Evaluating the Relevance of Virtual Drugs.

Information about drugs is numerous and varied, and many drugs can share the same information. Grouping drugs that have common characteristics can be useful to avoid redundancy and facilitate interoperability. Our work focused on the evaluation of the relevance of classes allowing this type of grouping: the "Virtual Drug". Thus, in this paper, we describe the process of creating this class from the data of the French Public Drug Database, which is then evaluated against the codes of the Anatomical Therapeutic Chemical classification associated with the drugs. Our evaluation showed that 99.55% of the "Virtual Drug" classes have a good intra-class consistency.

Visualizing Food-Drug Interactions in the Thériaque Database.

This paper presents a prototype for the visualization of food-drug interactions implemented in the MIAM project, whose objective is to develop methods for the extraction and representation of these interactions and to make them available in the Thériaque database. The prototype provides users with a graphical visualization showing the hierarchies of drugs and foods in front of each other and the links between them representing the existing interactions as well as additional details about them, including the number of articles reporting the interaction. The prototype is interactive in the following ways: hierarchies can be easily folded and unfolded, a filter can be applied to view only certain types of interactions, and details about a given interaction are displayed when the mouse is moved over the corresponding link. Future work includes proposing a version more suitable for non-health professional users and the representation of the food hierarchy based on a reference classification.

SmartCRF: A Prototype to Visualize, Search and Annotate an Electronic Health Record from an i2b2 Clinical Data Warehouse.

Clinical information in electronic health records (EHRs) is mostly unstructured. With the ever-increasing amount of information in patients' EHRs, manual extraction of clinical information for data reuse can be tedious and time-consuming without dedicated tools. In this paper, we present SmartCRF, a prototype to visualize, search and ease the extraction and structuration of information from EHRs stored in an i2b2 data warehouse.

Enabling West African Herbal-Based Traditional Medicine Digitizing: The WATRIMed Knowledge Graph.

The purpose of this study is to describe the design and development of the first release of the West African Herbal based Traditional Medicine Knowledge Graph (WATRIMed). It is a resource containing Traditional Medicine (TM) related entities and linked with publicly available knowledge bases in order to facilitate bringing West African TM into the digital world. The core model comprises currently 556 concepts including 143 identified West African medicinal plants and 108 recipes used by tradi-practitioners to treat 110 diseases and symptoms which are commonly encountered in this part of the world.

Detecting Drug Non-Compliance in Internet Fora Using Information Retrieval and Machine Learning Approaches.

Non-compliance situations happen when patients do not follow their prescriptions and take actions that lead to potentially harmful situations. Although such situations are dangerous, patients usually do not report them to their physicians. Hence, it is necessary to study other sources of information. We propose to study online health fora. The purpose of our work is to explore online health fora with supervised classification and information retrieval methods in order to identify messages that contain drug non-compliance. The supervised classification method permits detection of non-compliance with up to 0.824 F-measure, while the information retrieval method permits detection non-compliance with up to 0.529 F-measure. For some fine-grained categories and new data, it shows up to 0.65-0.70 Precision.

Romedi: An Open Data Source About French Drugs on the Semantic Web.

The W3C project, "Linking Open Drug Data" (LODD), linked several publicly available sources of drug data together. So far, French data, like marketed drugs and their summary of product characteristics, were not integrated and remained difficult to query. In this paper, we present Romedi (Référentiel Ouvert du Médicament), an open dataset that links French data on drugs to international resources. The principles and standard recommendations created by the W3C for sharing information were adopted. Romedi was connected to the Unified Medical Language System and DrugBank, two central resources of the LODD project. A SPARQL endpoint is available to query Romedi and services are provided to annotate textual content with Romedi terms. This paper describes its content, its services, its links to external resources, and expected future developments.

Appropriateness of psychotropic drug prescriptions in the elderly: structuring tools based on data extracted from the hospital information system to understand physician practices.

