Injection of Automatically Selected DBpedia Subjects in Electronic Medical Records to boost Hospitalization Prediction

International audienceAlthough there are many medical standard vocabularies available, it remains challenging to properly identify domain concepts in electronic medical records. Variations in the annotations of these texts in terms of coverage and abstraction may be due to the chosen annotation methods and the knowledge graphs, and may lead to very different performances in the automated processing of these annotations. We propose a semi-supervised approach based on DBpedia to extract medical subjects from EMRs and evaluate the impact of augmenting the features used to represent EMRs with these subjects in the task of predicting hospitalization. We compare the impact of subjects selected by experts vs. by machine learning methods through feature selection. Our approach was experimented on data from the database PRIMEGE PACA that contains more than 600,000 consultations carried out by 17 general practitioners (GPs)

Évaluation des améliorations de prédiction d'hospitalisation par l'ajout de connaissances métier aux dossiers médicaux

National audienceThe knowledge available through electronic medical records (EMR) themselves remains limited by the fact the features used by a machine learning algorithms from a text alone do not contain all the implicit information known by a domain expert. We propose and evaluate the ontological augmentations of features extracted from textual information from EMRs on several machine learning algorithms to predict hospitalization.Les dossiers médicaux électroniques (DME) contiennent des informations essentielles sur les différents épisodes symptomatiques qu'un patient a subis. Cependant, les connaissances disponibles à travers ces enregistrements restent limitées : les attributs extractibles à partir de ces textes pour un algorithme d'apprentissage ne contiennent pas toutes les informations implicites connues par un expert. Afin d'évaluer et de pallier ce problème, nous avons étudié l'impact de l'augmentation des textes et des informations textuelles en provenance des DMEs par des annotations ontologiques générées automatiquement à partir de leur analyse afin d'enrichir en amont les représentations vectorielles utilisées ensuite par des algorithmes d'apprentissage

Interface d'Aide à la Décision pour Prédire l'Hospitalisation de Patients et planifier les Actions Préventives pour Prévenir cet Événement

National audiencePhysicians are confronted with a constant increase in the number of their patients given the numerus clausus imposed on medical studies in France. In addition, the overall aging of the population requires them to treat patients with many diseases (comorbidity), which complicates the management of patients since polypharmacy implies the appearance of unexpected adverse drug’s effects. In this paper, we present the interface of an algorithm de-veloped to assist in the decision-making process of general practitioners (GPs) that allows them to identify in patients the first signs that lead to hospitalization and medical problems to be treated as a priorityLes médecins sont confrontés à une augmentation constante du nombre de leurs patients compte tenu du numerus clausus imposé aux études médicales en France. De plus, le vieillissement global de la population les oblige à traiter des patients atteints de nombreuses maladies chroniques (comorbidité), ce qui complique la prise en charge des patients puisque la polymédication implique l'apparition d'effets indésirables. Dans cet article, nous présentons l'interface d'un algorithme développé pour aider à la prise de décision des médecins généralistes qui leur permet d'identifier les premiers signes qui mènent les patients à l'hospitalisation et les problèmes médicaux à traiter en priorité

Educating Young Consumers about Food Hygiene and Safety with SafeConsume: A Multi-Centre Mixed Methods Evaluation

The SafeConsume educational suite was designed to improve knowledge about undertaking safer food practices and avoiding food-borne illnesses among young people. The resources were designed to support educators and members of the community who teach young people (aged 11–18 years) and include lesson plans and supporting teacher training resources. To assess the efficacy and suitability of the resources, an evaluation of the central lesson, the ‘user journey’, was conducted within four European countries. The mixed-methods evaluation included the following elements: a pre- and post-scenario-based questionnaire, a satisfaction questionnaire, focus groups with students; and interviews with teachers. Data from the scenario-based questionnaires were analysed using a mixed effects normal linear regression model. Qualitative data were thematically analysed, and the main themes were discussed. A total of 171 students and 9 educators took part from schools based in Portugal, Hungary, France and England. The results indicated a significant improvement in students’ knowledge and understanding of appropriate food hygiene practices overall, although this varied among countries. The resources were found to be well-suited to help teachers deliver the lesson, being considered by teachers to be both informative and flexible. Minor alternations were suggested, including alterations to lesson delivery or breaking the lesson into smaller sections, and increasing the lesson’s interactivity.info:eu-repo/semantics/publishedVersio

