Benchmarking Deep Learning Architectures for Predicting Readmission to the ICU and Describing Patients-at-Risk

Objective: To compare different deep learning architectures for predicting the risk of readmission within 30 days of discharge from the intensive care unit (ICU). The interpretability of attention-based models is leveraged to describe patients-at-risk. Methods: Several deep learning architectures making use of attention mechanisms, recurrent layers, neural ordinary differential equations (ODEs), and medical concept embeddings with time-aware attention were trained using publicly available electronic medical record data (MIMIC-III) associated with 45,298 ICU stays for 33,150 patients. Bayesian inference was used to compute the posterior over weights of an attention-based model. Odds ratios associated with an increased risk of readmission were computed for static variables. Diagnoses, procedures, medications, and vital signs were ranked according to the associated risk of readmission. Results: A recurrent neural network, with time dynamics of code embeddings computed by neural ODEs, achieved the highest average precision of 0.331 (AUROC: 0.739, F1-Score: 0.372). Predictive accuracy was comparable across neural network architectures. Groups of patients at risk included those suffering from infectious complications, with chronic or progressive conditions, and for whom standard medical care was not suitable. Conclusions: Attention-based networks may be preferable to recurrent networks if an interpretable model is required, at only marginal cost in predictive accuracy

Generation and analysis of networks with a prescribed degree sequence and subgraph family: higher-order structure matters

Designing algorithms that generate networks with a given degree sequence while varying both subgraph composition and distribution of subgraphs around nodes is an important but challenging research problem. Current algorithms lack control of key network parameters, the ability to specify to what subgraphs a node belongs to, come at a considerable complexity cost or, critically and sample from a limited ensemble of networks. To enable controlled investigations of the impact and role of subgraphs, especially for epidemics, neuronal activity or complex contagion, it is essential that the generation process be versatile and the generated networks as diverse as possible. In this article, we present two new network generation algorithms that use subgraphs as building blocks to construct networks preserving a given degree sequence. Additionally, these algorithms provide control over clustering both at node and global level. In both cases, we show that, despite being constrained by a degree sequence and global clustering, generated networks have markedly different topologies as evidenced by both subgraph prevalence and distribution around nodes, and large-scale network structure metrics such as path length and betweenness measures. Simulations of standard epidemic and complex contagion models on those networks reveal that degree distribution and global clustering do not always accurately predict the outcome of dynamical processes taking place on them. We conclude by discussing the benefits and limitations of both methods

ScanComplete: Large-Scale Scene Completion and Semantic Segmentation for 3D Scans

We introduce ScanComplete, a novel data-driven approach for taking an incomplete 3D scan of a scene as input and predicting a complete 3D model along with per-voxel semantic labels. The key contribution of our method is its ability to handle large scenes with varying spatial extent, managing the cubic growth in data size as scene size increases. To this end, we devise a fully-convolutional generative 3D CNN model whose filter kernels are invariant to the overall scene size. The model can be trained on scene subvolumes but deployed on arbitrarily large scenes at test time. In addition, we propose a coarse-to-fine inference strategy in order to produce high-resolution output while also leveraging large input context sizes. In an extensive series of experiments, we carefully evaluate different model design choices, considering both deterministic and probabilistic models for completion and semantic inference. Our results show that we outperform other methods not only in the size of the environments handled and processing efficiency, but also with regard to completion quality and semantic segmentation performance by a significant margin.Comment: Video: https://youtu.be/5s5s8iH0NF

Thickness dependence of electron-electron interactions in topological p-n junctions

Electron-electron interactions in topological p-n junctions consisting of vertically stacked topological insulators are investigated. n-type Bi2Te3 and p-type Sb2Te3 of varying relative thicknesses are deposited using molecular beam epitaxy and their electronic properties measured using low-temperature transport. The screening factor is observed to decrease with increasing sample thickness, a finding which is corroborated by semi-classical Boltzmann theory. The number of two-dimensional states determined from electron-electron interactions is larger compared to the number obtained from weak-antilocalization, in line with earlier experiments using single layers.Comment: 38 pages, 5 figures, 1 tabl

Scaling strength distributions in quasi-brittle materials from micro- to macro-scales: A computational approach to modeling Nature-inspired structural ceramics

International audienceThis paper presents an approach to predict the strength distribution of quasi-brittle materials across multiple length-scales, with emphasis on Nature-inspired ceramic structures. It permits the computation of the failure probability of any structure under any mechanical load, solely based on considerations of the microstructure and its failure properties by naturally incorporating the statistical and size-dependent aspects of failure. We overcome the intrinsic limitations of single periodic unit-based approaches by computing the successive failures of the material components and associated stress redistributions on arbitrary numbers of periodic units. For large size samples, the microscopic cells are replaced by a homogenized continuum with equivalent stochastic and damaged constitutive behavior. After establishing the predictive capabilities of the method, and illustrating its potential relevance to several engineering problems, we employ it in the study of the shape and scaling of strength distributions across differing length-scales for a particular quasi-brittle system. We find that the strength distributions display a Weibull form for samples of size approaching the periodic unit; however, these distributions become closer to normal with further increase in sample size before finally reverting to a Weibull form for macroscopic sized samples. In terms of scaling, we find that the weakest link scaling applies only to microscopic, and not macroscopic scale, samples. These findings are discussed in relation to failure patterns computed at different size-scales

Targeted interventions for patellofemoral pain syndrome (TIPPS): classification of clinical subgroups

