. The coverage of the list in two major clinical vocabularies, MeSH and SNOMED-CT, will be assessed. Methods: Four hundred and twenty-fivec linical trials conducted between 2000 and 2011 at a German university hospital were analysed. 6671 EC were manually annotated by a medical coder using Concept Unique Identifiers (CUIs) provided by the Unified Medical Language System. Two physicians performed a semi-automatic CUI code revision. Concept frequency was analysed and clusters of concepts were manually identified.A binomial significance test was applied to quantify coverage differences of the most frequent concepts in MeSH and SNOMED-CT. Results: Based on manual medical coding of 425 clinical trials, 7588 concepts were identified, of which 5236 were distinct. A top 100 list containing 101 most frequent medical concepts was established. The concepts of this list cover 25 % of all concept occur-rences in all analysed clinical trials. This list reveals six missing entries in SNOMED-CT, 12 in MeSH. The median of EC frequency per trial has increased throughout the trial years (2000 –2005: 8 EC/trial, 2011: 14 EC/ trial). Conclusions: Relatively few concepts cover one quarter of concept occurrences that represent EC in recent studies. Therefore, these concepts can serve as candidate data elements for integration into EHRs to optimise patient recruitment in clinical research.

Data standards consisting of key data elements for clinical routine and trial documentation harmonize documentation within and across different health care institutions making documentation more efficient and improving scientific data analysis. This work focusses on the field of myeloid leukemia (ML), where a semantic core of common data elements (CDEs) in routine and trial documentation is established by automatic UMLS-based form analysis of existing documentation models. These CDEs (n = 227) were initially reviewed and commented by leukemia experts before they were systematically surveyed by an international voting process through seven hematologists of four countries. The total agreement score was 86%. 116 elements (51%) of these share an agreement score of 100%. This work generated CDEs with language-independent semantic codes and international clinical expert review to build a first approach towards an international data standard for ML. A first version of the CDE list is implemented in the data standard Operational Data Model and additional other data formats for reuse in different medical information systems.

The volume and complexity of patient data – especially in personalised medicine – is steadily increasing, both regarding clinical data and genomic profiles: Typically more than 1,000 items (e.g., laboratory values, vital signs, diagnostic tests etc.) are collected per patient in clinical trials. In oncology hundreds of mutations can potentially be detected for each patient by genomic profiling. Therefore data integration from multiple sources constitutes a key challenge for medical research and healthcare. Semantic annotation of data elements can facilitate to identify matching data elements in different sources and thereby supports data integration. Millions of different annotations are required due to the semantic richness of patient data. These annotations should be uniform, i.e., two matching data elements shall contain the same annotations. However, large terminologies like SNOMED CT or UMLS don’t provide uniform coding. It is proposed to develop semantic annotations of medical data elements based on a large-scale public metadata repository. To achieve uniform codes, semantic annotations shall be re-used if a matching data element is available in the metadata repository. A web-based tool called ODMedit ( https://odmeditor.uni-muenster.de/ ) was developed to create data models with uniform semantic annotations. It contains ~800,000 terms with semantic annotations which were derived from ~5,800 models from the portal of medical data models (MDM). The tool was successfully applied to manually annotate 22 forms with 292 data items from CDISC and to update 1,495 data models of the MDM portal. Uniform manual semantic annotation of data models is feasible in principle, but requires a large-scale collaborative effort due to the semantic richness of patient data. A web-based tool for these annotations is available, which is linked to a public metadata repository.

Electric double layer capacitors Mesoporous electrode morphology Specific area capacitance Diffuse layer Stern layer a b s t r a c t This paper aims to numerically predict the capacitance of electric double layer capacitors (EDLCs) made of mesoporous electrodes consisting of closely packed monodispersed mesoporous carbon spheres in (C2H5)4NBF4/propylene carbonate electrolyte. The model faithfully accounts for the electrode packing morphology and the dependency of electrolyte dielectric permittivity on local electric field. Three sphere packing morphologies were investigated, namely, simple cubic (SC), body-centered cubic (BCC), and facecentered cubic (FCC). A cylindrical mesopore in a mesoporous carbon sphere was also simulated. This study demonstrates that the field-dependent electrolyte dielectric permittivity significantly affects the predicted capacitance of EDLCs. Moreover, the Stern layer needs to be accounted for in order to match predicted specific area capacitance with experimental data. This study also establishes that, for all packing structures, larger sphere diameter results in larger electric field at the electrode surface and thus larger diffuse layer specific area capacitance. For sphere diameter less than 40 nm, SC packing had the largest electrolyte volume fraction. This provided more space for the electric potential to decrease resulting in larger electric field at the electrode surface and diffuse layer specific area capacitance compared with BCC and FCC packings. On the contrary, FCC features the smallest volume fraction resulting in the lowest surface electric field and diffuse layer specific area capacitance. The packing morphologies of electrode spheres were found to have no significant effect on the diffuse layer specific area capacitance for sphere diameter larger than 40 nm.

