dataesr/scientific-paragraphs-categorization huggingface dataset:
The RM is a high-level abstraction model composed of a set of generic classes, which should be included in any EHR, to structure clinical data (syntactic interoperability). The CIM is a terminology-ontology-constrained subset of the RM which allows semantic enrichment of the RM. The clinical artifacts resulting from this design process will be easily organized, stored, queried, displayed, analyzed for various purposes, and exchanged between different information systems  [13]. Goosen et al. [16]  has described the most popular initiatives that follow the dual model approach, highlighting their differences and similarities. Most have been assessed in various projects to expose EHR data: Chen et al. [17]  used the openEHR Archetype to convert data of a regional EHR system and Spath et al. [18]  to model data of a biobank database.
This mechanism is called profiling. We compared the local i2b2 data structure and storage model to the corresponding FHIR resources to determine whether it was necessary to define a profile. We first analyzed the i2b2 schema and data organization in collaboration with the HEGP i2b2 database administrator to identify the data related to the requirements of the client. These data are described in Fig. 3 , in which we depict the i2b2 star schema with only the targeted entities, relations, and fields. Second, we manually browsed and analyzed the FHIR resources to identify the corresponding data elements required for the HEGP local context and adaptation, if necessary. Five FHIR resources were selected: Patient, Medication, Encounter, MedicationOrder, and Diagnosti-cReport. Third, we followed the method described by Lee et al.
[29]  and conducted a gap analysis between these two models. Figure  4  describes the mapping between these two models. We mapped all data elements from the local model to a corresponding data element in a FHIR resource and defined system terminology constraints for some to comply with the local context and ensure interoperability with external systems. Finally, we implemented and validated the resulting mapping using the HAPI FHIR API. HAPI FHIR  [24]  is a Java open-source implementation of the FHIR specifications. For the work described here, we used HAPI structure library DSTU2, version 1. 2, which corresponds to the FHIR specification version 1. 0. 0.
The data access object (DAO) layer We implemented this layer using the DAO design pattern  [30]  combined with a java object-relational mapping tool: Hibernate. For each of these methods, a corresponding HQL (Hibernate Query Language) query has been defined to query the i2b2 data model. Basic operations on database sessions are handled by the class HibernateDao (Fig. 6 ). This layer also contains a set of Java classes called entities. Entities implement the Java representation of each i2b2 database table. For each i2b2 database table column we defined a Java class field with the same name. For each Java class field, we based the datatype mapping on the Oracle mapping table described in this reference  [32]. In this layer, the entities, generic abstract DAO class, and set of DAO interface classes should not be modified except if the i2b2 star schema model is modified.
In this query, we also allowed the client to include data related to Patient, Encounter, and Medication FHIR resources (Fig. 8 ). Query type #1 and #2 displayed 80 FHIR Diagnosti-cReport resources for 36 distinct patients and 503 FHIR MedicationOrder resources for 290 distinct patients, respectively. We validated the results displayed by the FHIR server in response to these two queries by asking the HEGP i2b2 database administrator to query the i2b2 database server to respond to the "client" requirements described in Figs. 1  and 2. The database administrator wrote two ad-hoc SQL queries for these two objectives and the results were extracted in csv files. All results (80 FHIR DiagnosticReport for 36 patients) displayed for query type #1 were manually verified and compared. For query type #2, we randomly chose 30 patients from the results displayed by the FHIR API and manually compared.
Several articles have reported work in which mapping between a VHR CIM and a data source system (mostly an EHR) has been described  [13]. The most often reported VHR CIM is the OpenEHR Archetype  [13]. In a recent post  [38] , Thomas Beale, compared the FHIR and openEHR approach of clinical data modelling from a different point of view. In terms of their aims, the objective of FHIR is to solve the problem of system-to-system communication, using the message approach, and the problem of system-toapplication communication by allowing the development of APIs suited to programming. The objective of open-EHR is to address the patient data challenge: i. e. creating long-lived, multi-version, distributed, and computable patient records that are future-proof and semantically enabled, using platform architecture based on formal semantics combined with terminologies, ontologies, drug databases service interfaces, etc.