Young People’s Views on Food Hygiene and Food Safety: A Multicentre Qualitative Study

Foodborne diseases are a global burden, are preventable, and young people are a key population for behaviour change as they gain autonomy. This study aimed to explore young people’s needs across several European countries in relation to learning about and implementing food hygiene and food safety. Qualitative focus groups and interviews were conducted in rural and city regions across England, France, Hungary and Portugal. Data were collected to attain data saturation, transcribed, thematically analysed, and mapped to the Theoretical Domains Framework. Twenty-five out of 84 schools approached (29.8%) participated, with data collected from 156 11–18-year-old students. Students had good knowledge of personal hygiene but did not always follow hygiene rules due to forgetfulness, lack of facilities or lack of concern for consequences. Students had limited understanding of foodborne microbes, underestimated the risks and consequences of foodborne illness and perceived the “home” environment as the safest. Young people preferred interactive educational methods. Addressing gaps in young people’s food safety knowledge is essential to improve their lack of concern towards foodborne illness and motivate them to follow food hygiene and safety behaviours consistently. Findings have been used to develop educational resources to address gaps in knowledge, skills, attitudes and beliefs.info:eu-repo/semantics/publishedVersio

Evidence-based health interventions for the educational sector: Application and lessons learned from developing European food hygiene and safety teaching resources

Background: Foodborne illnesses have a significant global burden and can be life-threatening, but good food hygiene practice can prevent most. SafeConsume is an EU-funded, transdisciplinary project aiming to improve consumers’ food safety behaviour and reduce the burden of foodborne illness. Young people are at risk of foodborne illness and research indicates a lack of knowledge or concern about food hygiene. Educational settings provide an opportunity to influence behaviour; but for resources to be effective and implementable, they should be evidence-based and thoughtfully designed. Aim: To develop educational resources to teach food hygiene and food safety to school children aged 11–18 years old, through a user-based approach, specific to the educational setting. Methods: Development used a two-step process referred to as: the insight phase; and prototyping and refinement phase. This included using the findings of a needs assessment with students and educators based on the Theo-retical Domains Framework (TDF) presented in earlier publications (Eley et al., 2021; Syeda et al., 2021). A user-centred approach to development was then taken, employing an iterative process of idea generation, consultation with a multidisciplinary steering group, and user testing. Results: The insight phase identified students’ and educators’ deficiencies in knowledge and skills, and cultural and social influences on food safety behaviours. This phase, including Curriculum analysis informed student learning objectives and educator training topics. Following a round of development and consultation, a total of seven teaching resources were developed, with four educator training modules to improve knowledge and confidence of educators. Conclusions: Behavioural theory is a useful foundation for the development of school-based health interventions, which aim to positively influence students’ knowledge, behaviour, and attitudes. To support educators’ uptake, materials should be aligned to the national curriculum and should consider practical factors like time and environmental factors. By working closely with stakeholders at all stages of development, barriers to use, implementation and efficacy can be identified and mitigated.info:eu-repo/semantics/publishedVersio

Utilisation des enregistrements médicaux électroniques, exemple d’utilisation dans le cadre du projet PRIMEGE PACA ; quels sont les principaux motifs de recours, diagnostics et prescriptions en soins primaires.

