Detection and analysis of drug non-compliance in internet fora using information retrieval approaches

International audienceIn the health-related field, drug non-compliance situations happen when patients do not follow their prescriptions and do actions which 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 with information retrieval methods in order to identify messages that contain drug non-compliance information. Exploitation of information retrieval methods permits to detect non-compliance messages with up to 0.529 F-measure, compared to 0.824 F-measure reached with supervized machine learning methods. For some fine-grained categories and on new data, it shows up to 0.70 Precision

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" = 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" = 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

Visualizing Food-Drug Interactions in the Theriaque 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

Stud Health Technol Inform

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

Public Health and Epidemiology Informatics

International audienceObjectives: To survey advances in public and population health and epidemiology informatics over the past 18 months. Methods: We conducted a review of English-language research works conducted in the domain of public and population health informatics and published in MEDLINE or Web of Science between January 2015 and June 2016 where information technology or informatics was a primary subject or main component of the study methodology. Selected articles were presented using a thematic analysis based on the 2011 American Medical Informatics Association (AMIA) Public Health Informatics Agenda tracks as a typology. Results: Results are given within the context developed by Dixon et al., (2015) and key themes from the 2011 AMIA Public Health Informatics Agenda. Advances are presented within a socio-technical infrastructure undergirded by a trained, competent public health workforce, systems development to meet the business needs of the practice field, and research that evaluates whether those needs are adequately met. The ability to support and grow the infrastructure depends on financial sustainability. Conclusions: The fields of public health and population health informatics continue to grow, with the most notable developments focused on surveillance, workforce development, and linking to or providing clinical services, which encompassed population health informatics advances. Very few advances addressed the need to improve communication, coordination, and consistency with the field of informatics itself, as identified in the AMIA agenda. This will likely result in the persistence of the silos of public health information systems that currently exist. Future research activities need to aim toward a holistic approach of informatics across the enterprise

Détection des effets indésirables des médicaments par un système de génération automatisée du signal adapté à la base nationale française de pharmacovigilance

L'évaluation et l'amélioration du rapport bénéfice/risque des médicaments, passe par la surveillance de leurs effets indésirables après leur mise sur le marché. La pharmacovigilance a pour principal objectif la détection des effets indésirables médicamenteux et repose essentiellement sur les notifications spontanées de ces effets. La pharmacovigilance française est confrontée à un flux de données très important sans qu'aucune méthode automatique ne permette d'éditer une liste de cas potentiellement suspects. Huit méthodes ont été étudiées : le "Proportional Reporting Ratio" (PRR), le "Reporting Odds Ratio" (ROR), le "Yule's Q", le "Sequential Probability Ration Test" (SPRT2), les probabilités de Poisson, le X2, l'"Information Component" (IC) et l'"Empirical Bayes Method" (EBAM). Les signaux obtenus avec chaque méthode ont été comparés à partir de données simulées, puis à partir des données réelles de la pharmacovigilance française.Evaluation and improvement of drugs risk/benefit ratio in population implies their adverse reactions surveillance after marketing. Pharmacovigilance main objective is to detect drugs adverse reactions relied mainly on spontaneous notifications. The French pharmacovigilance is faced to a very large data flow while no automatic method is available to edit a list of potentially suspected drug/adverse drug reaction associations. Eight methods were studied : Proportional Reporting Ratio (PRR), Reporting Odds Ratio (ROR), Uule's Q, Sequential Probability Ratio Test (SPRT2), Poisson's probabilities, X2, Information Component (IC), and Empirical Baye's Method (EBAM). Signals obtained with each method were compared through simulated data, then through real data from the French pharmacovigilance database.BORDEAUX2-BU Santé (330632101) / SudocPARIS-BIUP (751062107) / SudocSudocFranceF

Types de risque médical et leur traitement avec des méthodes de TAL

International audienceTypes of medical risk and their processing with the NLP methods The aim of this study is the medical risk and the way it is processed by NLP methods. The medical field is indeed widely concerned by risk. On one hand, the purpose of medicine is to treat patients who present multiple risks to develop pathologies. All steps of healthcare process are prone to a certain degree of uncertainty: diagnosis, individual reaction to treatment, evolution, etc. On the other hand, the healthcare process can also generate the risk for patients. We propose a classification of the medical risk. We also analyze with more detail some types of risk, and particularly those which are processed with the NLP methods.L'objectif de ce travail est d'étudier le risque médical et la manière dont il est traité avec les méthodes de TAL. Le domaine médical est en effet intimement lié avec le risque. D'une part, la médecine a la vocation de traiter les patients qui présentent des risques multiples de développer des maladies. Toutes les étapes de prise en charge sont entachées d'un degré plus ou moins important d'incertitude : incertitude diagnostique, incertitude de la réaction individuelle au traitement, incertitude de l'évo-lution, etc. D'autre part, le processus de soins médicaux peut également générer le risque chez les patients. Nous proposons une classification de risques médicaux. Nous analysons aussi plus en détail certains types de risques, en particulier ceux qui sont traités avec des méthodes de TAL