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. This study aimed at applying a text-mining approach to online social networks in order to identify early signs associated with endometriosis. Methods: An automated exploration technique of online forums was performed to extract posts. After a cleaning step of the built corpus, we retrieved all symptoms evoked by women, and connected them to the MedDRA dictionary. Then, temporal markers allowed targeting only the earliest symptoms. The latter were those evoked near a marker of precocity. A co-occurrence approach was further applied to better account for the context of evocations. Results: Results were visualised using the graph-oriented database Neo4j. We collected 7148 discussions threads and 78,905 posts from 10 French forums. We extracted 41 groups of contextualised symptoms, including 20 groups of early symptoms associated with endometriosis. Among these groups of early symptoms, 13 were found to portray already known signs of endometriosis. The remaining 7 clusters of early symptoms were limb oedema, muscle pain, neuralgia, haematuria, vaginal itching, altered general condition (i.e. dizziness, fatigue, nausea) and hot flush. Conclusion: We pointed out some additional symptoms of endometriosis qualified as early symptoms, which can serve as a screening tool for prevention and/or treatment purpose. The present findings offer an opportunity for further exploration of early biological processes triggering this disease.

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. The reuse of primary care data allows to study outpatient clinical data such as laboratory results and drug prescriptions, which are not documented in claims and hospital databases. In this study, we selected the population of treated diabetics from the Wattrelos primary care data warehouse, in North of France. Firstly, we studied the laboratory results of diabetics by identifying whether the recommendations of the French National Authority for Health (HAS) were respected. In a second step, we studied the prescriptions of diabetics by identifying the oral hypoglycemic agents treatments and insulins treatments. The diabetic population represents 690 patients of the health care center. The recommendations on labortatory are respected for 84% of diabetics. The majority of diabetics are treated with oral hypoglycemic agents 68.6%. As recommended by the HAS, metformin is the first-line treatment in the diabetic population.

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.

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. MATERIAL: We searched the literature for publications that mentioned "data quality problems," "data quality taxonomy," "data quality assessment," or "dirty data." The publications were then reviewed, compared, summarized, and structured using a bottom-up approach, to provide an operational taxonomy of data quality problems. The latter were illustrated with fictional examples (though based on reality) from clinical databases. RESULTS: Twelve publications were selected, and 286 instances of data quality problems were identified and were classified according to six distinct levels of granularity. We used the classification defined by Oliveira et al to structure our taxonomy. The extracted items were grouped into 53 data quality problems. DISCUSSION: This taxonomy facilitated the systematic assessment of data quality in databases by presenting the data's quality according to their granularity. The definition of this taxonomy is the first step in the data cleaning process. The subsequent steps include the definition of associated quality assessment methods and data cleaning methods. CONCLUSION: Our new taxonomy enabled the classification and illustration of 53 data quality problems found in hospital databases.

Standardized Description of the Feature Extraction Process to Transform Raw Data Into Meaningful Information for Enhancing Data Reuse: Consensus Study.

BACKGROUND: Despite the many opportunities data reuse offers, its implementation presents many difficulties, and raw data cannot be reused directly. Information is not always directly available in the source database and needs to be computed afterwards with raw data for defining an algorithm. OBJECTIVE: The main purpose of this article is to present a standardized description of the steps and transformations required during the feature extraction process when conducting retrospective observational studies. A secondary objective is to identify how the features could be stored in the schema of a data warehouse. METHODS: This study involved the following 3 main steps: (1) the collection of relevant study cases related to feature extraction and based on the automatic and secondary use of data; (2) the standardized description of raw data, steps, and transformations, which were common to the study cases; and (3) the identification of an appropriate table to store the features in the Observation Medical Outcomes Partnership (OMOP) common data model (CDM). RESULTS: We interviewed 10 researchers from 3 French university hospitals and a national institution, who were involved in 8 retrospective and observational studies. Based on these studies, 2 states (track and feature) and 2 transformations (track definition and track aggregation) emerged. "Track" is a time-dependent signal or period of interest, defined by a statistical unit, a value, and 2 milestones (a start event and an end event). "Feature" is time-independent high-level information with dimensionality identical to the statistical unit of the study, defined by a label and a value. The time dimension has become implicit in the value or name of the variable. We propose the 2 tables "TRACK" and "FEATURE" to store variables obtained in feature extraction and extend the OMOP CDM. CONCLUSIONS: We propose a standardized description of the feature extraction process. The process combined the 2 steps of track definition and track aggregation. By dividing the feature extraction into these 2 steps, difficulty was managed during track definition. The standardization of tracks requires great expertise with regard to the data, but allows the application of an infinite number of complex transformations. On the contrary, track aggregation is a very simple operation with a finite number of possibilities. A complete description of these steps could enhance the reproducibility of retrospective studies.

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. The training offers, over two years, courses covering computer science, mathematics and statistics, health and biology. With more than 60 professors and lecturers, a total of 835 hours of classes (not including the mandatory 5 months of internship per year), this curriculum has enrolled a total of 53 students today. The feedback from the students and alumni allowed us identifying new needs in terms of training, which may help us to adapt the program for the coming academic years. In particular, we will offer an additional module covering data management, from the edition of the clinical report form to the implementation of a data warehouse with an ETL process. Git and application lifecycle management will be included in programming courses or multidisciplinary projects.

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. We implemented a R tutorial based on a synthetic data set using Jupyter Notebook for a better understanding of the data management workflow.

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. Results: Two hundred seventy-eight proteins associated with endometriosis symptomatology in the scientific literature were extracted. Thirty-five proteins were selected according to degree and betweenness scores criteria. The most enriched biological pathways associated with these symptoms were (i) Interleukin-4 and Interleukin-13 signaling (p = 1.11 x 10-16), (ii) Signaling by Interleukins (p = 1.11 x 10-16), (iii) Cytokine signaling in Immune system (p = 1.11 x 10-16), and (iv) Interleukin-10 signaling (p = 5.66 x 10-15). Conclusion: Our study identified some key proteins with the ability to modulate endometriosis symptomatology. Our findings indicate that both pro- and anti-inflammatory biological pathways may play important roles in the symptomatology of endometriosis. This approach represents a genuine systemic method that may complement traditional experimental studies. The current data can be used to identify promising biomarkers for early diagnosis and potential therapeutic targets.

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. Regarding primary care data, the implementation of this type of warehouse in a routine way is hindered in particular by the analysis of textual data provided by general practitioners during patient consultations. In our study we collected primary care data for standardization in a data warehouse. The purpose of this analysis was to assess the feasibility of analyzing primary care data, and particularly to study the consultations and prescriptions of the elderly patient contained in our primary care data warehouse.

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. They were able to conduct the research they wanted and appreciated being able to use this tool without having to program.

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. They had to adapt a R Notebook and complete 14 questions and to perform descriptive analyses. The average rate of correct answers was 90.7. Jupyter Notebook enabled novice users to easily and autonomously analyze data from Twitter.