51 research outputs found
Unsupervised Early Exit in DNNs with Multiple Exits
Deep Neural Networks (DNNs) are generally designed as sequentially cascaded
differentiable blocks/layers with a prediction module connected only to its
last layer. DNNs can be attached with prediction modules at multiple points
along the backbone where inference can stop at an intermediary stage without
passing through all the modules. The last exit point may offer a better
prediction error but also involves more computational resources and latency. An
exit point that is `optimal' in terms of both prediction error and cost is
desirable. The optimal exit point may depend on the latent distribution of the
tasks and may change from one task type to another. During neural inference,
the ground truth of instances may not be available and error rates at each exit
point cannot be estimated. Hence one is faced with the problem of selecting the
optimal exit in an unsupervised setting. Prior works tackled this problem in an
offline supervised setting assuming that enough labeled data is available to
estimate the error rate at each exit point and tune the parameters for better
accuracy. However, pre-trained DNNs are often deployed in new domains for which
a large amount of ground truth may not be available. We model the problem of
exit selection as an unsupervised online learning problem and use bandit theory
to identify the optimal exit point. Specifically, we focus on Elastic BERT, a
pre-trained multi-exit DNN to demonstrate that it `nearly' satisfies the Strong
Dominance (SD) property making it possible to learn the optimal exit in an
online setup without knowing the ground truth labels. We develop upper
confidence bound (UCB) based algorithm named UEE-UCB that provably achieves
sub-linear regret under the SD property. Thus our method provides a means to
adaptively learn domain-specific optimal exit points in multi-exit DNNs. We
empirically validate our algorithm on IMDb and Yelp datasets.Comment: To be presented at International conference on AI-ML system
Neutrinos from Stellar Collapse: Effects of flavour mixing
We study the effect of non-vanishing masses and mixings among neutrino
flavours on the detection of neutrinos from stellar collapse by a water
Cerenkov detector. We consider a realistic framework in which there are three
neutrino flavours whose mass squared differences and mixings are constrained by
the present understanding of solar and atmospheric neutrinos. We also include
the effects of high dense matter within the supernova core. We find that the
number of events due to the dominant process involving electron-antineutrinos
may change dramatically for some allowed mixing parameters. Furthermore,
contributions from charged-current scattering off oxygen atoms in the detector
can be considerably enhanced due to flavour mixing; such events have a distinct
experimental signature since they are backward-peaked. Hence, mixing has a
non-trivial effect on the signature of neutrinos (and antineutrinos) from
stellar collapse.Comment: 22 pages Latex file, with 6 postscript figures, minor changes made in
tex
Detection of left ventricular wall motion abnormalities from volume rendering of 4DCT cardiac angiograms using deep learning
BackgroundThe presence of left ventricular (LV) wall motion abnormalities (WMA) is an independent indicator of adverse cardiovascular events in patients with cardiovascular diseases. We develop and evaluate the ability to detect cardiac wall motion abnormalities (WMA) from dynamic volume renderings (VR) of clinical 4D computed tomography (CT) angiograms using a deep learning (DL) framework.MethodsThree hundred forty-three ECG-gated cardiac 4DCT studies (age: 61 ± 15, 60.1% male) were retrospectively evaluated. Volume-rendering videos of the LV blood pool were generated from 6 different perspectives (i.e., six views corresponding to every 60-degree rotation around the LV long axis); resulting in 2058 unique videos. Ground-truth WMA classification for each video was performed by evaluating the extent of impaired regional shortening visible (measured in the original 4DCT data). DL classification of each video for the presence of WMA was performed by first extracting image features frame-by-frame using a pre-trained Inception network and then evaluating the set of features using a long short-term memory network. Data were split into 60% for 5-fold cross-validation and 40% for testing.ResultsVolume rendering videos represent ~800-fold data compression of the 4DCT