El Grupo de Tratamiento de Señal (Departamento de Teoría de la Señal y Comunicaciones, Universidad Carlos III de Madrid, España), ofrece su experiencia en el desarrollo de sistemas de monitorización basados en redes de sensores. Las principales ventajas de esta tecnología son la reducción de costes, el ahorro de tiempo de proceso y la mayor fiabilidad de los resultados. Se busca cooperación técnica para el desarrollo con financiación interna y externa.
The Signal Processing Group (Department of Signal Theory and Communications, University Carlos III, Madrid, Spain) offers the expertise of its members in the development of monitoring systems based on sensor networks. The main advantages of this technology are the decreased cost, the time saved and the increased reliability of the results. Technical cooperation for the research and development with internal and external funding is sought.
Medical data sets are usually corrupted by noise and missing data. These missing patterns are commonly assumed to be completely random, but in medical scenarios, the reality is that these patterns occur in bursts due to sensors that are off for some time or data collected in a misaligned uneven fashion, among other causes. This paper proposes to model medical data records with heterogeneous data types and bursty missing data using sequential variational autoencoders (VAEs). In particular, we propose a new methodology, the Shi-VAE, which extends the capabilities of VAEs to sequential streams of data with missing observations. We compare our model against state-of-theart solutions in an intensive care unit database (ICU) and a dataset of passive human monitoring. Furthermore, we find that standard error metrics such as RMSE are not conclusive enough to assess temporal models and include in our analysis the cross-correlation between the ground truth nd the imputed signal. We show that Shi-VAE achieves the best performance in terms of using both metrics, with lower computational complexity than the GP-VAE model, which is the state-of-the-art method for medical records. ; This work was supported in part by Spanish Government MCI under Grants TEC2017-92552-EXP and RTI2018-099655-B-100, in part by Comunidad de Madrid under Grants IND2017/TIC-7618, IND2018/TIC-9649, IND2020/TIC-17372, and Y2018/TCS-4705, in part by BBVA Foundation under the Deep-DARWiN Project, and in part 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.
One of the current challenges faced by health centers is to reduce the number of patients who do not attend their appointments. The existence of these patients causes the underutilization of the center's services, which reduces their income and extends patient's access time. In order to reduce these negative effects, several appointment scheduling systems have been developed. With the recent availability of electronic health records, patient scheduling systems that incorporate the patient's no-show prediction are being developed. However, the benefits of including a personalized individual variable time slot for each patient in those probabilistic systems have not been yet analyzed. In this article, we propose a scheduling system based on patients' no-show probabilities with variable time slots and a dynamic priority allocation scheme. The system is based on the solution of a mixed-integer programming model that aims at maximizing the expected profits of the clinic, accounting for first and follow-up visits. We validate our findings by performing an extensive simulation study based on real data and specific scheduling requirements provided by a Spanish hospital. The results suggest potential benefits with the implementation of the proposed allocation system with variable slot times. In particular, the proposed model increases the annual cumulated profit in more than 50% while decreasing the waiting list and waiting times by 30% and 50%, respectively, with respect to the actual appointment scheduling system. ; This work was partly funded by Carlos III (ISCIII PI16/01852), American Foundation for Suicide Prevention (LSRG-1-005-16), the Madrid Regional Government (B2017/BMD-3740 AGES-CM 2CM; Y2018/TCS-4705 PRACTICO-CM), MINECO/FEDER ('ADVENTURE', id. TEC2015-69868-C2-1-R), MCIU Explora Grant 'aMBITION' (id. TEC2017-92552-EXP), and MICINN ('CLARA', id. RTI2018-099655-B-I00).
Crowdsourcing has been proven to be an effective and efficient tool to annotate large data-sets. User annotations are often noisy, so methods to combine the annotations to produce reliable estimates of the ground truth are necessary. We claim that considering the existence of clusters of users in this combination step can improve the performance. This is especially important in early stages of crowdsourcing implementations, where the number of annotations is low. At this stage there is not enough information to accurately estimate the bias introduced by each annotator separately, so we have to resort to models that consider the statistical links among them. In addition, finding these clusters is interesting in itself as knowing the behavior of the pool of annotators allows implementing efficient active learning strategies. Based on this, we propose in this paper two new fully unsupervised models based on a Chinese restaurant process (CRP) prior and a hierarchical structure that allows inferring these groups jointly with the ground truth and the properties of the users. Efficient inference algorithms based on Gibbs sampling with auxiliary variables are proposed. Finally, we perform experiments, both on synthetic and real databases, to show the advantages of our models over state-of-the-art algorithms. ; Pablo G. Moreno is supported by an FPU fellowship from the Spanish Ministry of Education (AP2009-1513). This work has been partly supported by Ministerio de Economía of Spain ('COMONSENS', id. CSD2008-00010, 'ALCIT', id. TEC2012-38800-C03-01, 'COMPREHENSION', id. TEC2012-38883-C02-01) and Comunidad de Madrid (project 'CASI-CAM-CM', id. S2013/ICE-2845). This work was also supported by the European Union 7th Framework Programme through the Marie Curie Initial Training Network "Machine Learning for Personalized Medicine" MLPM2012, Grant No. 316861. Yee Why Teh's research leading to these results has received funding from the European Research Council under the European Union's Seventh Framework Programme (FP7/2007-2013) ERC grant agreement no. 617411.