In terms of their technical representation, the FHIR reference model is defined by a collection of information models, called resources, which can be profiled (or not) to generate a clinical information model. OpenEHR defines a reference model for the entire world and all open-EHR data obey it. Various layers of models at the top, including archetypes, templates, and terminology subsets, allow the definition of a clinical information model. FHIR resources are produced from a superset of data found in legacy systems to be directly used by developers. In open-EHR, models are generally built by communities of clinical domain experts, based on requirements. In terms of platform coverage, openEHR and FHIR provide the same coverage level of patient information. The difference lies in the semantic models used.
Open-EHR defines semantic querying using portable query language, AQL, whereas FHIR do not cover this aspect. However, FHIR coverage on APIs is substantially greater. Studies in which mapping between a VHR CIM and a data source system has been reported covered a large variety of clinical domains, including nursing, oncology, neonatology, genetics, and infectious diseases  [13]. Moreno-Conde et al. [13]  analyzed these articles to assess whether there is an emergent consensus strategy for building and using CIM artefacts and whether it is possible to propose a common or unified CIM Process (CIMP). They described a CIM design and implementation process comprised of seven steps: 1. Scope definition leading to selection of the domain and selecting relevant experts 2. Analysis of the information covered in the specific domain 3.
Design of the CIMs 4. Definition of implementable CIM specifications 5. Validation 6. Publishing and maintenance 7. Governance In our approach, the first step corresponds to the identification and the formalization of the requirements of the "client". The second step corresponds to the analysis of these requirements, which led to the identification of the various data elements targeted in both the i2b2 data model and structure and the FHIR resources and terminologies. We accomplished this by manually browsing the specifications of the FHIR resources. The flat structure of the FHIR CIM, represented by the UML notation, is clearly an advantage and the current descriptions made by the HL7 FHIR authors greatly facilitate browsing the model. For each FHIR resource, the authors describe the model using UML and several other formalisms.
They describe the terminology which helps the developers determine whether it is necessary/recommended to map the data of the EHR with an existing coding system. They also list the possible parameters that can be used to implement FHIR Search operations. Steps 3 and 4 correspond to the gap analysis that we have performed, between the data elements identified in the local context and FHIR resources, and the definition of our profile. Step 5 concerns the manner in which the newly designed CIMs are validated. Various techniques have been adopted to validate the results of the previous steps, including peer review, the creation of prototype screens, and challenging the VHR CIM with routinely collected clinical data from multiple patients, which is probably the strongest  [13]. We chose the latter technique, as one of the advantages of FHIR specification is the definition of an API specification to implement its CIM.
Limitations and upcoming improvements Currently the i2b2 FHIR server supports only FHIR Search access operations to expose data related to two FHIR Resources: DiagnosticReport and MedicationOrder. We are implementing additional FHIR resources and plan to extend the server to other types of operations. A terminology enrichment layer is currently under development. The implementation of this layer within the French context depends mainly on mapping efforts performed between the French and international coding systems. For medication, the French medication identification coding system is already mapped to ATC at the HEGP and has been used to enrich the FHIR server query results as depicted in Fig. 8. This mapping should be externalized for use in the implementation of the terminology enrichment layer.
For the other coding systems, the HEGP was a data provider in a large European project  [10]. Two terminological mappings have been performed for a sub set of codes: -An HEGP local coding system for health measurements and LOINC (Logical Observation Identifiers Names and Codes) -A French coding system for clinical and surgical procedures, called classification commune des actes médicaux (CCAM) and SNOMED (The Systematized Nomenclature of Medicine). We plan to use these mappings to enhance the terminology enrichment layer of the FHIR server. Security is important when manipulating healthcare data. A security layer is under development. The server will include two levels of security recommended by the FHIR authors  [40] : -Authentication: to verify that the user is who they say they are. This layer will implement an HTTP basic Auth coupled with the local Lightweight Directory Access Protocol (LDAP) server.