volumes. Per-video DL classification performance was high for both cross-validation (accuracy = 93.1%, sensitivity = 90.0% and specificity = 95.1%, κ: 0.86) and testing (90.9, 90.2, and 91.4% respectively, κ: 0.81). Per-study performance was also high (cross-validation: 93.7, 93.5, 93.8%, κ: 0.87; testing: 93.5, 91.9, 94.7%, κ: 0.87). By re-binning per-video results into the 6 regional views of the LV we showed DL was accurate (mean accuracy = 93.1 and 90.9% for cross-validation and testing cohort, respectively) for every region. DL classification strongly agreed (accuracy = 91.0%, κ: 0.81) with expert visual assessment.ConclusionsDynamic volume rendering of the LV blood pool combined with DL classification can accurately detect regional WMA from cardiac CT
Clinical Presentation and Outcomes of Kawasaki Disease in Children from Latin America: A Multicenter Observational Study from the REKAMLATINA Network
Objetivos: Describir la presentación clínica, el manejo y los resultados de la enfermedad de Kawasaki (EK) en Latinoamérica y evaluar los indicadores pronósticos tempranos de aneurisma de la arteria coronaria (AAC). Diseño del estudio: Se realizó un estudio observacional basado en el registro de la EK en 64 centros pediátricos participantes de 19 países latinoamericanos de forma retrospectiva entre el 1 de enero de 2009 y el 31 de diciembre de 2013, y de forma prospectiva desde el 1 de junio de 2014 hasta el 31 de mayo de 2017. Se recopilaron datos demográficos, clínicos y de laboratorio iniciales. Se utilizó una regresión logística que incorporaba factores clínicos y la puntuación z máxima de la arteria coronaria en la presentación inicial (entre 10 días antes y 5 días después de la inmunoglobulina intravenosa [IGIV]) para desarrollar un modelo pronóstico de AAC durante el seguimiento (>5 días después de la IGIV). Resultados: De 1853 pacientes con EK, el ingreso tardío (>10 días tras el inicio de la fiebre) se produjo en el 16%, el 25% tuvo EK incompleta y el 11% fue resistente a la IGIV. Entre los 671 sujetos con puntuación z de la arteria coronaria notificada durante el seguimiento (mediana: 79 días; IQR: 36, 186), el 21% presentaba AAC, incluido un 4% con aneurismas gigantes. Un modelo pronóstico simple que utilizaba sólo una puntuación z de la arteria coronaria máxima ≥2,5 en la presentación inicial fue óptimo para predecir la AAC durante el seguimiento (área bajo la curva: 0,84; IC del 95%: 0,80, 0,88). Conclusiones: De nuestra población latinoamericana, la puntuación z de la arteria coronaria ≥2,5 en la presentación inicial fue el factor pronóstico más importante que precedió a la AAC durante el seguimiento. Estos resultados resaltan la importancia de la ecocardiografía temprana durante la presentación inicial de la EK. © 2023 Los autoresObjectives: To describe the clinical presentation, management, and outcomes of Kawasaki disease (KD) in Latin America and to evaluate early prognostic indicators of coronary artery aneurysm (CAA). Study design: An observational KD registry-based study was conducted in 64 participating pediatric centers across 19 Latin American countries retrospectively between January 1, 2009, and December 31, 2013, and prospectively from June 1, 2014, to May 31, 2017. Demographic and initial clinical and laboratory data were collected. Logistic regression incorporating clinical factors and maximum coronary artery z-score at initial presentation (between 10 days before and 5 days after intravenous immunoglobulin [IVIG]) was used to develop a prognostic model for CAA during follow-up (>5 days after IVIG). Results: Of 1853 patients with KD, delayed admission (>10 days after fever onset) occurred in 16%, 25% had incomplete KD, and 11% were resistant to IVIG. Among 671 subjects with reported coronary artery z-score during follow-up (median: 79 days; IQR: 36, 186), 21% had CAA, including 4% with giant aneurysms. A simple prognostic model utilizing only a maximum coronary artery z-score ≥2.5 at initial presentation was optimal to predict CAA during follow-up (area under the curve: 0.84; 95% CI: 0.80, 0.88). Conclusion: From our Latin American population, coronary artery z-score ≥2.5 at initial presentation was the most important prognostic factor preceding CAA during follow-up. These results highlight the importance of early echocardiography during the initial presentation of KD. © 2023 The Author(s
Global variation in diabetes diagnosis and prevalence based on fasting glucose and hemoglobin A1c