Modeling strategies were developed in order to reduce the computational effort required to simulate oxidation in textile composites. The oxidation behavior in heterogeneous configurations was found to be complex and dependent on a number of factors. A decoupled subdomain strategy for analyzing textile composites was evaluated and used to simulate the oxidation behavior in a graphite/PMR-15 plain-weave composite.

Many years ago, free edge effects were studied extensively, perhaps excessively, for ordinary tape laminate composites. Various techniques were used to show that the free edge effects extended only a short distance from the free edge and that stacking sequence had a significant impact on interlaminar stresses, which could cause premature failure. In contrast, the analytical study of woven composites has focused on periodic analysis of unit cells. Since such analyses assume an infinite array of unit cells, free edge effects were not considered. This article investigates the significance of traction free surfaces on stresses in woven composites. Effects due to both free edge and free lateral surfaces (due to finite thickness) were investigated. The significance of the free edge effect was found to be sensitive to the waviness of the plain weave mat. Also, the stress distribution is quite different for finite and infinitely thick stacks of woven mats.

This paper describes an approach to determine effective oxidation mat erial properties for a unidirectional laminate or tow. The effective properties will ultimately be used to model the oxidation behavior of textile composites. The overall goal of this research effort is to simulate the effect of high temperature oxidation on the damage progression and mechanical behavior of textile composites. This involves running a coupled analysis simulating the oxidation of the composite material over time and a progressive damage analysis that takes into account the effect of the oxida tion on the mechanical properties and conversely, the effect of damage and stresses on the oxidation behavior . It is necessary to use homogenized properties for the tow because it is practically impossible to discretely model all the fibers in a composite. The basic oxidation model is taken from existing work in the literature which was used to simulate the oxidation behavior of neat PMR -15 resin. The governing equations and the finite element formulation for the model are described as well as different app roaches for determining the various effective properties. Two configurations are analyzed to test the accuracy of the effective oxidation properties. The configurations are discretely modeled to provide the reference solution and the different models with fully homogenized materials as well as mixed regions are used to determine the accuracy of the effective properties. The concentration of oxygen in the model as well as the oxidation state of the materials in the composite are compared to the reference mod el. It is seen that the effective oxidation properties perform reasonably well for both configurations and is able to simulate the oxidation layer growth.

A planar electrode containing cylindrical pores with semi-circular ends is modeled using a finite element implementation of the transient nonlinear 2D Nernst– Planck–Poisson-modified Stern (NPPMS) model. The model uses a modified Stern layer to account for finite ion size. The study includes the effects of pore radius and depth on the predicted electric potential, ion concentration, surface charge density, surface energy density, and charging time. The ion concentration and electric potential are found to be sensitive to the change in radii of the pore and insensitive to the pore depth. The surface charge density is slightly higher within the pore than along the vertical flat regions of the electrode. The increase in surface area due to porosity increases the charging time.

A new strategy has been introduced by an earlier work where the natural hierarchical character of model descriptions and simulation results are exploited to expedite analysis of problems. Hierarchical techniques are used in the development, organization, and management of finite element models, and for the post-processing of the results from the analyses. In the current work, the hierarchical strategy is used to analyze materials that have a complex microstructure. The hierarchical analysis environment is used to analyze a double cantilever beam (DCB) specimen fabricated with woven composite. Such a configuration contains many microstructural scales such as fiber/matrix, tow architecture, and laminate. The results of a detailed finite element stress analysis on the DCB specimen are presented. The strain energy release rate along the width of the crack front is calculated and is found to be dependent on the local microstructure at the crack front.

