Multimodal Learning for Detecting Stress under Missing Modalities

International audienceDealing with missing modalities is critical for many real-life applications. In this work, we propose a scalable framework for detecting stress induced by specific triggers in multimodal data with missing modalities. Our method has two key components: (i) aligning all modalities in the space of the strongest modality (the video) for learning a joint embedding space and (ii) a Masked Multimodal Transformer, leveraging inter- and intra-modality correlations while handling missing modalities. We validate our method through experiments on the StressID dataset, where we set the new state of the art while demonstrating its robustness across various modality scenarios and its high potential for real-life applications

ADAPT: Multimodal Learning for Detecting Physiological Changes under Missing Modalities

International audienceMultimodality has recently gained attention in the medical domain, where imaging or video modalities may be integrated with biomedical signals or health records. Yet, two challenges remain: balancing the contributions of modalities, especially in cases with a limited amount of data available, and tackling missing modalities. To address both issues, in this paper, we introduce the AnchoreD multimodAl Physiological Transformer (ADAPT), a multimodal, scalable framework with two key components: (i) aligning all modalities in the space of the strongest, richest modality (called anchor) to learn a joint embedding space, and (ii) a Masked Multimodal Transformer, leveraging both inter- and intra-modality correlations while handling missing modalities. We focus on detecting physiological changes in two real-life scenarios: stress in individuals induced by specific triggers and fighter pilots' loss of consciousness induced by g-forces. We validate the generalizability of ADAPT through extensive experiments on two datasets for these tasks, where we set the new state of the art while demonstrating its robustness across various modality scenarios and its high potential for real-life applications. Our code is available at https://github.com/jumdc/ADAPT.git

ADAPT: Multimodal Learning for Detecting Physiological Changes under Missing Modalities

International audienceMultimodality has recently gained attention in the medical domain, where imaging or video modalities may be integrated with biomedical signals or health records. Yet, two challenges remain: balancing the contributions of modalities, especially in cases with a limited amount of data available, and tackling missing modalities. To address both issues, in this paper, we introduce the AnchoreD multimodAl Physiological Transformer (ADAPT), a multimodal, scalable framework with two key components: (i) aligning all modalities in the space of the strongest, richest modality (called anchor) to learn a joint embedding space, and (ii) a Masked Multimodal Transformer, leveraging both inter- and intra-modality correlations while handling missing modalities. We focus on detecting physiological changes in two real-life scenarios: stress in individuals induced by specific triggers and fighter pilots' loss of consciousness induced by g-forces. We validate the generalizability of ADAPT through extensive experiments on two datasets for these tasks, where we set the new state of the art while demonstrating its robustness across various modality scenarios and its high potential for real-life applications. Our code is available at https://github.com/jumdc/ADAPT.git

Multimodal Learning for Detecting Stress under Missing Modalities

International audienceDealing with missing modalities is critical for many real-life applications. In this work, we propose a scalable framework for detecting stress induced by specific triggers in multimodal data with missing modalities. Our method has two key components: (i) aligning all modalities in the space of the strongest modality (the video) for learning a joint embedding space and (ii) a Masked Multimodal Transformer, leveraging inter- and intra-modality correlations while handling missing modalities. We validate our method through experiments on the StressID dataset, where we set the new state of the art while demonstrating its robustness across various modality scenarios and its high potential for real-life applications

Paediatric long term continuous positive airway pressure and noninvasive ventilation in France: A cross-sectional study

International audienceObjective: To describe the characteristics of children treated with long term continuous positive airway pressure (CPAP) or noninvasive ventilation (NIV) in France.Design: Cross-sectional national survey.Setting: Paediatric CPAP/NIV teams of 28 tertiary university hospitals in France.Patients: Children aged <20 years treated with CPAP/NIV since at least 3 months on June 1st, 2019.Intervention: An anonymous questionnaire was filled in for every patient.Results: The data of 1447 patients (60% boys), mean age 9.8 ± 5.8 years were analysed. The most frequent underlying disorders were: upper airway obstruction (46%), neuromuscular disease (28%), disorder of the central nervous system (13%), cardiorespiratory disorder (7%), and congenital bone disease (4%). Forty-five percent of the patients were treated with CPAP and 55% with NIV. Treatment was initiated electively for 92% of children, while 8% started during an acute illness. A poly(somno)graphy (P(S)G) was performed prior to treatment initiation in 26%, 36% had a P(S)G with transcutaneous carbon dioxide monitoring (PtcCO2), while 23% had only a pulse oximetry (SpO2) with PtcCO2 recording. The decision of CPAP/NIV initiation during an elective setting was based on the apnea-hypopnea index (AHI) in 41% of patients, SpO2 and PtcCO2 in 25% of patients, and AHI with PtcCO2 in 25% of patients. Objective adherence was excellent with a mean use of 7.6 ± 3.2 h/night. Duration of CPAP/NIV was 2.7 ± 2.9 years at the time of the survey.Conclusion: This survey shows the large number of children treated with long term CPAP/NIV in France with numerous children having disorders other than neuromuscular disease

Long term noninvasive ventilation and continuous positive airway pressure in children with neuromuscular diseases in France

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Factors Associated with Asthma Severity in Children: Data from the French COBRAPed Cohort

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Rare predicted loss-of-function variants of type I IFN immunity genes are associated with life-threatening COVID-19

BackgroundWe previously reported that impaired type I IFN activity, due to inborn errors of TLR3- and TLR7-dependent type I interferon (IFN) immunity or to autoantibodies against type I IFN, account for 15-20% of cases of life-threatening COVID-19 in unvaccinated patients. Therefore, the determinants of life-threatening COVID-19 remain to be identified in similar to 80% of cases.MethodsWe report here a genome-wide rare variant burden association analysis in 3269 unvaccinated patients with life-threatening COVID-19, and 1373 unvaccinated SARS-CoV-2-infected individuals without pneumonia. Among the 928 patients tested for autoantibodies against type I IFN, a quarter (234) were positive and were excluded.ResultsNo gene reached genome-wide significance. Under a recessive model, the most significant gene with at-risk variants was TLR7, with an OR of 27.68 (95%CI 1.5-528.7, P=1.1x10(-4)) for biochemically loss-of-function (bLOF) variants. We replicated the enrichment in rare predicted LOF (pLOF) variants at 13 influenza susceptibility loci involved in TLR3-dependent type I IFN immunity (OR=3.70[95%CI 1.3-8.2], P=2.1x10(-4)). This enrichment was further strengthened by (1) adding the recently reported TYK2 and TLR7 COVID-19 loci, particularly under a recessive model (OR=19.65[95%CI 2.1-2635.4], P=3.4x10(-3)), and (2) considering as pLOF branchpoint variants with potentially strong impacts on splicing among the 15 loci (OR=4.40[9%CI 2.3-8.4], P=7.7x10(-8)). Finally, the patients with pLOF/bLOF variants at these 15 loci were significantly younger (mean age [SD]=43.3 [20.3] years) than the other patients (56.0 [17.3] years; P=1.68x10(-5)).ConclusionsRare variants of TLR3- and TLR7-dependent type I IFN immunity genes can underlie life-threatening COVID-19, particularly with recessive inheritance, in patients under 60 years old

Correction: Rare predicted loss-of-function variants of type I IFN immunity genes are associated with life-threatening COVID-19

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