Missing Data Imputation and Acquisition with Deep Hierarchical Models and Hamiltonian Monte Carlo

Variational Autoencoders (VAEs) have recently been highly successful at imputing and acquiring heterogeneous missing data. However, within this specific application domain, existing VAE methods are restricted by using only one layer of latent variables and strictly Gaussian posterior approximations. To address these limitations, we present HH-VAEM, a Hierarchical VAE model for mixed-type incomplete data that uses Hamiltonian Monte Carlo with automatic hyper-parameter tuning for improved approximate inference. Our experiments show that HH-VAEM outperforms existing baselines in the tasks of missing data imputation and supervised learning with missing features. Finally, we also present a sampling-based approach for efficiently computing the information gain when missing features are to be acquired with HH-VAEM. Our experiments show that this sampling-based approach is superior to alternatives based on Gaussian approximations.Comment: Accepted at NeurIPS 202

Deep Sequential Models for Suicidal Ideation from Multiple Source Data

This paper presents a novel method for predicting suicidal ideation from electronic health records (EHR) and ecological momentary assessment (EMA) data using deep sequential models. Both EHR longitudinal data and EMA question forms are defined by asynchronous, variable length, randomly sampled data sequences. In our method, we model each of them with a recurrent neural network, and both sequences are aligned by concatenating the hidden state of each of them using temporal marks. Furthermore, we incorporate attention schemes to improve performance in long sequences and time-independent pre-trained schemes to cope with very short sequences. Using a database of 1023 patients, our experimental results show that the addition of EMA records boosts the system recall to predict the suicidal ideation diagnosis from 48.13% obtained exclusively from EHR-based state-of-the-art methods to 67.78%. Additionally, our method provides interpretability through the t-distributed stochastic neighbor embedding (t-SNE) representation of the latent space. Furthermore, the most relevant input features are identified and interpreted medically.This work was supported in part by the Spanish MINECO under Grants TEC2015-69868-C2-1-R, TEC2016-78434-C3-3-R, and TEC2017-92552-EXP, in part by Spanish MICINN under Grant RTI2018-099655-B-I00, in part by Comunidad de Madrid under Grants IND2017/TIC-7618, IND2018/TIC-9649, Y2018/TCS-4705, and B2017/BMD-3740 AGES-CM 2CM, in part by BBVA Foundation under Deep-DARWiN - FBBVA Grant for scientific research teams 2018, in part by ISCIII under Grant PI16/01852, and in part by AFSP under Grant LSRG-1-005-16

Actigraphic recording of motor activity in depressed inpatients: a novel computational approach to prediction of clinical course and hospital discharge

Depressed patients present with motor activity abnormalities, which can be easily recorded using actigraphy. The extent to which actigraphically recorded motor activity may predict inpatient clinical course and hospital discharge remains unknown. Participants were recruited from the acute psychiatric inpatient ward at Hospital Rey Juan Carlos (Madrid, Spain). They wore miniature wrist wireless inertial sensors (actigraphs) throughout the admission. We modeled activity levels against the normalized length of admission—‘Progress Towards Discharge’ (PTD)—using a Hierarchical Generalized Linear Regression Model. The estimated date of hospital discharge based on early measures of motor activity and the actual hospital discharge date were compared by a Hierarchical Gaussian Process model. Twenty-three depressed patients (14 females, age: 50.17 ± 12.72 years) were recruited. Activity levels increased during the admission (mean slope of the linear function: 0.12 ± 0.13). For n = 18 inpatients (78.26%) hospitalised for at least 7 days, the mean error of Prediction of Hospital Discharge Date at day 7 was 0.231 ± 22.98 days (95% CI 14.222–14.684). These n = 18 patients were predicted to need, on average, 7 more days in hospital (for a total length of stay of 14 days) (PTD = 0.53). Motor activity increased during the admission in this sample of depressed patients and early patterns of actigraphically recorded activity allowed for accurate prediction of hospital discharge date.This work has been partly-funded by the Spanish Ministerio de Ciencia, Innovación y Universidades (TEC2017-92552-EXP, RTI2018-099655-B-I00, FPU18/00516), the Comunidad de Madrid (Y2018/TCS-4705 PRACTICOCM, B2017/BMD-3740 AGES-CM 2CM), ISCIII (PI16/01852), BBVA Foundation (Deep-DARWiN grant) and AFSP (Grant LSRG-1-005-16). JDLM acknowledges funding support from the Universidad Autónoma de Madrid and European Union-European Commission via the Intertalentum Project & Marie Skłodowska-Curie Actions Grant (GA 713366