Introduction Patellofemoral pain (PFP) can cause significant pain leading to limitations in societal participation and physical activity. An international expert group has highlighted the need for a classification system to allow targeted intervention for patients with PFP; we have developed a work programme systematically investigating this. We have proposed six potential subgroups: hip abductor weakness, quadriceps weakness, patellar hypermobility, patellar hypomobility, pronated foot posture and lower limb biarticular muscle tightness. We could not uncover any evidence of the relative frequency with which patients with PFP fell into these subgroups or whether these subgroups were mutually exclusive. The aim of this study is to provide information on the clinical utility of our classification system. Methods and analysis 150 participants will be recruited over 18 months in four National Health Services (NHS) physiotherapy departments in England. Inclusion criteria: adults 18–40 years with PFP for longer than 3 months, PFP in at least two predesignated functional activities and PFP elicited by clinical examination. Exclusion criteria: prior or forthcoming lower limb surgery; comorbid illness or health condition; and lower limb training or pregnancy. We will record medical history, demographic details, pain, quality of life, psychomotor movement awareness and knee temperature. We will assess hip abductor and quadriceps weakness, patellar hypermobility and hypomobility, foot posture and lower limb biarticular muscle tightness. The primary analytic approach will be descriptive. We shall present numbers and percentages of participants who meet the criteria for membership of (1) each of the subgroups, (2) none of the subgroups and (3) multiple subgroups. Exact (binomial) 95% CIs for these percentages will also be presented. Ethics and dissemination This study has been approved by National Research Ethics Service (NRES) Committee North West—Greater Manchester North (11/NW/0814) and University of Central Lancashire (UCLan) Built, Sport, Health (BuSH) Ethics Committee (BuSH 025). An abstract has been accepted for the third International Patellofemoral Pain Research Retreat, Vancouver, September 2013

Ordering our world: the quest for traces of temporal organization in autobiographical memory

An experiment examined the idea, derived from the Self Memory System model (Conway & Pleydell-Pearce, 2000), that autobiographical events are sometimes tagged in memory with labels reflecting the life era in which an event occurred. The presence of such labels should affect the ease of judgments of the order in which life events occurred. Accordingly, 39 participants judged the order of two autobiographical events. Latency data consistently showed that between-era judgments were faster than within-era judgments, when the eras were defined in terms of either: (a) college versus high school, (b) academic quarter within year, or (c) academic year within school. The accuracy data similarly supported the presence of a between-era judgment effect for the college versus high school dichotomy

Modeling superimposed preeclampsia using Ang II (Angiotensin II) infusion in pregnant stroke-prone spontaneously hypertensive rats

Hypertensive disorders of pregnancy are the second leading cause of maternal deaths worldwide. Superimposed preeclampsia is an increasingly common problem and often associated with impaired placental perfusion. Understanding the underlying mechanisms and developing treatment options are crucial. The pregnant stroke-prone spontaneously hypertensive rat has impaired uteroplacental blood flow and abnormal uterine artery remodeling. We used Ang II (angiotensin II) infusion in pregnant stroke-prone spontaneously hypertensive rats to mimic the increased cardiovascular stress associated with superimposed preeclampsia and examine the impact on the maternal cardiovascular system and fetal development. Continuous infusion of Ang II at 500 or 1000 ng/kg per minute was administered from gestational day 10.5 until term. Radiotelemetry and echocardiography were used to monitor hemodynamic and cardiovascular changes, and urine was collected prepregnancy and throughout gestation. Uterine artery myography assessed uteroplacental vascular function and structure. Fetal measurements were made at gestational day 18.5, and placentas were collected for histological and gene expression analyses. The 1000 ng/kg per minute Ang II treatment significantly increased blood pressure (P<0.01), reduced cardiac output (P<0.05), and reduced diameter and increased stiffness of the uterine arteries (P<0.01) during pregnancy. The albumin:creatinine ratio was increased in both Ang II treatment groups (P<0.05; P<0.0001). The 1000 ng/kg per minute–treated fetuses were significantly smaller than vehicle treatment (P<0.001). Placental expression of Ang II receptors was increased in the junctional zone in 1000 ng/kg per minute Ang II–treated groups (P<0.05), with this zone showing depletion of glycogen content and structural abnormalities. Ang II infusion in pregnant stroke-prone spontaneously hypertensive rats mirrors hemodynamic, cardiac, and urinary profiles observed in preeclamptic women, with evidence of impaired fetal growth

High CO2 decreases the long-term resilience of the free-living coralline algae Phymatolithon lusitanicum

Maerl/rhodolith beds are protected habitats that may be affected by ocean acidification (OA), but it is still unclear how the availability of CO2 will affect the metabolism of these organisms. Some of the inconsistencies found among OA experimental studies may be related to experimental exposure time and synergetic effects with other stressors. Here, we investigated the long-term (up to 20months) effects of OA on the production and calcification of the most common maerl species of southern Portugal, Phymatolithon lusitanicum. Both the photosynthetic and calcification rates increased with CO2 after the first 11months of the experiment, whereas respiration slightly decreased with CO2. After 20months, the pattern was reversed. Acidified algae showed lower photosynthetic and calcification rates, as well as lower accumulated growth than control algae, suggesting that a metabolic threshold was exceeded. Our results indicate that long-term exposure to high CO2 will decrease the resilience of Phymatolithon lusitanicum. Our results also show that shallow communities of these rhodoliths may be particularly at risk, while deeper rhodolith beds may become ocean acidification refuges for this biological community.Fundacao para a Ciencia e a Tecnologia [PTDC/MAR/115789/2009, SFRH/BD/76762/2011