BACKGROUND: The appropriateness of psychotropic prescriptions in the elderly is a major quality-of-care challenge at hospital. Quality indicators have been developed to prevent inappropriate psychotropic prescriptions. We aimed to select and automatically calculate such indicators, from the Bordeaux University Hospital information system, and to analyze the appropriateness of psychotropic prescription practices, in an observational study. METHODS: Experts selected indicators of the appropriateness of psychotropic prescriptions in hospitalized elderly patients, according to guidelines from the French High Authority for Health. The indicators were reformulated to focus on psychotropic administrations. The automated calculation of indicators was analyzed by comparing their measure to data collected from a clinical audit. In elderly patients hospitalized between 2014 and 2015, we then analyzed the evolution of the appropriateness of psychotropic prescription practices during hospital stay, using methods of visualization, and described practices by considering patients' characteristics. RESULTS: Two indicators were automated to detect overuse and misuse of psychotropic drugs. Indicators identified frequent inappropriate drug administrations, but practices tended to become more appropriate after quality-of-care improvement actions. In the majority of patients (85%), there was no inappropriate administration of psychotropic drugs during hospital stay; for the remaining 15% with at least one inappropriate administration, physicians tended to limit overuse or misuse during hospital stay. Inappropriate administrations were more frequent in patients suffering from psychiatric disorders, dependence and associated complications or morbidities. CONCLUSIONS: The automated indicators are structuring tools for the development of a drug prescription monitoring system. Inappropriate psychotropic administrations were limited by physicians during hospital stay; some inappropriate prescriptions might be explained by clinical characteristics of patients.

Risk of Drug-Drug Interactions in Out-Hospital Drug Dispensings in France: Results From the DRUG-Drug Interaction Prevalence Study.

Introduction: Drug interactions could account for 1% of hospitalizations in the general population and 2-5% of hospital admissions in the elderly. However, few data are available on the drugs concerned and the potential severity of the interactions encountered. We thus first aimed to estimate the prevalence of dispensings including drugs Contraindicated or Discommended because of Interactions (CDI codispensings) and to identify the most frequently involved drug pairs. Second, we aimed to investigate whether the frequency of CDI codispensings appeared higher or lower than the expected for the drugs involved. Methods: We carried out a study using a random sample of all drugs dispensings registered in a database of the French Health Insurance System between 2010 and 2015. The distribution of the drugs involved was described considering active principles, detailing the 20 most frequent ones for both contraindicated or discommended codispensings (DCs). To investigate whether the frequency of CDI codispensings appeared higher or lower than the expected for the drugs involved, we developed a specific indicator, the Drug-drug interaction prevalence study-score (DIPS-score), that compares for each drug pair the observed frequency of codispensing to its expected probability. The latter is determined considering the frequencies of dispensings of the individual drugs constituting a pair of interest. Results: We analyzed 6,908,910 dispensings: 13,196 (0.2%) involved contraindicated codispensings (CCs), and 95,410 (1.4%) DCs. For CCS, the most frequently involved drug pair was "bisoprolol+flecainide" (n = 5,036); four out of five of the most represented pairs involved cardiovascular drugs. For DCS, the most frequently involved drug pair was "ramipril+spironolactone" (n = 4,741); all of the five most represented pairs involved cardiovascular drugs. The drug pair involved in the CC with the highest score value was "citalopram+hydroxyzine" (DIPS-score: 3.7; 2.9-4.6); that with the lowest score was "clarithromycin+simvastatin" (DIPS-score: 0.2; 0.2-0.3). DIPS-score median value was 0.4 for CCs and 0.6 for DCs. Conclusion: This high prevalence of CDI codispensings enforces the need for further risk-prevention actions regarding drug-drug interactions (DDIs), especially for arrhythmogenic or anti-arrhythmic drugs. In this perspective, the DIPS-score we develop could ease identifying the interactions that are poorly considered by clinicians/pharmacists and targeting interventions.

Design Considerations for a Knowledge Graph: The WATRIMed Use Case.

The World Health Organization estimates that as much as 80% of the population uses Traditional Medicine (TM) in some form, and in particular, herbal-based Traditional Medicine (HTM). However, TM is mostly orally transmitted and suffers from lack of standardizations and lack of computable TM data. Shareable standards could enable computational support of TM data management. In this paper, we outline the design and development of the West African Herbal Traditional Medicine (WATRIMed) Knowledge Graph (KG), which is an effort for bringing West Africa TM to the digital world and help establishing bridges with conventional medicine. WATRIMed entities have been enriched with knowledge from external publicly available knowledge bases and further mapped with the BioTopLite Upper Level Ontology. As of result, the model of the publicly available KG currently comprises 472 Concepts and 75 Properties (57 object properties and 18 data properties). It describes formally 115 medicinal plants, 179 chemical compounds and 67 recipes.

