Search

IntroductionRheport is an online rheumatology referral system allowing automatic appointment triaging of new rheumatology patient referrals according to the respective probability of an inflammatory rheumatic disease (IRD). Previous research reported that Rheport was well accepted among IRD patients. Its accuracy was, however, limited, currently being based on an expert-based weighted sum score. This study aimed to evaluate whether machine learning (ML) models could improve this limited accuracy.Materials and methodsData from a national rheumatology registry (RHADAR) was used to train and test nine different ML models to correctly classify IRD patients. Diagnostic performance was compared of ML models and the current algorithm was compared using the area under the receiver operating curve (AUROC). Feature importance was investigated using shapley additive explanation (SHAP).ResultsA complete data set of 2265 patients was used to train and test ML models. 30.5% of patients were diagnosed with an IRD, 69.3% were female. The diagnostic accuracy of the current Rheport algorithm (AUROC of 0.534) could be improved with all ML models, (AUROC ranging between 0.630 and 0.737). Targeting a sensitivity of 90%, the logistic regression model could double current specificity (17% vs. 33%). Finger joint pain, inflammatory marker levels, psoriasis, symptom duration and female sex were the five most important features of the best performing logistic regression model for IRD classification.ConclusionIn summary, ML could improve the accuracy of a currently used rheumatology online referral system. Including further laboratory parameters and enabling individual feature importance adaption could increase accuracy and lead to broader usage.

Additional file 2: Fig. S2. Intraclass correlation coefficients and 95% confidence interval by group and analyte

Supplementary Material 3

Supplementary Material 1

Supplementary Material 2

Additional file 1: Table S1. Echo intensity values in two age groups.

Additional file 1: Fig. S1. Self-sampling feasibility results according to randomization arm

Additional file 4: Figure S4. All symptoms entered first in the DDSS group and their association with a correct diagnosis.

Additional file 3: Figure S3. Top diagnoses for all cases and study groups.

Additional file 2: Figure S2. Exemplary DDSS summary report for a second case.

Additional file 1: Figure S1. Consolidated Standards of Reporting Trials-eHealth checklist.

Additional file 5: Figure S5. All symptoms entered and their entry order into the DDSS for case 2.

Supplemental material, Supplemental_Figure_S1 for Oxygen Cost During Walking in Individuals With Stroke: Hemiparesis Versus Cerebellar Ataxia by Maxence Compagnat, Jean-Christophe Daviet, Charles Batcho, Nicolas Vuillerme, Jean-Yves Salle, Romain David and Stephane Mandigout in Neurorehabilitation and Neural Repair

Publication in the conference proceedings of EUSIPCO, Lisbon, Portugal, 2014

This paper presents the first stage of our project aiming at preventing workrelated musculoskeletal disorders and improving work conditions for the Château Larose- Trintaudon (Saint-Laurent Médoc, France) vineyard workers: an ergonomic assessment of vine pruning activity was conducted. Self-reported musculoskeletal pain increased throughout the working day in the lower back region. Furthermore, video analysis during real working conditions revealed that pruning activity exposed vineyard workers to a high risk of developing WMSD. The vineyard workers frequently adopted trunk-thigh postures considered ‘extreme’. The present ergonomic evaluation strongly suggests that back pain is an important health issue among the vineyard worker population.

Publication in the conference proceedings of EUSIPCO, Bucharest, Romania, 2012

This article presents a study on different informative modalities of a perceptual supplementation device aiming at reducing overpressure at the buttock area. Visual, audio and tactile modalities are analysed and compared with a non-biofeedback session. In conclusion, modalities have a positive and equal effect, but they are not equally judged in term of comfort and disturbance with some other activities

We introduce the innovative technologies, based on the concept of "sensory substitution", we are developing in the fields of biomedical engineering and human disability. Precisely, our goal is to design, develop and validate practical assistive biomedical and/or technical devices and/or rehabilitating procedures for persons with disabilities, using artificial tongue-placed tactile biofeedback systems. This paper proposes an application for pressure sores prevention in case of spinal cord injuries (persons with paraplegia, or tetraplegia).

Computing the local dynamic stability using accelerometer data from inertial sensors has recently been proposed as a gait measure which may be able to identify elderly people at fall risk. However, the assumptions supporting this potential were concluded as most studies implement a retrospective fall history observation. The aim of this study was to evaluate the potential of local dynamic stability for fall risk prediction in a cohort of subjects over the age of 60 years using a prospective fall occurrence observation. A total of 131 elderly subjects voluntarily participated in this study. The baseline measurement included gait stability assessment using inertial sensors and clinical examination by Tinetti Balance Assessment Tool. After the baseline measurement, subjects were observed for a period of one year for fall occurrence. Our results demonstrated poor multiple falls predictive ability of trunk local dynamic stability (AUC = 0.673). The predictive ability improved when the local dynamic stability was combined with clinical measures, a combination of trunk medial-lateral local dynamic stability and Tinetti total score being the best predictor (AUC = 0.755). Together, the present findings suggest that the medial-lateral local dynamic stability during gait combined with a clinical score is a potential fall risk assessment measure in the elderly population.