-Authorization: to verify that the user is allowed to perform the given action. This layer will implement the AuthorizationInterceptor class of the HAPI FHIR API to examine the client request to determine whether a "write" or a "read" operation is legal. Other aspects of security should be taken into consideration by developers when installing the server. For example, all clocks should be synchronized using NTP/SNTP and all exchange of production data should be performed using TLS/SSl, in addition to other measures  [40]. The i2b2 FHIR server described in this article is based on FHIR DSTU2 version 1. 0. 0. An upgrade to the most recent FHIR version i. e. DSTU3 1. 9. 0 is currently under implementation. The most important change that can affect the i2b2 FHIR server is related to the introduction of a new interface called IBaseResource, from which the IResource interface extends.
Conclusion We have been able to show the feasibility of implementing a FHIR layer over the i2b2 database model to expose data of the CDW as a set of FHIR resources using the HAPI FHIR API. Our i2b2 FHIR server could be easily used/adapted by other developers to expose their local data in FHIR format. The DAO layer implemented is suitable for other i2b2 users and only requires adaptation of the service class package, where HQL queries should be rewritten according to the local i2b2 data storage model and/ or according to other requirements. The FHIR resource provider classes should also be adapted, as these classes handle the mapping between the i2b2 model and the FHIR resources exposed by the server. The code source is freely available online as open source.
Study design and patients Data were collected in a multinational, prospective cohort study performed in seven hospitals in The Netherlands, Italy, France, and Mexico, designed to evaluate clinical and laboratory predictors for cancer-associated VTE. Here we report on the performance of four published clinical prediction scores for cancer-associated VTE. Outpatients with lung, esophageal, colorectal, pancreatic, breast, prostate, gastric, ovarian, or bladder cancer classified as stage III or IV according to the American Joint Committee on Cancer criteria were eligible if they were scheduled for chemotherapy within seven days or had started chemotherapy in the previous three months. Exclusion.
Evaluation of prediction scores We evaluated the following clinical prediction scores for cancerassociated VTE: the Khorana,  7  Vienna CATS,  8  PROTECHT,  9  and CONKO scores 10 (Table  1 ). Characteristics of the derivation studies are provided in the Online Supplementary Appendix text 1. Evaluation of the Khorana score was included in the initial study protocol, while the other scores were published during the course of the study and were subsequently included in the analysis. We were unable to evaluate a recently proposed genetic risk score. 15 In order to calculate the Vienna CATS score, D-dimer (INNO-VANCE, Siemens) and soluble P-selectin (ELISA, R&D Systems, Minneapolis, MN, USA) concentrations were measured centrally in baseline samples. Analysis of samples of 98 patients from one center provided implausible results due to incorrect shipment, forcing us to omit these measurements, while the patients were retained in the dataset and among the imputed data.
18 The 95% confidence intervals were calculated by repeating the analyses in 250 bootstrap samples. To assess the difference in VTE risk between low-and high-risk patients, subdistribution hazard ratios (SHR) and 95% CIs for the dichotomized scores were estimated the competing risks regression model of Fine and Gray. 19 The predictive value of the separate items in each score was assessed by multivariable competing risks regression models. The proportionality assumption was checked by adding an interaction term between each variable and time to the model. Multiple imputation was used to minimize the bias associated with missing data. 20 We assumed a 'missing' at random pattern in which missingness depends on other observed variables. Center, inclusion year, all baseline characteristics, and outcome data were included in the imputation model to create twenty imputed datasets.
Analyses were performed separately in each imputed dataset; estimates with standard errors were combined across the datasets using Rubin's rule. 21 The complete case analyses were provided for comparison. A significance level of 0. 05 was used in statistical testing. All analyses were performed in R, v. 3. 3. 2 (R Foundation for Statistical Computing, Vienna, Austria; www. R-project. org), in particular using the "mice" v. 2. 25 package for multiple imputation, the "cmprsk" v. 2. 2-7 for the competing risk analyses, and the "pec" package v. 2. 4. 9 for the time-dependent c-indices. teristics of the patients enrolled prior to chemotherapy and the complete study group are summarized in Table  2. No significant differences were found between patients enrolled prior to chemotherapy and those enrolled during chemotherapy, except for higher P-selectin levels in the former group.
