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

Secondary use of health data: identification of locks and contributions from the Bordeaux University Hospital's data warehouse

L’utilisation secondaire des données de santé est un enjeu majeur, avec de nombreux domaines d’application (recherche, études et évaluation). Les établissements de santé sont l’un des producteurs des données de santé. L’objectif de ce travail était d’identifier les freins à l’utilisation secondaire des données de santé dans un établissement et de décrire l’apport de l’entrepôt de données de santé du CHU de Bordeaux dans ce cadre. Les freins à l’utilisation secondaire des données de santé ont été identifiés sur la base d’une revue de la littérature et d’une analyse des processus internes du CHU de Bordeaux. Les freins identifiés ont été classés dans cinq catégories : techniques (données complexes, en silo dans les différentes applications du système d’information hospitalier, hétérogènes), règlementaires (information individuelle par finalité, recueil de la non-opposition), organisationnels (absence de politique institutionnelle, absence de guichet unique), économiques (manque de moyen, retombées difficilement évaluables) et éthiques (respect du secret médical, pertinence des résultats des algorithmes). Agrégeant 12 sources de données au CHU de Bordeaux, le projet « entrepôt de données de santé » a l’ambition de devenir la plateforme centrale de l’utilisation secondaire des données de santé dans le respect des aspects de sécurité, réglementaires et éthiques. Un travail important reste encore à mener afin de prendre en compte l’ensemble des freins à l’utilisation secondaire des données de santé identifiés dans ce travail.Secondary use of health data is a major concern, with many areas of application (research, studies and evaluation). Hospitals are one of the main producers of health data. The objective of this work was to identify the barriers to the secondary use of health data in hospital context and to describe the contribution of the Bordeaux University Hospital's health data warehouse. The barriers to the secondary use of health data were identified on the basis of a literature review and an analysis of the internal processes of the Bordeaux University Hospital. Barriers were classified in five categories: technical barriers (complex data, organised in silo), regulatory barriers (individual information for each purpose, non-object collection, heterogeneous), organisational barriers (no institutional policy, no point of single contact), economic barriers (lack of resources, economic impact difficult to assess) and ethical barriers (respect for medical confidentiality, relevance of the results of the algorithms). Aggregating 12 health data sources at the Bordeaux University Hospital, the "health data warehouse" project aims to become the central platform for the secondary use of health data while respecting safety, regulatory and ethical aspects. Much work remains to be done to take into account all the obstacles to the secondary use of health data identified in this work

FIDEO: Food Interactions with Drugs Evidence Ontology

International audienceIn this paper we propose the Food Interactions with Drugs Evidence Ontology (FIDEO), an ontology used for annotation and retrieval of scientific articles about food-drug interactions. Currently available ontologies address mainly drugdrug interactions, but much less attention has been given to clinically significant food-drug interactions. This work proposes an extension of a drug interaction ontology following the METHONTOLOGY methodology with the goal of representing potential drug interactions with foods, food components and food categories. To evaluate the proposed formal ontological model, we discuss the results of populating the ontology with information from manually annotated abstracts and from a compendium

Stud Health Technol Inform

Secondary use of health data is made difficult in part because of large semantic heterogeneity. Many efforts are being made to align local terminologies with international standards. With increasing concerns about data privacy, we focused here on the use of machine learning methods to align biological data elements using aggregated features that could be shared as open data. A 3-step methodology (features engineering, blocking strategy and supervised learning) was proposed. The first results, although modest, are encouraging for the future development of this approach

The benefit of augmenting open data with clinical data-warehouse EHR for forecasting SARS-CoV-2 hospitalizations in Bordeaux area, France

International audienceObjective The aim of this study was to develop an accurate regional forecast algorithm to predict the number of hospitalized patients and to assess the benefit of the Electronic Health Records (EHR) information to perform those predictions. Materials and Methods Aggregated data from SARS-CoV-2 and weather public database and data warehouse of the Bordeaux hospital were extracted from May 16, 2020 to January 17, 2022. The outcomes were the number of hospitalized patients in the Bordeaux Hospital at 7 and 14 days. We compared the performance of different data sources, feature engineering, and machine learning models. Results During the period of 88 weeks, 2561 hospitalizations due to COVID-19 were recorded at the Bordeaux Hospital. The model achieving the best performance was an elastic-net penalized linear regression using all available data with a median relative error at 7 and 14 days of 0.136 [0.063; 0.223] and 0.198 [0.105; 0.302] hospitalizations, respectively. Electronic health records (EHRs) from the hospital data warehouse improved median relative error at 7 and 14 days by 10.9% and 19.8%, respectively. Graphical evaluation showed remaining forecast error was mainly due to delay in slope shift detection. Discussion Forecast model showed overall good performance both at 7 and 14 days which were improved by the addition of the data from Bordeaux Hospital data warehouse. Conclusions The development of hospital data warehouse might help to get more specific and faster information than traditional surveillance system, which in turn will help to improve epidemic forecasting at a larger and finer scale

