Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe

The small pelagic fishery of the Pemba Channel, Tanzania: what we know and what we need to know for management under climate change

Small pelagic fish, including anchovies, sardines and sardinellas, mackerels, capelin, hilsa, sprats and herrings, are distributed widely, from the tropics to the far north Atlantic Ocean and to the southern oceans off Chile and South Africa. They are most abundant in the highly productive major eastern boundary upwelling systems and are characterised by significant natural variations in biomass. Overall, small pelagic fisheries represent about one third of global fish landings although a large proportion of the catch is processed into animal feeds. Nonetheless, in some developing countries in addition to their economic value, small pelagic fisheries also make an important contribution to human diets and the food security of many low-income households. Such is the case for many communities in the Zanzibar Archipelago and on mainland Tanzania in the Western Indian Ocean. Of great concern in this region, as elsewhere, is the potential impact of climate change on marine and coastal ecosystems in general, and on small pelagic fisheries in particular. This paper describes data and information available on Tanzania's small pelagic fisheries, including catch and effort, management protocols and socio-economic significance

Développement d'algorithmes prédictifs du risque cardiovasculaire chez les patients investigués pour suspicion du syndrome d’apnées du sommeil - PREDIVASC

Sleep apnea syndrome (SAS) is a very common sleep disorder, leading to various consequences, including cardiovascular complications. Cardiovascular diseases are the first cause of death worldwide so prevention is essential. Clinicians need to assess cardiovascular risk to adapt treatment for SAS. However, there is no cardiovascular risk estimator adapted to this particularly high-risk population. The sleep exam gathers all the information allowing an estimation of this risk. We proposed a new tool based on 5,506 patients from the Pays de la Loire sleep cohort and a deep learning approach, combining signals recorded during the sleep exam and clinical variables. An area under the ROC curve of 0.82 was achieved, surpassing the performance of existing estimators and illustrating the benefit of using sleep data to estimate cardiovascular risk.Le syndrome d'apnées du sommeil (SAS) est un trouble du sommeil très fréquent, entraînant de nombreuses conséquences, dont des complications cardiovasculaires. Les maladies cardiovasculaires étant la première cause de décès dans le monde, leur prévention est primordiale. Les cliniciens ont besoin d'évaluer le risque cardiovasculaire pour adapter le traitement contre le SAS. Cependant, il n'existe pas d'estimateur du risque cardiovasculaire adapté à cette population particulièrement à haut risque. L'examen du sommeil réunit toutes les informations permettant une estimation de ce risque. Nous avons proposé un nouvel outil basé sur 5 506 patients de la cohorte sommeil des Pays de la Loire et une approche d'apprentissage profond, combinant des signaux enregistrés pendant l'examen du sommeil et des variables cliniques. Une aire sous la courbe de ROC de 0,82 a été atteinte, surpassant les performances des estimateurs existants et illustrant l'intérêt d'utiliser les données issues du sommeil pour estimer le risque cardiovasculaire

Development of predictive algorithms of cardiovascular risk for patients investigated for suspected sleep apnea syndrome

Le syndrome d'apnées du sommeil (SAS) est un trouble du sommeil très fréquent, entraînant de nombreuses conséquences, dont des complications cardiovasculaires. Les maladies cardiovasculaires étant la première cause de décès dans le monde, leur prévention est primordiale. Les cliniciens ont besoin d'évaluer le risque cardiovasculaire pour adapter le traitement contre le SAS. Cependant, il n'existe pas d'estimateur du risque cardiovasculaire adapté à cette population particulièrement à haut risque. L'examen du sommeil réunit toutes les informations permettant une estimation de ce risque. Nous avons proposé un nouvel outil basé sur 5 506 patients de la cohorte sommeil des Pays de la Loire et une approche d'apprentissage profond, combinant des signaux enregistrés pendant l'examen du sommeil et des variables cliniques. Une aire sous la courbe de ROC de 0,82 a été atteinte, surpassant les performances des estimateurs existants et illustrant l'intérêt d'utiliser les données issues du sommeil pour estimer le risque cardiovasculaire.Sleep apnea syndrome (SAS) is a very common sleep disorder, leading to various consequences, including cardiovascular complications. Cardiovascular diseases are the first cause of death worldwide so prevention is essential. Clinicians need to assess cardiovascular risk to adapt treatment for SAS. However, there is no cardiovascular risk estimator adapted to this particularly high-risk population. The sleep exam gathers all the information allowing an estimation of this risk. We proposed a new tool based on 5,506 patients from the Pays de la Loire sleep cohort and a deep learning approach, combining signals recorded during the sleep exam and clinical variables. An area under the ROC curve of 0.82 was achieved, surpassing the performance of existing estimators and illustrating the benefit of using sleep data to estimate cardiovascular risk