Sensitivity analyses The sensitivity analysis restricted to symptomatic VTE in the total study group yielded comparable results (Online Supplementary Appendix text 5). The 6-month risk of symptomatic VTE in high-risk patients ranged from 3. 7% (95%CI: 1. 2-8. 6%) for the Khorana score to 6. 2% (95%CI: 3. 8-9. 4%) for the PROTECHT score. In the sensitivity analysis restricted to the first 90 days of follow up, in which 34 of 876 patients (3. 9%) developed VTE, the discriminatory performance of all scores was slightly better (Online Supplementary Appendix text 6). The 90-day VTE incidence in high-risk patients (≥3 points) ranged from 3. 6% (95%CI: 1. 2-8. 4%) for the Khorana score to 6. 4% (95%CI: 3. 7-10%) for the Vienna CATS score and 6. 4% (95%CI: for the PROTECHT score. The results from the complete case analysis did not substantially differ from the analysis of the imputed datasets (Online Supplementary Appendix text 7).
Whether thromboprophylaxis with direct oral anticoagulants is a safe, effective, and less burdensome alternative in ambulatory cancer patients is currently being investigated in several ongoing trials that use the Khorana score for risk stratification (e. g. clinicaltrials. gov identifiers: 02555878 and 02048865). Interestingly, one of the trials applies the Khorana score at a lower positivity threshold of 2 points, which classifies a greater proportion of patients in the high-risk group, but in parallel may decrease the positive predictive value of the score. The present findings do not support the use of any of the examined scores to select patients for thromboprophylaxis. The discriminatory performance of the dichotomized Vienna CATS and PROTECHT scores is somewhat encouraging, but confirmation in subsequent observational and intervention studies providing thromboprophylaxis to high-risk patients is needed before they can be used in clinical practice.
Introduction Aging is a growing phenomenon all around the world. Along with this increase in the number of humans in the highest range of age, several problems come along  [1]. Frailty is one of these problems, due to worsening of health condition of the frail older adult, having a higher proportion of adverse outcomes and elevated usage of health care. Moreover, it has been noted by previous research that a considerable number of older adults are frail in the last year of their lives  [2] [3] [4]. However it is not common that health professionals routinely measure frailty, and some of the available datasets in order to study geriatric epidemiology lack appropriate measurements to operationalize frailty, rendering it invisible for quite a while  [5]. On the other hand, the frailty index allows integrating an accurate measurement of frailty from different number of sources, even if these sources do not have frailty measurements  [6].
Methods We report data from the third wave of the MHAS (2012). MHAS is a cohort study that currently has four rounds, the last one in 2015, whose main objective is to analyze how Mexican adults age. Complete aims and description of this study are available elsewhere  [12]. This round included a set of questionnaires that were answered by a member of the family of the deceased (i. e. , the last year of life of a communitydwelling older adult). Regarding FI, it was composed following the standardized procedure by Searle et al. , which includes transforming each variable into a score of 0 (deficit absent) to 1 (deficit present) with possible intermediate scores  [13]. All deficit scores were summed and then divided by 30 (total number of deficits in the current list) for each participant, with total scores for the FI ranging from 0 (no deficit present) to 1 (all deficits present).
Spearman correlations were used to evaluate monotonic associations of EMAQ scores with DEBQ scores and BMI. Group comparisons for Gender and Diagnosis (at risk or not for lifetime BN) on EMAQ-P and EMAQ-N scores were estimated by analyses of variance and associated effect sizes (partial eta-squared, η p 2 ). A value of η p 2 around 0. 01 is associated with a small effect, around 0. 06 with a medium effect, and around 0. 14 with a large effect  (Cohen, 1988). Exploratory factor analysis and CFA were carried out using R 2. 15. 2 (R Core Team, 2014) and Mplus 7  (Muthén and Muthén, 2013)  software. The remaining analyses were carried out using SPSS 20. 0  (Ibm Corp. Released, 2011). A fixed Type I error rate of 5% was retained for all statistical tests.
