Detection of Semantic Duplicates in Temporal Domains Using Directed Acyclic Graphs

We demonstrate the feasibility of a generalized technique for semantic deduplication in temporal data domains using graph-based representations of data records. Structured data records with multiple timestamp attributes per record may be represented as a directed graph where the nodes represent the events and the edges represent event sequences. Edge weights are based on elapsed time between connecting nodes. In comparing two records, we may merge these directed graphs and determine a representative directed acyclic graph (DAG) inclusive of a subset of nodes and edges that maintain the transitive weights of the original graphs. This DAG may then be evaluated by weighting elapsed time equivalences between records at each node and measuring the fraction of nodes represented in the DAG versus the union of nodes between the records being compared. With this information, we establish a duplication score and use a specified threshold requirement to assert duplication. This method is referred to as Temporal Deduplication using Directed Acyclic Graphs (TD:DAG). TD:DAG significantly outperformed established ASNM and ASNM+LCS methods for datasets rep-resenting two disparate domains, COVID-19 government policy data and PlayStation Network (PSN) trophy data. TD:DAG produced highly effective and comparable F1 scores of 0.960 and 0.972 for the two datasets, respectively, versus 0.864/0.938 for ASNM+LCS and 0.817/0.708 for ASNM. © 2023 IEEE.

Severity Clusters and Likelihood of Recovery from Post-Covid-19 Condition

Background: The Post-COVID-19 Condition (PCC) is a novel, long-lasting, poorly understood and highly disabling post-viral syndrome, which poses enormous healthcare, economic and socio-political challenges. Lack of validated biomarkers forces clinical management to be based on clinical definitions, which are imprecise. In the clinic, symptoms tend to present in clusters, which have yet to be properly defined. Also, it is unclear how often PCC resolves, and which factors influence PCC resolution. Method(s): To delineate PCC presentation clusters and explore factors related with PCC resolution, we performed a 2-year prospective cohort study in individuals who recovered from acute COVID-19 regardless of its acute and post-acute severity. All subjects were systematically followed in the outpatient post-COVID-19 clinic of a tertiary care hospital in Spain. PCC was defined as per the WHO 2021 definition. Persistent symptoms were those present >3 months after acute COVID-19, and lasting for >2 consecutive months. PCC recovery was the absence of PCC symptoms during >3 consecutive months. Symptom clusters were identified using Gower's distance matrices, dendograms, PCA and Silhouette techniques. Factors associated with PCC recovery were identified using a directed acyclic graph approach. Result(s): 548 subjects were included;341 (62%) had PCC. The latter were mostly females (69.8%) with mean age of 47.9 (SD 12.2) years. Only 38.1% required hospitalization and 9% required high-flow oxygen during acute COVID-19. Their most frequent comorbidities were allergy (31.4%), obesity (24.8%), dyslipidemia (24.0%) and hypertension (19.6%). At least 3 symptom clusters with additive symptoms were identified: considering only symptoms present in >35% of subjects, Cluster A was enriched in fatigue and dyspnea;Cluster B had Cluster A symptoms plus headache, arthralgia and neurocognitive complains;Cluster C had Cluster B symptoms plus chest pain and tachycardia. PCC recovery was achieved by 26 (7.6%) individuals over 2 years. Male sex (RR 3.01;CI 1.4-6.3), ICU admission (RR 7.85;CI 2.6-23.2), metabolic comorbidity (RR 2.07;CI 1.1-4.1), and mild acute COVID-19 (RR 2.70;CI 1.1-4.6) increased the likelihood of PCC recovery. Conversely, subjects with muscle pain, impaired attention, dyspnea, and tachycardia were less likely to recover from PCC (RR 0.26;CI 0.13-0.52). Conclusion(s): At least 3 severity clusters can be identified in the PCC. Over the first 2 years, only a minority of subjects fully recover from PCC.