Relationship between olive oil consumption and ankle-brachial pressure index in a population at high cardiovascular risk

Background and aims: The aim of this study was to ascertain the association between the consumption of different categories of edible olive oils (virgin olive oils and olive oil) and olive pomace oil and ankle-brachial pressure index (ABI) in participants in the PREDIMED-Plus study, a trial of lifestyle modification for weight and cardiovascular event reduction in individuals with overweight/obesity harboring the metabolic syndrome. Methods: We performed a cross-sectional analysis of the PREDIMED-Plus trial. Consumption of any category of olive oil and olive pomace oil was assessed through a validated food-frequency questionnaire. Multivariable linear regression models were fitted to assess associations between olive oil consumption and ABI. Additionally, ABI ≤1 was considered as the outcome in logistic models with different categories of olive oil and olive pomace oil as exposure. Results: Among 4330 participants, the highest quintile of total olive oil consumption (sum of all categories of olive oil and olive pomace oil) was associated with higher mean values of ABI (beta coefficient: 0.014, 95% confidence interval [CI]: 0.002, 0.027) (p for trend = 0.010). Logistic models comparing the consumption of different categories of olive oils, olive pomace oil and ABI ≤1 values revealed an inverse association between virgin olive oils consumption and the likelihood of a low ABI (odds ratio [OR] 0.73, 95% CI [0.56, 0.97]), while consumption of olive pomace oil was positively associated with a low ABI (OR 1.22 95% CI [1.00, 1.48]). Conclusions: In a Mediterranean population at high cardiovascular risk, total olive oil consumption was associated with a higher mean ABI. These results suggest that olive oil consumption may be beneficial for peripheral artery disease prevention, but longitudinal studies are needed

Relationship between olive oil consumption and ankle-brachial pressure index in a population at high cardiovascular risk

The aim of this study was to ascertain the association between the consumption of different categories of edible olive oils (virgin olive oils and olive oil) and olive pomace oil and ankle-brachial pressure index (ABI) in participants in the PREDIMED-Plus study, a trial of lifestyle modification for weight and cardiovascular event reduction in individuals with overweight/obesity harboring the metabolic syndrome. Methods: We performed a cross-sectional analysis of the PREDIMED-Plus trial. Consumption of any category of olive oil and olive pomace oil was assessed through a validated food-frequency questionnaire. Multivariable linear regression models were fitted to assess associations between olive oil consumption and ABI. Additionally, ABI ≤1 was considered as the outcome in logistic models with different categories of olive oil and olive pomace oil as exposure. Results: Among 4330 participants, the highest quintile of total olive oil consumption (sum of all categories of olive oil and olive pomace oil) was associated with higher mean values of ABI (beta coefficient: 0.014, 95% confidence interval [CI]: 0.002, 0.027) (p for trend = 0.010). Logistic models comparing the consumption of different categories of olive oils, olive pomace oil and ABI ≤1 values revealed an inverse association between virgin olive oils consumption and the likelihood of a low ABI (odds ratio [OR] 0.73, 95% CI [0.56, 0.97]), while consumption of olive pomace oil was positively associated with a low ABI (OR 1.22 95% CI [1.00, 1.48]). Conclusions: In a Mediterranean population at high cardiovascular risk, total olive oil consumption was associated with a higher mean ABI. These results suggest that olive oil consumption may be beneficial for peripheral artery disease prevention, but longitudinal studies are needed

The evolution of the ventilatory ratio is a prognostic factor in mechanically ventilated COVID-19 ARDS patients