Fasting plasma glucose (FPG) and hemoglobin A1c (HbA1c) are both used to diagnose diabetes, but these measurements can identify different people as having diabetes. We used data from 117 population-based studies and quantified, in different world regions, the prevalence of diagnosed diabetes, and whether those who were previously undiagnosed and detected as having diabetes in survey screening, had elevated FPG, HbA1c or both. We developed prediction equations for estimating the probability that a person without previously diagnosed diabetes, and at a specific level of FPG, had elevated HbA1c, and vice versa. The age-standardized proportion of diabetes that was previously undiagnosed and detected in survey screening ranged from 30% in the high-income western region to 66% in south Asia. Among those with screen-detected diabetes with either test, the age-standardized proportion who had elevated levels of both FPG and HbA1c was 29-39% across regions; the remainder had discordant elevation of FPG or HbA1c. In most low- and middle-income regions, isolated elevated HbA1c was more common than isolated elevated FPG. In these regions, the use of FPG alone may delay diabetes diagnosis and underestimate diabetes prevalence. Our prediction equations help allocate finite resources for measuring HbA1c to reduce the global shortfall in diabetes diagnosis and surveillance
Worldwide trends in underweight and obesity from 1990 to 2022: a pooled analysis of 3663 population-representative studies with 222 million children, adolescents, and adults
Background Underweight and obesity are associated with adverse health outcomes throughout the life course. We
estimated the individual and combined prevalence of underweight or thinness and obesity, and their changes, from
1990 to 2022 for adults and school-aged children and adolescents in 200 countries and territories.
Methods We used data from 3663 population-based studies with 222 million participants that measured height and
weight in representative samples of the general population. We used a Bayesian hierarchical model to estimate
trends in the prevalence of different BMI categories, separately for adults (age ≥20 years) and school-aged children
and adolescents (age 5–19 years), from 1990 to 2022 for 200 countries and territories. For adults, we report the
individual and combined prevalence of underweight (BMI <18·5 kg/m2) and obesity (BMI ≥30 kg/m2). For schoolaged children and adolescents, we report thinness (BMI <2 SD below the median of the WHO growth reference)
and obesity (BMI >2 SD above the median).
Findings From 1990 to 2022, the combined prevalence of underweight and obesity in adults decreased in
11 countries (6%) for women and 17 (9%) for men with a posterior probability of at least 0·80 that the observed
changes were true decreases. The combined prevalence increased in 162 countries (81%) for women and
140 countries (70%) for men with a posterior probability of at least 0·80. In 2022, the combined prevalence of
underweight and obesity was highest in island nations in the Caribbean and Polynesia and Micronesia, and
countries in the Middle East and north Africa. Obesity prevalence was higher than underweight with posterior
probability of at least 0·80 in 177 countries (89%) for women and 145 (73%) for men in 2022, whereas the converse
was true in 16 countries (8%) for women, and 39 (20%) for men. From 1990 to 2022, the combined prevalence of
thinness and obesity decreased among girls in five countries (3%) and among boys in 15 countries (8%) with a
posterior probability of at least 0·80, and increased among girls in 140 countries (70%) and boys in 137 countries (69%)
with a posterior probability of at least 0·80. The countries with highest combined prevalence of thinness and
obesity in school-aged children and adolescents in 2022 were in Polynesia and Micronesia and the Caribbean for
both sexes, and Chile and Qatar for boys. Combined prevalence was also high in some countries in south Asia, such
as India and Pakistan, where thinness remained prevalent despite having declined. In 2022, obesity in school-aged
children and adolescents was more prevalent than thinness with a posterior probability of at least 0·80 among girls
in 133 countries (67%) and boys in 125 countries (63%), whereas the converse was true in 35 countries (18%) and
42 countries (21%), respectively. In almost all countries for both adults and school-aged children and adolescents,
the increases in double burden were driven by increases in obesity, and decreases in double burden by declining
underweight or thinness.
