Transforming Primary Care Data Into the Observational Medical Outcomes Partnership Common Data Model: Development and Usability Study.

Background: Patient-monitoring software generates a large amount of data that can be reused for clinical audits and scientific research. The Observational Health Data Sciences and Informatics (OHDSI) consortium developed the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) to standardize electronic health record data and promote large-scale observational and longitudinal research.

Objective: This study aimed to transform primary care data into the OMOP CDM format.

Identification of early symptoms of endometriosis through the analysis of online social networks: A social media study.

Objective: Endometriosis is a complex full-body inflammation disease with an average time to diagnosis of 7-10 years. Social networks give opportunity to patient to openly discuss about their condition, share experiences, and seek advice. Thus, data from social media may provide insightful data about patient's experience.

Publication Dynamics on Social Media During the Orpea Nursing Homes Scandal: A Twitter Analysis.

The release of a book denouncing mistreatment in French nursing home triggered a scandal which was conveyed on social networks. The objectives of this study were to study the temporal trends and dynamics of publication on Twitter during the scandal as well as to identify the main topics of discussion.The first one is spontaneous and completely aligned with the actuality and fed by media and family of residents, while the second one is out of step with current events and fed by the company involved in the scandal.

Description of a French Population of Diabetics Treated Followed up by General Practitioners.

In France, the prevalence of treated diabetes has been estimated at 4.6%, or more than 3 million people and 5.2% in Northern France.

Definition of a Practical Taxonomy for Referencing Data Quality Problems in Health Care Databases.

Introduction: Health care information systems can generate and/or record huge volumes of data, some of which may be reused for research, clinical trials, or teaching. However, these databases can be affected by data quality problems; hence, an important step in the data reuse process consists in detecting and rectifying these issues. With a view to facilitating the assessment of data quality, we developed a taxonomy of data quality problems in operational databases.

Exploration of the core protein network under endometriosis symptomatology using a computational approach.

Background: Endometriosis is defined by implantation and invasive growth of endometrial tissue in extra-uterine locations causing heterogeneous symptoms, and a unique clinical picture for each patient. Understanding the complex biological mechanisms underlying these symptoms and the protein networks involved may be useful for early diagnosis and identification of pharmacological targets.

Methods: In the present study, we combined three approaches (i) a text-mining analysis to perform a systematic search of proteins over existing literature, (ii) a functional enrichment analysis to identify the biological pathways in which proteins are most involved, and (iii) a protein-protein interaction (PPI) network to identify which proteins modulate the most strongly the symptomatology of endometriosis.

Data Management for Health Data Reuse: Proposal of a Standard Workflow and a R Tutorial with Jupyter Notebook.

The data collected in the clinical registries or by data reuse require some modifications in order to suit the research needs. Several common operations are frequently applied to select relevant patients across the cohort, combine data from multiple sources, add new variables if needed and create unique tables depending on the research purpose. We carried out a qualitative survey by conducting semi-structured interviews with 7 experts in data reuse and proposed a standard workflow for health data management.

Master's Degree in Health Data Science: Implementation and Assessment After Five Years.

Health data science is an emerging discipline that bridges computer science, statistics and health domain knowledge. This consists of taking advantage of the large volume of data, often complex, to extract information to improve decision-making. We have created a Master's degree in Health Data Science to meet the growing need for data scientists in companies and institutions.

Jupyter Notebooks for Introducing Data Science to Novice Users.

Data science is a bridge discipline involving computer science, statistics, and knowledge of the health field. We developed a Jupyter Notebook to enable novice users to easily and autonomously analyze data from social networks. We conducted an experimentation with non-programmer students.

Automated Twitter Extraction and Visual Analytics with Dashboards: Development and First Experimentations.

Information found in the social media may help to set up infoveillance and track epidemics, identify high-risk behaviours, or assess trends or feelings about a subject or event. We developed a dashboard to enable novice users to easily and autonomously extract and analyze data from Twitter. Eleven users tested the dashboard and considered the tool to be highly usable and useful.

Implementation of a Data Warehouse in Primary Care: First Analyses with Elderly Patients.

The implementation of clinical data warehouses has advanced in recent years. The standardization of clinical data in these warehouses has made it possible to carry out multicenter studies and to formalize the clinical vocabulary. However, there is limited insight into a patient's overall care pathway in the clinical domain.