Thus if measurement invariance across gender/age does not hold, total score differences across gender/age groups are difficult to interpret, as they could result either from measurement differences or from genuine differences in emotionally driven eating behaviors. Accordingly, additional data are being collected to investigate this issue in a larger and more diverse sample. The current study presents some limitations. First, it was conducted among students, so there are limits to the generalizability of the findings. Moreover, the range of academic disciplines represented was relatively restricted (and included many psychology students) and some factors that could influence EE, such as ethno-culture, religion, socio-economic background, incomes or the level of physical activity were not taken into account  (Dubé et al.
Study Data To measure waitlisting variation and waitlisting racial disparities (African American vs. white), we analyzed United States Renal Data System data on dialysis facility characteristics, obtained by aggregating patient receiving dialysis (service dates from January 1, 2012 to December 31, 2014) by facility and year to obtain data on each facility in which a patient received dialysis. Each patient contributed 1 observation per facility per service period. Of a total of 9924 dialysis facilities, we included 5050 dialysis facilities with an ESRD network ID, $5 African American patients, $5 white patients, and $11 total patients with ESRD in every year (representing 987,046 patients) (Figure  1 ). Dialysis facility quality measures were obtained from the Centers for Medicare & Medicaid Services dataset used.
Study design, sampling and participants This study was part of the SPOTLIGHT project  [25]. A survey was conducted in five urban regions across Europe: Ghent and suburbs (Belgium), Paris and inner suburbs (France), Budapest and suburbs (Hungary), the Randstad (a conurbation including the cities of Amsterdam, Rotterdam, The Hague and Utrecht in the Netherlands) and Greater London (UK). Neighbourhood sampling was based on a combination of residential density and socioeconomic status (SES) data at the neighbourhood level. This resulted in four types of pre-specified neighbourhoods: low SES/low residential density, low SES/high residential density, high SES/low residential density and high SES/high residential density. In each country, three neighbourhoods of each type were randomly sampled (i.
Employment status (which includes people who were employed or in education) was classified into two groups: yes or no. As the percentage of missing values ranged from 1% (age and sex) to 16. 4% (fast food consumption), complete case analysis could potentially lead to biased results. We, therefore, performed multiple imputation on all variables (including outcomes and determinants)  [28]. To that end, data were assumed to be missing at random, i. e. the probability of a value being missing depends on other observed values but not on the missing value itself  [29]. Based on the percentage of missing values, we chose to impute 20 datasets, following the recommendation of Rubin  [29]  and Bodner  [30]. This was done via predictive mean matching using STATA ® 14. Descriptive analyses were performed on un-imputed data.
Acknowledgements The  SPOTLIGHT  project was funded by the  Seventh Framework Programme  ( CORDIS FP7 ) of the  European Commission ,  HEALTH  ( FP7-HEALTH-2011 -two-stage), Grant agreement No. 278186. The researcher 'de Pinho MGM' has a grant from the  Brazilian higher education agency CNPq  ( National Council for Scientific and Technological Development) as part of Science Without Borders Programme , Process number  233850/2014-7. The content of this article reflects only the authors' views, and the funding agencies are not liable for any use that may be made of the information contained therein.
Material and method The PREVAGAY survey provided data on HIV serostatus and places visited by MSM (see Data). Sexual networks were simulated based on these data (see Dynamic sexual network modelling) and under different assumptions for network characteristics that might be of interest in the epidemic spread (see Allocating partnerships to venues and Effect of correlation between venues visited). The spread of HIV was simulated in these networks (see HIV progression, transmission, screening and interventions). Finally, we compared the effect of PrEP under different scenarios (see Simulation Scenarios).