Background: Mortality due to COVID-19 is high, especially in patients requiring mechanical ventilation. The purpose of the study is to investigate associations between mortality and variables measured during the first three days of mechanical ventilation in patients with COVID-19 intubated at ICU admission. Methods: Multicenter, observational, cohort study includes consecutive patients with COVID-19 admitted to 44 Spanish ICUs between February 25 and July 31, 2020, who required intubation at ICU admission and mechanical ventilation for more than three days. We collected demographic and clinical data prior to admission; information about clinical evolution at days 1 and 3 of mechanical ventilation; and outcomes. Results: Of the 2,095 patients with COVID-19 admitted to the ICU, 1,118 (53.3%) were intubated at day 1 and remained under mechanical ventilation at day three. From days 1 to 3, PaO2/FiO2 increased from 115.6 [80.0-171.2] to 180.0 [135.4-227.9] mmHg and the ventilatory ratio from 1.73 [1.33-2.25] to 1.96 [1.61-2.40]. In-hospital mortality was 38.7%. A higher increase between ICU admission and day 3 in the ventilatory ratio (OR 1.04 [CI 1.01-1.07], p = 0.030) and creatinine levels (OR 1.05 [CI 1.01-1.09], p = 0.005) and a lower increase in platelet counts (OR 0.96 [CI 0.93-1.00], p = 0.037) were independently associated with a higher risk of death. No association between mortality and the PaO2/FiO2 variation was observed (OR 0.99 [CI 0.95 to 1.02], p = 0.47). Conclusions: Higher ventilatory ratio and its increase at day 3 is associated with mortality in patients with COVID-19 receiving mechanical ventilation at ICU admission. No association was found in the PaO2/FiO2 variation

Unsupervised learning of global factors in deep generative models

We present a novel deep generative model based on non i.i.d. variational autoencoders that captures global dependencies among observations in a fully unsupervised fashion. In contrast to the recent semi-supervised alternatives for global modeling in deep generative models, our approach combines a mixture model in the local or data-dependent space and a global Gaussian latent variable, which lead us to obtain three particular insights. First, the induced latent global space captures interpretable disentangled representations with no user-defined regularization in the evidence lower bound (as in beta-VAE and its generalizations). Second, we show that the model performs domain alignment to find correlations and interpolate between different databases. Finally, we study the ability of the global space to discriminate between groups of observations with non-trivial underlying structures, such as face images with shared attributes or defined sequences of digits images.This work has been partly supported by Spanish government (AEI/MCI) under grants PID2021-123182OB-I00, PID2021-125159NB-I00 and RTI2018-099655-B-100, by Comunidad de Madrid under grant IND2022/TIC-23550, by the European Union (FEDER) and the European Research Council (ERC) through the European Union's Horizon 2020 research and innovation program under Grant 714161, and by Comunidad de Madrid and FEDER through IntCARE-CM. The work of Ignacio Peis has been also supported by by Spanish government (MIU) under grant FPU18/00516

New Granada Medium for detection and identification of group B streptococci.

Comparative Study; Journal Article; Research Support, Non-U.S. Gov't;A methotrexate-containing medium for the detection of beta-hemolytic group B streptococci from clinical specimens on the basis of detection of pigment is described. The medium contained peptone, starch, serum, MgSO4, glucose, pyruvate, methotrexate (as pigment enhancer), phosphate-morpholine-propanesulfonic acid buffer, and selective agents. The recovery of beta-hemolytic group B streptococci was comparable to that obtained with selective broth.This work was supported by a grant from the FISSS, Spanish Ministry of Health (project 91/0311).Ye

A Heavy Tailed Expectation Maximization Hidden Markov Random Field Model with Applications to Segmentation of MRI

A wide range of segmentation approaches assumes that intensity histograms extracted from magnetic resonance images (MRI) have a distribution for each brain tissue that can be modeled by a Gaussian distribution or a mixture of them. Nevertheless, intensity histograms of White Matter and Gray Matter are not symmetric and they exhibit heavy tails. In this work, we present a hidden Markov random field model with expectation maximization (EM-HMRF) modeling the components using the α-stable distribution. The proposed model is a generalization of the widely used EM-HMRF algorithm with Gaussian distributions. We test the α-stable EM-HMRF model in synthetic data and brain MRI data. The proposed methodology presents two main advantages: Firstly, it is more robust to outliers. Secondly, we obtain similar results than using Gaussian when the Gaussian assumption holds. This approach is able to model the spatial dependence between neighboring voxels in tomographic brain MRI