Learning linear non-Gaussian directed acyclic graph with diverging number of nodes

An acyclic model, often depicted as a directed acyclic graph (DAG), has been widely employed to represent directional causal relations among collected nodes. In this article, we propose an efficient method to learn linear non-Gaussian DAG in high dimensional cases, where the noises can be of any continuous non-Gaussian distribution. The proposed method leverages the concept of topological layer to facilitate the DAG learning, and its theoretical justification in terms of exact DAG recovery is also established under mild conditions. Particularly, we show that the topological layers can be exactly reconstructed in a bottom-up fashion, and the parent-child relations among nodes can also be consistently established. The established asymptotic DAG recovery is in sharp contrast to that of many existing learning methods assuming parental faithfulness or ordered noise variances. The advantage of the proposed method is also supported by the numerical comparison against some popular competitors in various simulated examples as well as a real application on the global spread of COVID-19. ©2022 Ruixuan Zhao, Xin He, and Junhui Wang.

Mental health in Belgian hospital workers during the first wave of COVID-19

Objectives: Exploring anxiety, depression, and insomnia as a network of symptoms and their intensities among hospital workers after the first wave of COVID19. Method(s): 907 hospital workers have completed the survey including 442 Frontline Workers and 465 Hospital Workers. Online surveys were performed in two hospitals from June 6, 2020, and August 8, 2020, in Belgium. Anxiety, depression and insomnia was assessed by the GAD-7, PHQ-9 and ISI, respectively. We estimated a Directed Acyclic Graph for the items of these questionnaires and networks were compared and described in terms of true positive (connection between two nodes present in both networks), falls positive (connection between two nodes present among Frontline network) and falls negative (connection between two nodes present among Hospital Workers network). Finally, intensity of symptoms was calculated using the total mean score and severity frequency of the three questionnaires. Result(s): For both groups, the anxiety, depression, and insomnia items are independent: The three symptomatologies form clusters and do not seem to interact with each other, in both groups. Network comparison revealed 9 true positives, 11 false positives, and 9 false negative. Most of the different connections are found within the symptoms of insomnia. The insomnia symptom network in the Hospital group is characterized by "Difficulty maintaining sleep" as the initiating symptom that results to "Worry". In the Frontline group, "Interference" seems be the initiating symptom, which leads to the Early morning awakening. About intensity of symptoms, Frontline showed a significant higher intensity than Hospital Workers for anxiety, depression, and insomnia. Moreover, there were significantly more workers with moderate symptoms among Frontline than Hospital workers in comparison with "no symptoms" for our three scales. Conclusion(s): The network of anxiety and depression are similar between Frontline and Hospital, but not the insomnia network characterized by different initiating symptoms and leading to a different final symptom. In addition, Frontline have significantly higher complaints of anxiety, depression, and insomnia than Hospital workers, which is consistent with the plethora of studies on this topic. These differences in networks should be considered to developing specific treatment of insomnia in these two populations.

GAINING CONSENSUS ON EXPERT RULE STATEMENTS FOR A DIGITAL TWIN PATIENT MODEL IN THE ICU USING A DELPHI METHOD

SESSION TITLE: Optimizing Resources in the ICU SESSION TYPE: Original Investigations PRESENTED ON: 10/16/2022 10:30 am - 11:30 am PURPOSE: The COVID-19 pandemic has exposed worldwide heterogeneity in the application of fundamental critical care principles and best practices. New methods and strategies to facilitate timely and accurate interventions are needed. If built on a robust foundation of physiologic principles, a virtual critically ill patient (aka digital twin) could better inform decision making in critical care. When used in clinical practice, a digital twin may allow bedside providers to preview how organ systems interact to cause a clinical effect, providing the opportunity to test the effects of various interventions virtually, without exposing an actual patient to potential harm. Building on our previous work with a digital twin model of critically ill patients with sepsis, this current project focuses specifically on the respiratory system. METHODS: We assembled a modified Delphi panel of 36 international critical care experts. We modeled elements of respiratory system pathophysiology using directed acyclic graphs (DAG) and derived several statements describing associated ICU clinical processes. Panelists participated in three Delphi rounds to gauge agreement on 71 final statements using a 6-point Likert scale. Agreement was defined as >80% selection of a 5 (“agree”) or 6 (“strongly agree”). RESULTS: The first Delphi round included statements of pulmonary physiology affecting critically ill patients, eg pulmonary edema, hypoxemic and hypercapnic respiratory failure, shock, acute respiratory distress syndrome (ARDS), airway obstruction, restrictive lung disease, and ventilation-perfusion mismatch. Agreement was achieved on 60 (84.5%) of expert statements after completion of two rounds. After partial completion of the third round, agreement increased to 62 (87%). Statements with the most agreement included the physiology and management of airway obstruction decreasing alveolar ventilation and the effects of alveolar infiltrates on ventilation-perfusion matching. Lowest agreement was noted for the statements describing the interaction between shock and hypoxemic respiratory failure due to increased oxygen consumption and ARDS increasing dead space. CONCLUSIONS: An international cohort of critical care experts reached 87% agreement on our rule statements for respiratory system pathophysiology. The Delphi approach appears to be an effective way to refine content for our digital twin model. CLINICAL IMPLICATIONS: Expert consensus can be used to strengthen the respiratory physiology statements used to direct the ICU digital twin patient model. With a digital twin based on refined respiratory physiology statements, bedside providers may preview how organ systems interact to cause a clinical effect without exposing an actual patient to various interventions. DISCLOSURES: No relevant relationships by Ognjen Gajic, value=Royalty Removed 06/06/2022 by Ognjen Gajic No relevant relationships by Amos Lal No relevant relationships by John Litell No relevant relationships by Amy Montgomery