Contexte En France il n’existe aucun système d’information public associant motifs de recours aux médecins, actes pratiqués ou décisions prises en cours de séance.ObjectifLes objectifs de notre étude était d’étudier la faisabilité de recueil des dossiers médicaux électroniques (DME) afin de constituer une base de données en médecine générale dans le cadre du projet PRIMEGE PACA, puis d’utiliser ces données afin de décrire les principaux motifs de recours, diagnostics ainsi que les principales prescriptions médicamenteuses.Méthodeles DME de 2 logiciels médicaux ont été recueillis et importés dans une base de données MySQL. Afin de palier au faible taux de codage des médecins membres du réseau, une procédure permettant de transformer du texte libre en code CISP 2 a été implémentée.RésultatsLes données de 11 médecins sur 4 ans (2012 à 2015) ont été extraites représentant ; 205 343 consultations. La procédure de codage automatique a permis d’associer la plupart des motifs et des diagnostics à un code CISP2 (97,05% des motifs et 96,83% des diagnostics). Les principauxmotifs de recours rencontrés sont d’ordre respiratoire (23,51%), ostéo-articulaire (17,70%), général (14,11%) et digestif (11,88%). Concernant les diagnostics, ils sont principalement d’ordre ostéoarticulaire (19,11%), respiratoire (16,90%), digestif (9,51%) et cardio-vasculaire (9,24%). Lesprescriptions concernant le système nerveux, le système digestif et métabolisme, le système cardiovasculaire, le système respiratoire et le système musculo-squelettique sont les plus nombreuses et représentent 71,96% des cas.ConclusionNous avons pu développer une base de données en médecine générale basée sur la CISP2. De nouveaux logiciels sont à l’étude afin d’être intégrés dans notre base et de permettre l’inclusion de nouveaux médecins membres. Ce modèle régional pourrait se décliner sous forme d’unréseau d’observatoires régionaux fournissant ainsi un panel représentatif de médecins généralistes « producteurs » de donnée

Extending electronic medical records vector models with knowledge graphs to improve hospitalization prediction

International audienceBackground: Artificial intelligence methods applied to electronic medical records (EMRs) hold the potential to help physicians save time by sharpening their analysis and decisions, thereby improving the health of patients. On the one hand, machine learning algorithms have proven their effectiveness in extracting information and exploiting knowledge extracted from data. On the other hand, knowledge graphs capture human knowledge by relying on conceptual schemas and formalization and supporting reasoning. Leveraging knowledge graphs that are legion in the medical field, it is possible to pre-process and enrich data representation used by machine learning algorithms. Medical data standardization is an opportunity to jointly exploit the richness of knowledge graphs and the capabilities of machine learning algorithms. Methods: We propose to address the problem of hospitalization prediction for patients with an approach that enriches vector representation of EMRs with information extracted from different knowledge graphs before learning and predicting. In addition, we performed an automatic selection of features resulting from knowledge graphs to distinguish noisy ones from those that can benefit the decision making. We report the results of our experiments on the PRIMEGE PACA database that contains more than 600,000 consultations carried out by 17 general practitioners (GPs). Results: A statistical evaluation shows that our proposed approach improves hospitalization prediction. More precisely, injecting features extracted from cross-domain knowledge graphs in the vector representation of EMRs given as input to the prediction algorithm significantly increases the F1 score of the prediction. Conclusions: By injecting knowledge from recognized reference sources into the representation of EMRs, it is possible to significantly improve the prediction of medical events. Future work would be to evaluate the impact of a feature selection step coupled with a combination of features extracted from several knowledge graphs. A possible avenue is to study more hierarchical levels and properties related to concepts, as well as to integrate more semantic annotators to exploit unstructured data

Utilisation des enregistrements médicaux électroniques dans le cadre du projet PRIMEGE PACA