A finite element implementation of the transient nonlinear Nernst-Planck-Poisson (NPP) and Nernst-Planck-Poisson-modified Stern (NPPMS) models is presented. The NPPMS model uses multipoint constraints to account for finite ion size, resulting in realistic ion concentrations even at high surface potential. The Poisson-Boltzmann equation is used to provide a limited check of the transient models for low surface potential and dilute bulk solutions. The effects of the surface potential and bulk molarity on the electric potential and ion concentrations as functions of space and time are studied. The ability of the models to predict realistic energy storage capacity is investigated. The predicted energy is much more sensitive to surface potential than to bulk solution molarity.

Standardized quality assurance (QA) plays an import role to maintain and develop success of cardiovascular procedures (CP). Well-established QA models from Germany could be shared in a form repository for world-wide reuse and exchange. Therefore, we collected the complete set of all quality QA forms for CP, which is obligatory to be filled out by all German health service providers. Original forms were converted into standardized study forms according to ODM (Operational Data Model) and translated into English. Common medical concepts and clusters of medical concepts were identified based on UMLS coding of form items. All forms are available on the web as multilingual ODM documents. UMLS concept coverage analysis indicates 88% coverage with few but critically important definition gaps, which need to be addressed by UMLS.

Quality assurance (QA) is a key factor to evaluate success of organ transplantations. In Germany QA documentation is progressively developed and enforced by law. Our objective is to share QA models from Germany in a standardized format within a form repository for world-wide reuse and exchange. Original QA forms were converted into standardized study forms according to the Operational Data Model (ODM) and shared for open access in an international forms repository. Form elements were translated into English and semantically enriched with Concept Unique Identifiers from the Unified Medical Language System (UMLS) based on medical expert decision. All forms are available on the web as multilingual ODM documents. UMLS concept coverage analysis indicates 92% coverage with few but critically important definition gaps. New content and infrastructure for harmonized documentation forms is provided in the domain of organ transplantations enabling world-wide reuse and exchange.

Medical forms are frequently used to document patient data or to collect relevant data for clinical trials. It is crucial to harmonize medical forms in order to improve interoperability and data integration between medical applications. Here we propose a (semi-) automatic annotation of medical forms with concepts of the Unified Medical Language System (UMLS). Our annotation workflow encompasses a novel semantic blocking, sophisticated match techniques and post-processing steps to select reasonable annotations. We evaluate our methods based on reference mappings between medical forms and UMLS, and further manually validate the recommended annotations.

Dealing with uncertainty that is inherently present in any medical domain, is one of the major challenges when designing a medical decision support system. We demonstrate how probabilistic logic can be used to design medical knowledge bases at the example of analysing clinical brain tumor data. We use MECoRe, a system implementing probabilistic conditional logic, to create a knowledge base BT that contains medical knowledge originating from both statistical data as well as from medical experts. Any incomplete or unspecified knowledge is completed by MECoRe in an information-theoretically optimal way by employing the principle of maximum entropy. BT is evaluated with respect to a series of queries regarding diagnosis and prognosis, using a real documented patient case.

We present a case-study of applying probabilistic logic to the analysis of clinical patient data in neurosurgery. Probabilistic conditionals are used to build a knowledge base for modelling and representing clinical brain tumor data and expert knowledge of physicians working in this area. The semantics of a knowledge base consisting of probabilistic conditionals is defined by employing the principle of maximum entropy that chooses among those probability distributions satisfying all conditionals the one that is as unbiased as possible. For computing the maximum entropy distribution we use the MEcore system that additionally provides a series of knowledge management operations like revising, updating and querying a knowledge base. The use of the obtained knowledge base is illustrated by using MEcore's knowledge management operations.

Introduction: Information systems are a key success factor for medical research and healthcare. Currently, most of these systems apply heterogeneous and proprietary data models, which impede data exchange and integrated data analysis for scientific purposes. Due to the complexity of medical terminology, the overall number of medical data models is very high. At present, the vast majority of these models are not available to the scientific community. The objective of the Portal of Medical Data Models (MDM, https://medical-data-models.org) is to foster sharing of medical data models. Methods: MDM is a registered European information infrastructure. It provides a multilingual platform for exchange and discussion of data models in medicine, both for medical research and healthcare. The system is developed in collaboration with the University Library of Munster to ensure sustainability. A web front-end enables users to search, view, download and discuss data models. Eleven different export formats are available (ODM, PDF, CDA, CSV, MACRO-XML, REDCap, SQL, SPSS, ADL, R, XLSX). MDM contents were analysed with descriptive statistics. Results: MDM contains 4387 current versions of data models (in total 10 963 versions). 2475 of these models belong to oncology trials. The most common keyword (n = 3826) is ‘Clinical Trial’; most frequent diseases are breast cancer, leukemia, lung and colorectal neoplasms. Most common languages of data elements are English (n = 328 557) and German (n = 68 738). Semantic annotations (UMLS codes) are available for 108 412 data items, 2453 item groups and 35 361 code list items. Overall 335 087 UMLS codes are assigned with 21 847 unique codes. Few UMLS codes are used several thousand times, but there is a long tail of rarely used codes in the frequency distribution. Discussion: Expected benefits of the MDM portal are improved and accelerated design of medical data models by sharing best practice, more standardised data models with semantic annotation and better information exchange between information systems, in particular Electronic Data Capture (EDC) and Electronic Health Records (EHR) systems. Contents of the MDM portal need to be further expanded to reach broad coverage of all relevant medical domains. Database URL: https://medical-data-models.org

