Use of a modular ontology and a semantic annotation tool to describe the care pathway of patients with amyotrophic lateral sclerosis in a coordination network.

Journal: PloS one
PMID:

Abstract

The objective of this study was to describe the care pathway of patients with amyotrophic lateral sclerosis (ALS) based on real-life textual data from a regional coordination network, the Ile-de-France ALS network. This coordination network provides care for 92% of patients diagnosed with ALS living in Ile-de-France. We developed a modular ontology (OntoPaRON) for the automatic processing of these unstructured textual data. OntoPaRON has different modules: the core, medical, socio-environmental, coordination, and consolidation modules. Our approach was unique in its creation of fully defined concepts at different levels of the modular ontology to address specific topics relating to healthcare trajectories. We also created a semantic annotation tool specific to the French language and the specificities of our corpus, the Ontology-Based Semantic Annotation Module (OnBaSAM), using the OntoPaRON ontology as a reference. We used these tools to annotate the records of 928 patients automatically. The semantic (qualitative) annotations of the concepts were transformed into quantitative data. By using these pipelines we were able to transform unstructured textual data into structured quantitative data. Based on data processing, semantic annotations, sociodemographic data for the patient and clinical variables, we found that the need and demand for human and technical assistance depend on the initial form of the disease, the motor state, and the patient age. The presence of exhaustion in care management, is related to the patient's motor and cognitive state.

Authors

  • Sonia Cardoso
    Laboratoire d'Informatique Médicale et d'IngéInierie des Connaissances en e-Santé UMR-1142, Sorbonne Université, INSERM, Université Paris 13, Paris, France.
  • Pierre Meneton
    Laboratoire d'Informatique Médicale et d'IngéInierie des Connaissances en e-Santé UMR-1142, Sorbonne Université, INSERM, Université Paris 13, Paris, France.
  • Xavier Aimé
    INSERM, U1142, LIMICS, F-75006, Paris, France; Sorbonne Universités, UPMC Univ Paris 06, UMR_S 1142, LIMICS, F-75006, Paris, France; Université Paris 13, Sorbonne Paris Cité, LIMICS.
  • Vincent Meininger
    Ramsay General de Santé, Hôpital Peupliers, Paris, France.
  • David Grabli
    Département des maladies du Système Nerveux, Assistance Publique-Hôpitaux de Paris Pitié Salpêtrière, Paris, France.
  • Gilles Guézennec
    Sorbonne Université, Université Paris 13, Sorbonne Paris Cité, INSERM, LIMICS, UMR_S 1142, Paris, France.
  • Jean Charlet
    INSERM, U1142, LIMICS, F-75006, Paris, France; Sorbonne Universités, UPMC Univ Paris 06, UMR_S 1142, LIMICS, F-75006, Paris, France; Université Paris 13, Sorbonne Paris Cité, LIMICS.