Timeline representation of clinical data: usability and added value for pharmacovigilance.

BACKGROUND: Pharmacovigilance consists in monitoring and preventing the occurrence of adverse drug reactions (ADR). This activity requires the collection and analysis of data from the patient record or any other sources to find clues of a causality link between the drug and the ADR. This can be time-consuming because often patient data are heterogeneous and scattered in several files. To facilitate this task, we developed a timeline prototype to gather and classify patient data according to their chronology. Here, we evaluated its usability and quantified its contribution to routine pharmacovigilance using real ADR cases. METHODS: The timeline prototype was assessed using the biomedical data warehouse eHOP (from entrepôt de données biomédicales de l'HOPital) of the Rennes University Hospital Centre. First, the prototype usability was tested by six experts of the Regional Pharmacovigilance Centre of Rennes. Their experience was assessed with the MORAE software and a System and Usability Scale (SUS) questionnaire. Then, to quantify the timeline contribution to pharmacovigilance routine practice, three of them were asked to investigate possible ADR cases with the "Usual method" (analysis of electronic health record data with the DxCare software) or the "Timeline method". The time to complete the task and the data quality in their reports (using the vigiGrade Completeness score) were recorded and compared between methods. RESULTS: All participants completed their tasks. The usability could be considered almost excellent with an average SUS score of 82.5/100. The time to complete the assessment was comparable between methods (P = 0.38) as well as the average vigiGrade Completeness of the data collected with the two methods (P = 0.49). CONCLUSIONS: The results showed a good general level of usability for the timeline prototype. Conversely, no difference in terms of the time spent on each ADR case and data quality was found compared with the usual method. However, this absence of difference between the timeline and the usual tools that have been in use for several years suggests a potential use in pharmacovigilance especially because the testers asked to continue using the timeline after the evaluation.

Clinical Data Analytics With Time-Related Graphical User Interfaces: Application to Pharmacovigilance.

Pharmacovigilance consists in monitoring and preventing the occurrence of adverse drug reactions. This activity can be time-consuming because it requires the collection of both patient and medication information. In this paper, we present two visualization and data mining applications to make this task easier for the practitioner. These tools have been developed and tested using the biomedical data warehouse eHOP (Hospital Biomedical Data Warehouse) of the Rennes University Hospital Centre. The first application is a tool to visualize the patient electronic health record in the form of a timeline. All patient data is collected and displayed chronologically. The usability test of the timeline has been very positive (SUS score: 82.5) and the tool is now available for practitioners in their daily practice. The second application is a tool to visualize and search the sequences of a patient cohort. The visual interface allow user to quickly visualize sequences. A query builder allows user to search for sequences in relation with a reference sequence, such as a prescription sequence followed by an abnormal biological value. The sequences are then visually aligned with this reference sequence and ranked by similarity. The GSP (Generalized Sequential Pattern) and Apriori algorithms allow us to display a summary of the sequences list by searching for common sequences and associations. The tool was tested on a use case which consisted in detection of inappropriate drug administration. Compared to a random order, we showed this ranking system saved the practitioner time in this task (to analyze one sequence, 3.49 ± 3.54 vs. 2.26 ± 2.86 s, p = 0.0003). These two visualization and data mining applications will help the daily practice of pharmacovigilance.

Detection and Analysis of Drug Misuses. A Study Based on Social Media Messages.

Drug misuse may happen when patients do not follow the prescriptions and do actions which lead to potentially harmful situations, such as intakes of incorrect dosage (overuse or underuse) or drug use for indications different from those prescribed. Although such situations are dangerous, patients usually do not report the misuse of drugs to their physicians. Hence, other sources of information are necessary for studying these issues. We assume that online health fora can provide such information and propose to exploit them. The general purpose of our work is the automatic detection and classification of drug misuses by analysing user-generated data in French social media. To this end, we propose a multi-step method, the main steps of which are: (1) indexing of messages with extended vocabulary adapted to social media writing; (2) creation of typology of drug misuses; and (3) automatic classification of messages according to whether they contain drug misuses or not. We present the results obtained at different steps and discuss them. The proposed method permit to detect the misuses with up to 0.773 F-measure.