Interpretation The combined burden of underweight and obesity has increased in most countries, driven by an
increase in obesity, while underweight and thinness remain prevalent in south Asia and parts of Africa. A healthy
nutrition transition that enhances access to nutritious foods is needed to address the remaining burden of
underweight while curbing and reversing the increase in obesit
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
Global variations in diabetes mellitus based on fasting glucose and haemogloblin A1c
Fasting plasma glucose (FPG) and haemoglobin A1c (HbA1c) are both used to diagnose
diabetes, but may identify different people as having diabetes. We used data from 117
population-based studies and quantified, in different world regions, the prevalence of
diagnosed diabetes, and whether those who were previously undiagnosed and detected
as having diabetes in survey screening had elevated FPG, HbA1c, or both. We developed
prediction equations for estimating the probability that a person without previously
diagnosed diabetes, and at a specific level of FPG, had elevated HbA1c, and vice versa.
The age-standardised proportion of diabetes that was previously undiagnosed, and
detected in survey screening, ranged from 30% in the high-income western region to 66%
in south Asia. Among those with screen-detected diabetes with either test, the agestandardised
proportion who had elevated levels of both FPG and HbA1c was 29-39%
across regions; the remainder had discordant elevation of FPG or HbA1c. In most low- and
middle-income regions, isolated elevated HbA1c more common than isolated elevated
FPG. In these regions, the use of FPG alone may delay diabetes diagnosis and
underestimate diabetes prevalence. Our prediction equations help allocate finite
resources for measuring HbA1c to reduce the global gap in diabetes diagnosis and
surveillance.peer-reviewe
General and abdominal adiposity and hypertension in eight world regions: a pooled analysis of 837 population-based studies with 7·5 million participants
Background Adiposity can be measured using BMI (which is based on weight and height) as well as indices of abdominal adiposity. We examined the association between BMI and waist-to-height ratio (WHtR) within and across populations of different world regions and quantified how well these two metrics discriminate between people with and without hypertension. Methods We used data from studies carried out from 1990 to 2023 on BMI, WHtR and hypertension in people aged 20–64 years in representative samples of the general population in eight world regions. We graphically compared the regional distributions of BMI and WHtR, and calculated Pearson’s correlation coefficients between BMI and WHtR within each region. We used mixed-effects linear regression to estimate the extent to which WHtR varies across regions at the same BMI. We graphically examined the prevalence of hypertension and the distribution of people who have hypertension both in relation to BMI and WHtR, and we assessed how closely BMI and WHtR discriminate between participants with and without hypertension using C-statistic and net reclassification improvement (NRI). Findings The correlation between BMI and WHtR ranged from 0·76 to 0·89 within different regions. After adjusting for age and BMI, mean WHtR was highest in south Asia for both sexes, followed by Latin America and the Caribbean and the region of central Asia, Middle East and north Africa. Mean WHtR was lowest in central and eastern Europe for both sexes, in the high-income western region for women, and in Oceania for men. Conversely, to achieve an equivalent WHtR, the BMI of the population of south Asia would need to be, on average, 2·79 kg/m² (95% CI 2·31–3·28) lower for women and 1·28 kg/m² (1·02–1·54) lower for men than in the high-income western region. In every region, hypertension prevalence increased with both BMI and WHtR. Models with either of these two adiposity metrics had virtually identical C-statistics and NRIs for every region and sex, with C-statistics ranging from 0·72 to 0·81 and NRIs ranging from 0·34 to 0·57 in different region and sex combinations. When both BMI and WHtR were used, performance improved only slightly compared with using either adiposity measure alone. Interpretation BMI can distinguish young and middle-aged adults with higher versus lower amounts of abdominal adiposity with moderate-to-high accuracy, and both BMI and WHtR distinguish people with or without hypertension. However, at the same BMI level, people in south Asia, Latin America and the Caribbean, and the region of central Asia, Middle East and north Africa, have higher WHtR than in the other regions
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