scholarly journals An openEHR Virtual Patient Template for Pancreatic Cancer

2021 ◽  
Author(s):  
Samer Alkarkoukly ◽  
Abdul-Mateen Rajput
Keyword(s):  
Pancreatic Cancer ◽  
Data Model ◽  
Building Blocks ◽  
Pancreatic Cancer Patient ◽  
Virtual Patient ◽  
Data Set ◽  
Minimal Data ◽  
Generic Data Model ◽  
Data Elements ◽  
Virtual Data

openEHR is an open-source technology for e-health, aims to build data models for interoperable Electronic Health Records (EHRs) and to enhance semantic interoperability. openEHR architecture consists of different building blocks, among them is the “template” which consists of different archetypes and aims to collect the data for a specific use-case. In this paper, we created a generic data model for a virtual pancreatic cancer patient, using the openEHR approach and tools, to be used for testing and virtual environments. The data elements for this template were derived from the “Oncology minimal data set” of HiGHmed project. In addition, we generated virtual data profiles for 10 patients using the template. The objective of this exercise is to provide a data model and virtual data profiles for testing and experimenting scenarios within the openEHR environment. Both of the template and the 10 virtual patient profiles are available publicly.

scholarly journals From OpenEHR to FHIR and OMOP Data Model for Microbiology Findings

2021 ◽  
Author(s):  
Eugenia Rinaldi ◽  
Sylvia Thun
Keyword(s):  
Infection Control ◽  
Data Model ◽  
Data Exchange ◽  
Control Group ◽  
Common Data Model ◽  
University Hospitals ◽  
Data Set ◽  
Related Data ◽  
National Initiative ◽  
Data Elements

HiGHmed is a German Consortium where eight University Hospitals have agreed to the cross-institutional data exchange through novel medical informatics solutions. The HiGHmed Use Case Infection Control group has modelled a set of infection-related data in the openEHR format. In order to establish interoperability with the other German Consortia belonging to the same national initiative, we mapped the openEHR information to the Fast Healthcare Interoperability Resources (FHIR) format recommended within the initiative. FHIR enables fast exchange of data thanks to the discrete and independent data elements into which information is organized. Furthermore, to explore the possibility of maximizing analysis capabilities for our data set, we subsequently mapped the FHIR elements to the Observational Medical Outcomes Partnership Common Data Model (OMOP CDM). The OMOP data model is designed to support the conduct of research to identify and evaluate associations between interventions and outcomes caused by these interventions. Mapping across standard allows to exploit their peculiarities while establishing and/or maintaining interoperability. This article provides an overview of our experience in mapping infection control related data across three different standards openEHR, FHIR and OMOP CDM.

scholarly journals OSIRIS: A Minimum Data Set for Data Sharing and Interoperability in Oncology

2021 ◽  
pp. 256-265
Author(s):  
Julien Guérin ◽  
Yec'han Laizet ◽  
Vincent Le Texier ◽  
Laetitia Chanas ◽  
Bastien Rance ◽  
...  
Keyword(s):  
Cancer Research ◽  
Data Sharing ◽  
Data Model ◽  
Task Force ◽  
Genomic Data ◽  
Common Data Model ◽  
University Hospitals ◽  
Data Set ◽  
Community Initiatives ◽  
Minimal Data

PURPOSE Many institutions throughout the world have launched precision medicine initiatives in oncology, and a large amount of clinical and genomic data is being produced. Although there have been attempts at data sharing with the community, initiatives are still limited. In this context, a French task force composed of Integrated Cancer Research Sites (SIRICs), comprehensive cancer centers from the Unicancer network (one of Europe's largest cancer research organization), and university hospitals launched an initiative to improve and accelerate retrospective and prospective clinical and genomic data sharing in oncology. MATERIALS AND METHODS For 5 years, the OSIRIS group has worked on structuring data and identifying technical solutions for collecting and sharing them. The group used a multidisciplinary approach that included weekly scientific and technical meetings over several months to foster a national consensus on a minimal data set. RESULTS The resulting OSIRIS set and event-based data model, which is able to capture the disease course, was built with 67 clinical and 65 omics items. The group made it compatible with the HL7 Fast Healthcare Interoperability Resources (FHIR) format to maximize interoperability. The OSIRIS set was reviewed, approved by a National Plan Strategic Committee, and freely released to the community. A proof-of-concept study was carried out to put the OSIRIS set and Common Data Model into practice using a cohort of 300 patients. CONCLUSION Using a national and bottom-up approach, the OSIRIS group has defined a model including a minimal set of clinical and genomic data that can be used to accelerate data sharing produced in oncology. The model relies on clear and formally defined terminologies and, as such, may also benefit the larger international community.

