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Rhumatologue

Docteur CHARLOTTE JACQUEMIN

📍 Bry-sur-Marne (94)HospitalierRPPS 10100974194
📚 HAL (6)

Diplômes

🎓 DES & spécialité ordinale

  • DES Rhumatologie
  • Rhumatologie (SM)

🎓 Diplômes

  • DE Docteur en médecine

Source : Annuaire Santé ANS (FHIR Practitioner.qualification) · Mises à jour quotidiennes.

Bibliographie

Source : HAL — archive ouverte CCSD/CNRS (couvre articles, chapitres EMC, communications congrès, thèses).

Lieu de consultation

  • HOPITAL SAINT CAMILLE

    2 Rue DES PERES CAMILLIENS, 94366 Bry-sur-Marne

    0149831010Hospitalier

Tarifs & secteur de conventionnement

Secteur de conventionnement non disponible (médecin hospitalier ou non présent dans l'Annuaire santé CNAM des libéraux conventionnés).

Prendre rendez-vous & contact

Lien Doctolib = recherche Google site:doctolib.fr (le 1er résultat est presque toujours le profil correct s'il existe).

Top publications · les plus citées

  • 2
    Detection of Flares by Decrease in Physical Activity, Collected Using Wearable Activity Trackers in Rheumatoid Arthritis or Axial Spondyloarthritis: An Application of Machine Learning Analyses in Rheumatology

    Arthritis care & research · 2019

    📚 102 citations🎯 RCR 6.57Top 5% NIH🩺 Clinique
    Lire l'abstract Crossref ↓

    ObjectiveFlares in rheumatoid arthritis (RA) and axial spondyloarthritis (SpA) may influence physical activity. The aim of this study was to assess longitudinally the association between patient‐reported flares and activity‐tracker–provided steps per minute, using machine learning.MethodsThis prospective observational study (ActConnect) included patients with definite RA or axial SpA. For a 3‐month time period, physical activity was assessed continuously by number of steps/minute, using a consumer grade activity tracker, and flares were self‐assessed weekly. Machine‐learning techniques were applied to the data set. After intrapatient normalization of the physical activity data, multiclass Bayesian methods were used to calculate sensitivities, specificities, and predictive values of the machine‐generated models of physical activity in order to predict patient‐reported flares.ResultsOverall, 155 patients (1,339 weekly flare assessments and 224,952 hours of physical activity assessments) were analyzed. The mean ± SD age for patients with RA (n = 82) was 48.9 ± 12.6 years and was 41.2 ± 10.3 years for those with axial SpA (n = 73). The mean ± SD disease duration was 10.5 ± 8.8 years for patients with RA and 10.8 ± 9.1 years for those with axial SpA. Fourteen patients with RA (17.1%) and 41 patients with axial SpA (56.2%) were male. Disease was well‐controlled (Disease Activity Score in 28 joints mean ± SD 2.2 ± 1.2; Bath Ankylosing Spondylitis Disease Activity Index score mean ± SD 3.1 ± 2.0), but flares were frequent (22.7% of all weekly assessments). The model generated by machine learning performed well against patient‐reported flares (mean sensitivity 96% [95% confidence interval (95% CI) 94–97%], mean specificity 97% [95% CI 96–97%], mean positive predictive value 91% [95% CI 88–96%], and negative predictive value 99% [95% CI 98–100%]). Sensitivity analyses were confirmatory.ConclusionAlthough these pilot findings will have to be confirmed, the correct detection of flares by machine‐learning processing of activity tracker data provides a framework for future studies of remote‐control monitoring of disease activity, with great precision and minimal patient burden.

Publications scientifiques (15) — classées par pathologie

Source PubMed · Recherche par auteur (homonymes possibles, vérifier l'affiliation).

Transversal6

Lupus3

Activité physique / Rééducation2

Case report / série1

Corticothérapie1

Épidémiologie & registres1

IA en rhumatologie1

Qualité de vie / PROMs1

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