Artificial Intelligence in Public Health and Epidemiology.

OBJECTIVES: To introduce and summarize current research in the field of Public Health and Epidemiology Informatics. METHODS: The 2017 literature concerning public health and epidemiology informatics was searched in PubMed and Web of Science, and the returned references were reviewed by the two section editors to select 14 candidate best papers. These papers were then peer-reviewed by external reviewers to provide the editorial team with an enlightened vision to select the best papers. RESULTS: Among the 843 references retrieved from PubMed and Web of Science, two were finally selected as best papers. The first one analyzes the relationship between the disease, social/mass media, and public emotions to understand public overreaction (leading to a noticeable reduction of social and economic activities) in the context of a nation-wide outbreak of Middle East Respiratory Syndrome (MERS) in Korea in 2015. The second paper concerns a new methodology to de-identify patient notes in electronic health records based on artificial neural networks that outperformed existing methods. CONCLUSIONS: Surveillance is still a productive topic in public health informatics but other very important topics in Public Health are appearing. For example, the use of artificial intelligence approaches is increasing.

Integrating Biobank Data into a Clinical Data Research Network: The IBCB Project.

Development of biobanks is still hampered by difficulty to collect high quality sample annotations using patient clinical information. The IBCB project evaluated the feasibility of a nationwide clinical data research network for this purpose. METHOD: the infrastructure, based on eHOP and I2B2 technologies, was interfaced with the legacy IT components of 3 hospitals. The evaluation focused on the data management process and tested 5 expert queries in Hepatocarcinoma. RESULTS: the integration of biobank data was comprehensive and easy. Five out of 5 queries were successfully performed and shown consistent results with the data sources excepted one query which required to search in unstructured data. The platform was designed to be scalable and showed that with few effort biobank data and clinical data can be integrated and leveraged between hospitals. Clinical or phenotyping concepts extraction techniques from free text could significantly improve the samples annotation with fine granularity information.

Typology of Drug Misuse Created from Information Available in Health Fora.

Patients seldom report the misuse of drugs to their physicians. Hence, other sources of information are necessary for studying these issues. We assume that online health fora can provide such information and propose to exploit them for building a typology of drug misuses. The misuses detected are structured according to the goals of patients: we distinguished three types of non-intentional misuses and 14 types of intentional misuses. This work will be used to guide future task of automatic extraction of drug misuses.

Requests for post-registration studies (PRS), patients follow-up in actual practice: Changes in the role of databases.

Early market access of health products is associated with a larger number of requests for information by the health authorities. Compared with these expectations, the growing expansion of health databases represents an opportunity for responding to questions raised by the authorities. The computerised nature of the health system provides numerous sources of data, and first and foremost medical/administrative databases such as the French National Inter-Scheme Health Insurance Information System (SNIIRAM) database. These databases, although developed for other purposes, have already been used for many years with regard to post-registration studies (PRS). The use thereof will continue to increase with the recent creation of the French National Health Data System (SNDS [2016 health system reform law]). At the same time, other databases are available in France, offering an illustration of "product use under actual practice conditions" by patients and health professionals (cohorts, specific registries, data warehouses, etc.). Based on a preliminary analysis of requests for PRS, approximately two-thirds appeared to have found at least a partial response in existing databases. Using these databases has a number of disadvantages, but also numerous advantages, which are listed. In order to facilitate access and optimise their use, it seemed important to draw up recommendations aiming to facilitate these developments and guarantee the conditions for their technical validity. The recommendations drawn up notably include the need for measures aiming to promote the visibility of research conducted on databases in the field of PRS. Moreover, it seemed worthwhile to promote the interoperability of health data warehouses, to make it possible to match information originating from field studies with information originating from databases, and to develop and share algorithms aiming to identify criteria of interest (proxies). Methodological documents, such as the French National Authority for Health (HAS) recommendations on "Les études post-inscription sur les technologies de santé (médicaments, dispositifs médicaux et actes). Principes et méthodes" [Post-registration studies on health technologies (medicinal products, medical devices and procedures). Principles and methods] should be updated to incorporate these developments.