scholarly journals Patient Cohort Identification on Time Series Data Using the OMOP Common Data Model

2021 ◽  
Vol 12 (01) ◽  
pp. 057-064
Author(s):  
Christian Maier ◽  
Lorenz A. Kapsner ◽  
Sebastian Mate ◽  
Hans-Ulrich Prokosch ◽  
Stefan Kraus
Keyword(s):  
Data Model ◽  
Time Series Data ◽  
Series Data ◽  
Common Data Model ◽  
Eligibility Criteria ◽  
Data Set ◽  
Cohort Identification ◽  
Data Elements ◽  
Clinical Source ◽  
Arden Syntax

Abstract Background The identification of patient cohorts for recruiting patients into clinical trials requires an evaluation of study-specific inclusion and exclusion criteria. These criteria are specified depending on corresponding clinical facts. Some of these facts may not be present in the clinical source systems and need to be calculated either in advance or at cohort query runtime (so-called feasibility query). Objectives We use the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) as the repository for our clinical data. However, Atlas, the graphical user interface of OMOP, does not offer the functionality to perform calculations on facts data. Therefore, we were in search for a different approach. The objective of this study is to investigate whether the Arden Syntax can be used for feasibility queries on the OMOP CDM to enable on-the-fly calculations at query runtime, to eliminate the need to precalculate data elements that are involved with researchers' criteria specification. Methods We implemented a service that reads the facts from the OMOP repository and provides it in a form which an Arden Syntax Medical Logic Module (MLM) can process. Then, we implemented an MLM that applies the eligibility criteria to every patient data set and outputs the list of eligible cases (i.e., performs the feasibility query). Results The study resulted in an MLM-based feasibility query that identifies cases of overventilation as an example of how an on-the-fly calculation can be realized. The algorithm is split into two MLMs to provide the reusability of the approach. Conclusion We found that MLMs are a suitable technology for feasibility queries on the OMOP CDM. Our method of performing on-the-fly calculations can be employed with any OMOP instance and without touching existing infrastructure like the Extract, Transform and Load pipeline. Therefore, we think that it is a well-suited method to perform on-the-fly calculations on OMOP.

scholarly journals Exploratory Geovisualizations for Supporting the Qualitative Analysis and Synthesis of Place-Related Emotion Data

2019 ◽  
Author(s):  
Susanne Bleisch ◽  
Daria Hollenstein
Keyword(s):  
Data Analysis ◽  
Qualitative Analysis ◽  
Data Model ◽  
Qualitative Data ◽  
Elderly Women ◽  
Qualitative Data Analysis ◽  
Data Set ◽  
Personal Significance ◽  
Generic Data ◽  
Generic Data Model

Locations become places through personal significance and experience. While place data are not emotion data, per se, personal significance and experience are often emotional. In this paper, we explore the potential of using visual data exploration to support the qualitative analysis of place-related emotion data. To do so, we draw upon Creswell’s (2009) definition of place to define a generic data model that contains emotion data for a given location and its locale. For each data dimension in our model, we present symbolization options that can be combined to create a range of interactive visualizations, specifically supporting re-expression. We discuss the usefulness of example visualizations, created based on a data set from a pilot study on how elderly women experience their neighborhood. We find that the visualizations support four broad qualitative data analysis tasks: revising categorizations, making connections and relationships, aggregating for synthesis, and corroborating evidence by combining sense of place with locale information to support a holistic interpretation of place data. In conclusion, the paper contributes to the literature in three ways. It provides a generic data model and associated symbolization options, and uses examples to show how place-related emotion data can be visualized. Further, the example visualizations make explicit how re-expression, the combination of emotion data with locale information, and visualization of vagueness and linked data support the analysis of emotion data. Finally, we advocate for visualization-supported qualitative data analysis in interdisciplinary teams so that more suitable maps are used and so that cartographers can better understand and support qualitative data analysis.