J Biomed Semantics

The increasing number of articles on adverse interactions that may occur when specific foods are consumed with certain drugs makes it difficult to keep up with the latest findings. Conflicting information is available in the scientific literature and specialized knowledge bases because interactions are described in an unstructured or semi-structured format. The FIDEO ontology aims to integrate and represent information about food-drug interactions in a structured way. This article reports on the new version of this ontology in which more than 1700 interactions are integrated from two online resources: DrugBank and Hedrine. These food-drug interactions have been represented in FIDEO in the form of precompiled concepts, each of which specifies both the food and the drug involved. Additionally, competency questions that can be answered are reviewed, and avenues for further enrichment are discussed.Maladies, Interactions Alimentation-MédicamentsKnowledge graph completion using Artificial Neural Networks for Herb-Drug Interaction discover

Enriching the FIDEO ontology with food-drug interactions from online knowledge sources

Abstract The increasing number of articles on adverse interactions that may occur when specific foods are consumed with certain drugs makes it difficult to keep up with the latest findings. Conflicting information is available in the scientific literature and specialized knowledge bases because interactions are described in an unstructured or semi-structured format. The FIDEO ontology aims to integrate and represent information about food-drug interactions in a structured way. This article reports on the new version of this ontology in which more than 1700 interactions are integrated from two online resources: DrugBank and Hedrine. These food-drug interactions have been represented in FIDEO in the form of precompiled concepts, each of which specifies both the food and the drug involved. Additionally, competency questions that can be answered are reviewed, and avenues for further enrichment are discussed

Ann Phys Rehabil Med

BACKGROUND: Robotic devices are often used in rehabilitation and might be efficient to improve walking capacity after stroke. OBJECTIVE: First to investigate the effects of robot-assisted gait training after stroke and second to explain the observed heterogeneity of results in previous meta-analyses. METHODS: All randomized controlled trials investigating exoskeletons or end-effector devices in adult patients with stroke were searched in databases (MEDLINE, EMBASE, CENTRAL, CINAHL, OPENGREY, OPENSIGLE, PEDRO, WEB OF SCIENCE, CLINICAL TRIALS, conference proceedings) from inception to November 2019, as were bibliographies of previous meta-analyses, independently by 2 reviewers. The following variables collected before and after the rehabilitation program were gait speed, gait endurance, Berg Balance Scale (BBS), Functional Ambulation Classification (FAC) and Timed Up and Go scores. We also extracted data on randomization method, blinding of outcome assessors, drop-outs, intention (or not) to treat, country, number of participants, disease duration, mean age, features of interventions, and date of outcomes assessment. RESULTS: We included 33 studies involving 1466 participants. On analysis by subgroups of intervention, as compared with physiotherapy alone, physiotherapy combined with body-weight support training and robot-assisted gait training conferred greater improvement in gait speed (+0.09 m/s, 95% confidence interval [CI] 0.03 to 0.15; p= 0.002), FAC scores (+0.51, 95% CI 0.07 to 0.95; p=0.022) and BBS scores (+4.16, 95% CI 2.60 to 5.71; p=0.000). A meta-regression analysis suggested that these results were underestimated by the attrition bias of studies. CONCLUSIONS: Robot-assisted gait training combined with physiotherapy and body-weight support training seems an efficient intervention for gait recovery after stroke

Evaluating the Relevance of Virtual Drugs

International audienceInformation 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

Linkage of Hospital Records and Death Certificates by a Search Engine and Machine Learning

INTRODUCTION: Vital status is of central importance to hospital clinical research. However, hospital information systems record only in-hospital death information. Recently, the French government released a publicly available dataset containing death-certificate data for over 25 million individuals. The objective of this study was to link French death certificates to the Bordeaux University Hospital records to complete the vital status information. MATERIALS AND METHODS: Our linkage strategy was composed of a search engine to reduce the number of comparisons and machine-learning algorithms. The overall pipeline was evaluated by assembling a file containing 3,565 in-hospital deaths and 15,000 alive persons. RESULTS: The recall and precision of our linkage strategy were 97.5% and 99.97% for the upper threshold and 99.4% and 98.9% for the lower threshold, respectively. CONCLUSION: In this study, we demonstrated the feasibility of accurately linking hospital records with death certificates using a search engine and machine learning