Amélioration des performances diagnostiques de la polygraphie ventilatoire par l’ajout d’une voie d’EEG

National audienc

A Deep Survival Learning Approach for Cardiovascular Risk Estimation in Patients With Sleep Apnea

Cardiovascular (CV) disorders and obstructive sleep apnea (OSA) are very prevalent diseases worldwide. Multiple studies have demonstrated that OSA is associated with increased CV risk. Clinicians need to assess the CV risk to select the proper OSA treatment. A growing number of research have employed machine learning to predict CV risk by integrating clinical and sleep features. In this paper, a multiple input deep learning model was proposed to directly use sleep signals combined with clinical features. Data from 5,506 patients from the Pays de la Loire Sleep Cohort, without a history of major adverse cardiovascular events (MACE), investigated for OSA, were used. After a median follow-up of 6.0 years, 613 patients had been diagnosed with MACE according to the French national health system. Following an architecture selection, deep survival convolutional neural networks were computed to assess the MACE risk score. A custom loss function was integrated to consider the follow-up time of each patient. Based on the weights of each model input, a method for interpreting the model was also proposed to show the contribution of signals compared to clinical features. Sleep signals were extracted from a home sleep apnea test. The best results were obtained with the autonomic manifestation signal. An area under the ROC curve of 0.823 was reached. After interpretation of the models, consideration of sleep appeared to be more important in women and in those under 60. This method may help improve OSA patient care by estimating their risk of MACE during sleep diagnosis

La variation du rythme cardiaque post apnées-hypopnées, risque cardiovasculaire et réponse à la pression positive continue

National audienc

Cardiovascular risk and mortality prediction in patients suspected of sleep apnea: a model based on an artificial intelligence system

International audienceObjective. Cardiovascular disease (CVD) is one of the leading causes of death worldwide. There are many CVD risk estimators but very few take into account sleep features. Moreover, they are rarely tested on patients investigated for obstructive sleep apnea (OSA). However, numerous studies have demonstrated that OSA index or sleep features are associated with CVD and mortality. The aim of this study is to propose a new simple CVD and mortality risk estimator for use in routine sleep testing.Approach. Data from a large multicenter cohort of CVD-free patients investigated for OSA were linked to the French Health System to identify new-onset CVD. Clinical features were collected and sleep features were extracted from sleep recordings. A machine-learning model based on trees, AdaBoost, was applied to estimate the CVD and mortality risk score.Main results. After a median [inter-quartile range] follow-up of 6.0 [3.5-8.5] years, 685 of 5234 patients had received a diagnosis of CVD or had died. Following a selection of features, from the original 30 features, 9 were selected, including five clinical and four sleep oximetry features. The final model included age, gender, hypertension, diabetes, systolic blood pressure, oxygen saturation and pulse rate variability (PRV) features. An area under the receiver operating characteristic curve (AUC) of 0.78 was reached.Significance. AdaBoost, an interpretable machine-learning model, was applied to predict 6 year CVD and mortality in patients investigated for clinical suspicion of OSA. A mixed set of simple clinical features, nocturnal hypoxemia and PRV features derived from single channel pulse oximetry were used

Association of symptom subtypes and obstructive sleep apnoea-specific hypoxic burden with cardiovascular morbidity and all-cause mortality

International audienceBackground: Data from population-based cohorts suggest that symptom subtypes and obstructive sleep apnea (OSA) specific hypoxic burden (HB) could help to better identify patients with OSA at high cardiovascular (CV) risk.Aim: We aimed to evaluate whether OSA symptoms subtypes and HB derived from sleep recordings were associated with increased CV risk in clinical setting.Methods: Clinical data from a large multicenter cohort were linked to health administrative data to identify new-onset of CV events and death in patients with OSA and no overt CV disease. Latent class analysis was used to identify subtypes based on 8 clinically relevant variables. HB was defined as the total area under the respiratory event-related desaturation curve. Cox proportional hazards models were used to evaluate the association of symptom subtypes and HB with a composite outcome of incident CV events or death from any cause.Results: Four symptom subtypes were identified (minimally symptomatic [22.0%], disturbed sleep [17.5%], excessively sleepy [49.8%], and moderately sleepy [10.6%]), similar to prior studies. After a median follow-up of 78 months, 592 (11.05%) of 5358 patients experienced our composite outcome. In a fully adjusted model, increasing age and HB, male gender, the presence of diabetes, hypertension and COPD were significant predictors of the composite outcome. AHI and symptom subtypes were not associated with CV risk after adjustments.Conclusion: In clinical setting, patients with OSA who demonstrate elevated OSA specific HB are at higher risk of CV event and all cause mortality. Symptom subtypes have no predictive value after adjustment for confounders