Lorlatinib Induces Durable Disease Stabilization in a Pancreatic Cancer Patient with a ROS1 p.L1950F Mutation: Case Report

2021 ◽  
pp. 1-7
Author(s):  
Janna-Lisa Velthaus ◽  
Peter Iglauer ◽  
Ronald Simon ◽  
Carsten Bokemeyer ◽  
Peter Bannas ◽  
...  
Keyword(s):  
Pancreatic Cancer ◽  
Cancer Patient ◽  
Point Mutation ◽  
Kinase Inhibitors ◽  
Point Mutations ◽  
Progression Free Survival ◽  
Pancreatic Cancer Patient ◽  
Targeted Treatment ◽  
Precision Oncology ◽  
Disease Stabilization

<b><i>Introduction:</i></b> The prognosis of pancreatic cancer has improved only modestly in recent years. This is partly due to the lack of development in precision oncology including immune oncology in this entity. Rearrangements of the proto-oncogene tyrosine protein kinase <i>ROS1</i> gene represent driver alterations found especially in lung cancer. Tyrosine kinase inhibitors (TKI) with activity against ROS1 including lorlatinib substantially improved the outcome of this patient population. Anecdotal evidence reports treatment of pancreatic cancer harboring <i>ROS1</i> fusions with ROS1 TKI, but data concerning treatment of patients with <i>ROS1</i> point mutations are lacking. <b><i>Case Presentation:</i></b> This case describes a pancreatic cancer patient harboring a <i>ROS1</i> point mutation that occurred without an underlying <i>ROS1</i> rearrangement and thus not in the resistance situation. The heavily pretreated patient showed a strong decrease of the tumor biomarkers (CA19-9 and CEA) and radiologically a durable stable disease to the targeted treatment with lorlatinib, thereby achieving a progression-free survival of 12 months. <b><i>Conclusion:</i></b> Our data are the first to show a clinical benefit from targeted treatment with ROS1 TKI in a cancer patient with a thus far undescribed <i>ROS1</i> point mutation without a concomitant <i>ROS1</i> rearrangement. Furthermore, they indicate that <i>ROS1</i> could be an oncogenic driver in pancreatic cancer. This subgroup could be eligible for targeted treatments, which may contribute to the urgently needed improvement in patient outcome.

scholarly journals hTERT mRNA dendritic cell vaccination: complete response in a pancreatic cancer patient associated with response against several hTERT epitopes

2011 ◽  
Vol 60 (6) ◽  
pp. 809-818 ◽  
Author(s):  
Else M. Inderberg Suso ◽  
Svein Dueland ◽  
Anne-Marie Rasmussen ◽  
Turid Vetrhus ◽  
Steinar Aamdal ◽  
...  
Keyword(s):  
Pancreatic Cancer ◽  
Dendritic Cell ◽  
Cancer Patient ◽  
Complete Response ◽  
Pancreatic Cancer Patient ◽  
Dendritic Cell Vaccination ◽  
Htert Mrna

scholarly journals European Loans and Visits System (ELViS) as a Use Case for a Collection Descriptions Standard

2020 ◽  
Vol 4 ◽  
Author(s):  
Sharif Islam
Keyword(s):  
Data Model ◽  
User Requirements ◽  
Use Case ◽  
Current Development ◽  
Heterogeneous Datasets ◽  
Application Programming ◽  
Data Elements ◽  
Programming Interfaces ◽  
Improve Access ◽  
Insight Into

The European Loans and Visits System (ELViS) is an e-service in development designed to improve access to natural history collections across Europe. Bringing together heterogeneous datasets about institutions, people, collections and specimens, ELViS will provide an e-service (with application programming interfaces (APIs) and portal) that handles various stages of collections-based research. One of the main functionalities of ELViS is to facilitate loan and visit requests related to collections. To facilitate activities such as searching for collections, requesting loans, generating reports on collection usage, and ensuring interoperability with existing and new systems and services, ELViS must use a standard way of describing collections. In this talk, I show how ELViS can use the Collection Descriptions (CD) standard currently being developed by the CD Task Group at TDWG. I will provide a brief introduction to ELViS, summarise the current development efforts, and show how the Collection Description standard can support specific user requirements (gathered via an extensive set of user stories). I will also provide insight into the data elements within ELViS (see Fig. 1) and how they relate to the Collection Description data model.

scholarly journals Factors Associated with Ketamine Use in Pancreatic Cancer Patient in a Single Hospice Center

2016 ◽  
Vol 19 (3) ◽  
pp. 249-255
Author(s):  
Kyung Min Kwon ◽  
Yong Joo Lee ◽  
Chang Jin Choi ◽  
Chul Min Kim ◽  
Jo Hi Yoon ◽  
...  
Keyword(s):  
Pancreatic Cancer ◽  
Cancer Patient ◽  
Pancreatic Cancer Patient ◽  
Factors Associated
Sign in / Sign up

Export Citation Format

Share Document