The problem of blind sparse analysis of electrogram (EGM) signals under atrial fibrillation (AF) conditions is considered in this paper. A mathematical model for the observed signals that takes into account the multiple foci typically appearing inside the heart during AF is firstly introduced. Then, a reconstruction model based on a fixed dictionary is developed and several alternatives for choosing the dictionary are discussed. In order to obtain a sparse solution, which takes into account the biological restrictions of the problem at the same time, the paper proposes using a Least Absolute Shrinkage and Selection Operator (LASSO) regularization followed by a post-processing stage that removes low amplitude coefficients violating the refractory period characteristic of cardiac cells. Finally, spectral analysis is performed on the clean activation sequence obtained from the sparse learning stage in order to estimate the number of latent foci and their frequencies. Simulations on synthetic signals and applications on real data are provided to validate the proposed approach. ; This work has been partly financed by the Spanish government through the CONSOLIDER-INGENIO 2010 program (COMONSENS project, ref. CSD2008-00010), as well as projects COSIMA (TEC2010-19545-C04-03), ALCIT (TEC2012 38800- C03-01), COMPREHENSION (TEC2012-38883-C02-01) and DISSECT (TEC2012-38058-C03-01).
Background: Anxiety symptoms during public health crises are associated with adverse psychiatric outcomes and impaired health decision-making. The interaction between real-time social media use patterns and clinical anxiety during infectious disease outbreaks is underexplored. Objective: We aimed to evaluate the usage pattern of 2 types of social media apps (communication and social networking) among patients in outpatient psychiatric treatment during the COVID-19 surge and lockdown in Madrid, Spain and their short-term anxiety symptoms (7-item General Anxiety Disorder scale) at clinical follow-up. Methods: The individual-level shifts in median social media usage behavior from February 1 through May 3, 2020 were summarized using repeated measures analysis of variance that accounted for the fixed effects of the lockdown (prelockdown versus postlockdown), group (clinical anxiety group versus nonclinical anxiety group), the interaction of lockdown and group, and random effects of users. A machine learning–based approach that combined a hidden Markov model and logistic regression was applied to predict clinical anxiety (n=44) and nonclinical anxiety (n=51), based on longitudinal time-series data that comprised communication and social networking app usage (in seconds) as well as anxiety-associated clinical survey variables, including the presence of an essential worker in the household, worries about life instability, changes in social interaction frequency during the lockdown, cohabitation status, and health status. Results: Individual-level analysis of daily social media usage showed that the increase in communication app usage from prelockdown to lockdown period was significantly smaller in the clinical anxiety group than that in the nonclinical anxiety group (F1,72=3.84, P=.05). The machine learning model achieved a mean accuracy of 62.30% (SD 16%) and area under the receiver operating curve 0.70 (SD 0.19) in 10-fold cross-validation in identifying the clinical anxiety group. Conclusions: Patients who reported severe anxiety symptoms were less active in communication apps after the mandated lockdown and more engaged in social networking apps in the overall period, which suggested that there was a different pattern of digital social behavior for adapting to the crisis. Predictive modeling using digital biomarkers—passive-sensing of shifts in category-based social media app usage during the lockdown—can identify individuals at risk for psychiatric sequelae. ; JR was supported by the American Psychiatric Association 2021 Junior Psychiatrist Research Colloquium (NIDA R-13 grant). ES received funding from the European Union Horizon 2020 research and innovation program (Marie Sklodowska-Curie grant 813533). AA is supported by the Spanish Ministerio de Ciencia, Innovación y Universidades (RTI2018-099655-B-I00), the Comunidad de Madrid (Y2018/TCS-4705 PRACTICO-CM), and the BBVA Foundation (Deep-DARWiN grant).
Background: Although insight in schizophrenia spectrum disorders (SSD) has been associated with positive outcomes, the effect size of previous treatments on insight has been relatively small to date. The metacognitive basis of insight suggests that metacognitive training (MCT) may improve insight and clinical outcomes in SSD, although this remains to be established. Methods: This single-center, assessor-blind, parallel-group, randomised clinical trial (RCT) aims to investigate the efficacy of MCT for improving insight (primary outcome), including clinical and cognitive insight, which will be measured by the Schedule for Assessment of Insight (Expanded version) (SAI-E) and the Beck Cognitive Scale (BCIS), respectively, in (at least) n = 126 outpatients with SSD at three points in time: i) at baseline (T0); ii) after treatment (T1) and iii) at 1-year follow-up (T2). SSD patients receiving MCT and controls attending a non-intervention support group will be compared on insight level changes and several clinical and cognitive secondary outcomes at T1 and T2, whilst adjusting for baseline data. Ecological momentary assessment (EMA) will be piloted to assess functioning in a subsample of participants. Discussion: To the best of our knowledge, this will be the first RCT testing the effect of group MCT on multiple insight dimensions (as primary outcome) in a sample of unselected patients with SSD, including several secondary outcomes of clinical relevance, namely symptom severity, functioning, which will also be evaluated with EMA, hospitalizations and suicidal behaviour. ; This study was supported by the Universidad Autónoma de Madrid and European Union via the Intertalentum Project Grant-Marie Skłodowska Curie Actions (GA 713366) to JDLM who is the Princiapl Investigator. This grant therefore funds both JDLM's salary and the consumable expenses related to the study. JDLM, VGRR, ASEM, MLBE, LMI, LML, SSA, AAR and EBG's salaries come from the Hospital Universitario Fundación Jiménez Díaz, where this study is currently being carried out, which therefore provides the necessary institutional/departmental support for its development. Additional departmental support concerning the use of Ecological Momentary Assessment (see Methods section, page 11 -last paragraph- and page 12 –first paragraph-, for details) is provided by the Instituto de Salud Carlos III (Madrid, Spain) (ISCIII PI16/01852) and the Madrid Regional Government (Madrid, Spain) (B2017/BMD-3740 AGES-CM 2CM; Y2018/TCS-4705 PRACTICO-CM). ASD acknowledges funding supports from University College London, which covers his salary