Gl obal/local strategies have long been used in conjunction with finite element anal ysis to analyze regions of interest in configurations that are too l arge for a complete detailed analysis. Material properties and/or architecture are somehow approxi mated in the global section, and detailed properties and architecture are used in the l ocal section. Textile composites represent a configuration suitable to global/local analysis because the macrolevel response of the composites is so dependent upon the complex microstructure of the weave. In this work, several global/local strategies for detailed stress analysis of textile composites were i nvestigated. Different approxi mati ons of the textile material properties and architecture were eval uated for use in the global region. It was found that using homogenized properties for the global region yields accurate local solutions only in limited cases. Addi tionally, it was found that using multi-fiel d macroelements to model the global textile architecture yiel ds very accurate soluti ons in the local region.

The increasing popularity of textile composite materials in advanced structures presents challenges that require reliable predictive tools. Finite element analysis has been employed to study the behavior of textile composites and gain greater insight on these materials. However, there has been virtually no study on the convergence of flnite element models of textile composite materials. Herein, a variety of metrics will be studied including volume averaged quantities such as efiective engineering properties and local measures such as point stresses. A plain weave architecture was chosen due to symmetries allowing for a smaller analysis region that could be analyzed using very reflned meshes. The efiect of a glass or graphite material system was investigated as well as the efiect of the waviness of the architecture. Results showed the convergence of some metrics are sensitive to material system while others are sensitive to the waviness of architecture.

*† ‡ A new methodology is introduced where the natural hierarchical character of model descriptions and simulation results are exploited to expedite analysis of problems. The philosophy and the different concepts involved are illustrated by implementing the strategy to solve some sample problems. The work resulted in a mix of mechanics, hierarchical data structures and software interfaces that form a rapid analysis environment. This can be very advantageous for cases where a sequence of analyses is required because of safety concerns or cost. When designing a structure, it is common to make numerous modifications to the model. In such cases, the ability to use data from different models within the same analysis environment becomes a major advantage. The proposed system’s forte is its hierarchical framework that allows models to communicate with each other and share information with one another. This makes it ideal for global/local analyses where solutions from a global model are used to derive the boundary conditions for the local model. The system was used to conduct a micro mechanical analysis on unidirectional composites that have a nonuniform spatial distribution of the fibers. The hierarchical strategy is not tied to any specific methodology and can be adapted to solve problems using different technologies. This allows the strategy to be used across multiple length scales and governing equations.

This paper aims to numerically assess the effects of electrolyte properties and electrode morphology on the capacitance of electric double layer capacitors (EDLCs) made of mesoporous electrodes consisting of ordered cylindrical pores in non-aqueous electrolytes. Simulations solved a three-dimensional modified Poisson-Boltzmann model. They accounted for the finite size of ions and field-dependent electrolyte permittivity while the pores were perpendicular to the current collector. The effects of pore radius, porosity, effective ion diameter, and electrolyte field-dependent permittivity on the diffuse layer gravimetric capacitance were investigated systematically in order to determine key parameters affecting EDLCs’ performance. The simulations showed that reducing the ion effective diameter and the pore radius resulted in the strongest increase in diffuse layer gravimetric capacitance up to a critical radius below which the capacitance reaches a plateau. Increasing the electrode porosity also increased the diffuse layer gravimetric capacitance. Accounting for more realistic field-dependent permittivity was found to significantly reduce the predicted diffuse layer gravimetric capacitance. Finally, accounting for the contribution of the Stern layer to the total capacitance was essential in predicting experimental data for a wide range of porous activated carbon electrodes and non-aqueous electrolytes.