International audienceBackground. In France, there is no public information system associating reasons for consultation, acts performed, or decisions made during the session. This situation impelled us to create the data collection PRIMEGE PACA (Plateforme Regionale d'Information en MEdecine GEnerale en Provence-Alpes-Cote d'Azur). Its purposes are the improvement of existing professional practices and academic research. Objectives. To assess the feasibility of creating a database from the Electronic Health records (EHRs). To provide examples of the use of this database by describing the main reasons for consultation, diagnoses and drug prescriptions. Methods. EHRs of 11 general practitioners were collected and imported into a MySQL (R) database. To overcome the low coding rate of the physicians, a procedure allowing translation free text into ICPC-2 was implemented. Results. The medical records of 11 general practitioners over 4 years (2012 to 2015) were extracted representing: 205 343 consultations. The automatic coding procedure allowed us to associate main reasons for consultation and diagnoses with an ICPC-2 code (about 97% of each). The main reasons were respiratory (23.5%), osteoarticular (17.7%), general (14.1%) and digestive (11.9%). Diagnoses were mainly osteoarticular (19.1%), respiratory (16.9%), digestive (9.5%) and cardiovascular (9.2%). Prescriptions concerning the nervous system (22.2%), the digestive system and metabolism (15.8%), the cardiovascular system (12.5%) and the respiratory system (12.2%) were the most numerous. Conclusion. This study has shown that it is possible to create a database from electronic health records. New software is being studied to be integrated into the database and will allow inclusion of new physicians. This regional model could be configured in a network of regional observatories providing a representative panel of general practitioners ``Producers'' of data.Contexte. En France, il n’existe aucun système d’information public associant les motifs de recours aux médecins, les procédures de soins et les actes pratiqués en cours de séance. Ce constat a motivé la création du recueil de données PRIMEGE PACA (Plateforme Régionale d’Information en MÉdecine GÉnerale en Provence-Alpes-Côte d’Azur), dont la finalité est l’amélioration des pratiques professionnelles et la recherche académique. Objectifs. Étudier la faisabilité de la création d’une base de données à partir des dossiers médicaux électroniques (DME). Fournir des exemples d’utilisation de cette base en décrivant les principaux motifs de recours, diagnostics et prescriptions en médecine générale. Méthodes. Les DME de 11 médecins généralistes ont été recueillis et importés dans une base de données MySQL®. Afin de pallier le faible taux de codage des médecins membres du projet PRIMEGE PACA, une procédure permettant de transformer du texte libre en code CISP-2 a été implémentée. Résultats. Les dossiers médicaux de 11 médecins sur 4 ans (2012 à 2015) ont été extraits, représentant 205 343 consultations. La procédure de codage automatique a permis d’associer la plupart des motifs et des diagnostics à un code CISP-2 (environ 97 % des motifs et des diagnostics). Les principaux motifs de recours rencontrés ont été d’ordre respiratoire (23,5 %), ostéoarticulaire (17,7 %), général (14,1 %) et digestif (11,9 %). Concernant les diagnostics, ils ont été principalement d’ordre ostéoarticulaire (19,1 %), respiratoire (16,9 %), digestif (9,5 %) et cardiovasculaire (9,2 %). Les principales prescriptions ont concerné le système nerveux (22,2 %), le système digestif ou métabolisme (15,8 %), le système cardiovasculaire (12,5 %) et le système respiratoire (12,2 %). Conclusion. Cette étude a montré qu’il était possible de créer une base de données à partir des dossiers médicaux électroniques. De nouveaux logiciels sont à l’étude afin de pouvoir intégrer les DME qui en sont issus dans la base PRIMEGE PACA, et permettre l’inclusion de nouveaux médecins membres. Ce modèle régional pourrait se décliner sous forme d’un réseau d’observatoires régionaux fournissant ainsi un panel représentatif de médecins généralistes « producteurs » de données

Use of Electronic Health Records in the PRIMEGE PACA project. Main reasons of encounter, diagnoses and prescriptions in primary care

Background. In France, there is no public information system associating reasons for consultation, acts performed, or decisions made during the session. This situation impelled us to create the data collection PRIMEGE PACA (Plateforme Regionale d'Information en MEdecine GEnerale en Provence-Alpes-Cote d'Azur). Its purposes are the improvement of existing professional practices and academic research. Objectives. To assess the feasibility of creating a database from the Electronic Health records (EHRs). To provide examples of the use of this database by describing the main reasons for consultation, diagnoses and drug prescriptions. Methods. EHRs of 11 general practitioners were collected and imported into a MySQL (R) database. To overcome the low coding rate of the physicians, a procedure allowing translation free text into ICPC-2 was implemented. Results. The medical records of 11 general practitioners over 4 years (2012 to 2015) were extracted representing: 205 343 consultations. The automatic coding procedure allowed us to associate main reasons for consultation and diagnoses with an ICPC-2 code (about 97% of each). The main reasons were respiratory (23.5%), osteoarticular (17.7%), general (14.1%) and digestive (11.9%). Diagnoses were mainly osteoarticular (19.1%), respiratory (16.9%), digestive (9.5%) and cardiovascular (9.2%). Prescriptions concerning the nervous system (22.2%), the digestive system and metabolism (15.8%), the cardiovascular system (12.5%) and the respiratory system (12.2%) were the most numerous. Conclusion. This study has shown that it is possible to create a database from electronic health records. New software is being studied to be integrated into the database and will allow inclusion of new physicians. This regional model could be configured in a network of regional observatories providing a representative panel of general practitioners ``Producers'' of data