Definition of indicators of the appropriateness of oral anticoagulant prescriptions in hospitalized adults: Literature review and consensus (PACHA study).

BACKGROUND: Indicators of the appropriateness of oral anticoagulant prescriptions are lacking, despite the major contribution they could make to improve quality of care. AIM: To identify and select such indicators according to their utility and operational implementation. METHODS: A literature review was conducted to identify indicators of the appropriateness of oral anticoagulant prescriptions according to the guidelines of health authorities and European learned societies. A first list of indicators was identified from guidelines related to general or targeted clinical situations. A two-round Delphi consensus process, completed by a synthesis meeting, was then set up to ask European experts to rate the utility and operational implementation of the indicators on a qualitative binary scale. An indicator was selected if ≥80% of the experts judged it both useful and implementable (strong consensus). RESULTS: We selected 32 references, from which 84 indicators were identified. Nineteen indicators were short-listed for submission to expert judgment. Twenty-two experts participated in the Delphi process. Sixteen indicators obtained strong consensus for selection; three indicators did not achieve consensus. Two-thirds of the selected indicators focused on the appropriateness of oral anticoagulant prescriptions in general or in patients with atrial fibrillation; the other third focused on the appropriateness of prescriptions in patients with a prosthetic heart valve, venous thromboembolism or trauma. CONCLUSION: This work addresses the current lack of indicators of the appropriateness of oral anticoagulant prescriptions. The selected indicators will be implemented from the hospital information system to assess their metrological properties to detect inappropriate prescriptions.

Visualizing omics and clinical data: Which challenges for dealing with their variety?

Life sciences are currently going through a great number of transformations raised by the in-going revolution in high-throughput technologies for the acquisition of data. The integration of their high dimensionality, ranging from omics to clinical data, is becoming one of the most challenging stages. It involves inter-disciplinary developments with the aim to move towards an enhanced understanding of human physiology for caring purposes. Biologists, bioinformaticians, physicians and other experts related to the healthcare domain have to accompany each step of the analysis process in order to investigate and expertise these various data. In this perspective, methods related to information visualization are gaining increasing attention within life sciences. The softwares based on these methods are now well recognized to facilitate expert users' success in carrying out their data analysis tasks. This article aims at reviewing the current methods and techniques dedicated to information visualisation and their current use in software development related to omics or/and clinical data.

An Automated System Combining Safety Signal Detection and Prioritization from Healthcare Databases: A Pilot Study.

INTRODUCTION: Signal detection from healthcare databases is possible, but is not yet used for routine surveillance of drug safety. One challenge is to develop methods for selecting signals that should be assessed with priority. AIM: The aim of this study was to develop an automated system combining safety signal detection and prioritization from healthcare databases and applicable to drugs used in chronic diseases. METHODS: Patients present in the French EGB healthcare database for at least 1 year between 2005 and 2015 were considered. Noninsulin glucose-lowering drugs (NIGLDs) were selected as a case study, and hospitalization data were used to select important medical events (IME). Signal detection was performed quarterly from 2008 to 2015 using sequence symmetry analysis. NIGLD/IME associations were screened if one or more exposed case was identified in the quarter, and three or more exposed cases were identified in the population at the date of screening. Detected signals were prioritized using the Longitudinal-SNIP (L-SNIP) algorithm based on strength (S), novelty (N), and potential impact of signal (I), and pattern of drug use (P). Signals scored in the top 10% were identified as of high priority. A reference set was built based on NIGLD summaries of product characteristics (SPCs) to compute the performance of the developed system. RESULTS: A total of 815 associations were screened and 241 (29.6%) were detected as signals; among these, 58 (24.1%) were prioritized. The performance for signal detection was sensitivity = 47%; specificity = 80%; positive predictive value (PPV) 33%; negative predictive value = 82%. The use of the L-SNIP algorithm increased the early identification of positive controls, restricted to those mentioned in the SPCs after 2008: PPV = 100% versus PPV = 14% with its non-use. The system revealed a strong new signal with dipeptidylpeptidase-4 inhibitors and venous thromboembolism. CONCLUSION: The developed system seems promising for the routine use of healthcare data for safety surveillance of drugs used in chronic diseases.