Depression and PTSD in the aftermath of strict COVID-19 lockdowns: a cross-sectional and longitudinal network analysis.

Background: Post-traumatic stress disorder (PTSD) and major depressive disorder (MDD) are two highly comorbid psychological outcomes commonly studied in the context of stress and potential trauma. In Hubei, China, of which Wuhan is the capital, residents experienced unprecedented stringent lockdowns in the early months of 2020 when COVID-19 was first reported. The comorbidity between PTSD and MDD has been previously studied using network models, but often limited to cross-sectional data and analysis. Objectives: This study aims to examine the cross-sectional and longitudinal network structures of MDD and PTSD symptoms using both undirected and directed methods. Methods: Using three types of network analysis - cross-sectional undirected network, longitudinal undirected network, and directed acyclic graph (DAG) - we examined the interrelationships between MDD and PTSD symptoms in a sample of Hubei residents assessed in April, June, August, and October 2020. We identified the most central symptoms, the most influential bridge symptoms, and causal links among symptoms. Results: In both cross-sessional and longitudinal networks, the most central depressive symptoms included sadness and depressed mood, whereas the most central PTSD symptoms changed from irritability and hypervigilance at the first wave to difficulty concentrating and avoidance of potential reminders at later waves. Bridge symptoms showed similarities and differences between cross-sessional and longitudinal networks with irritability/anger as the most influential bridge longitudinally. The DAG found feeling blue and intrusive thoughts the gateways to the emergence of other symptoms. Conclusions: Combining cross-sectional and longitudinal analysis, this study elucidated central and bridge symptoms and potential causal pathways among PTSD and depression symptoms. Clinical implications and limitations are discussed.

Antecedentes: El trastorno de estrés postraumático (TEPT) y el trastorno depresivo mayor (TDM) son dos resultados psicológicos altamente comórbidos que se estudian comúnmente en el contexto del estrés y trauma potencial. En Hubei, China, de la cual Wuhan es la capital, los residentes experimentaron cuarentenas estrictas sin precedentes en los primeros meses de 2020 cuando se informó por primera vez del COVID-19. La comorbilidad entre TEPT y TDM se ha estudiado previamente utilizando modelos de red, pero a menudo se limita a datos y análisis transversales.Objetivos: Este estudio tiene como objetivo examinar las estructuras de red transversales y longitudinales de los síntomas de TDM y TEPT utilizando métodos dirigidos y no dirigidos.Métodos: Mediante el uso de tres tipos de análisis de red: red no dirigido transversal, red no dirigido longitudinal y gráfico acíclico dirigido (DAG), examinamos las interrelaciones entre los síntomas de TDM y TEPT en una muestra de residentes de Hubei evaluados en abril, junio, agosto y octubre de 2020. Identificamos los síntomas centrales, los síntomas puente más influyentes y los vínculos causales entre los síntomas.Resultados: Tanto en redes transversales como longitudinales, los síntomas depresivos más centrales incluyeron tristeza y estado de ánimo deprimido, mientras que los síntomas de TEPT más centrales cambiaron de irritabilidad e hipervigilancia en la primera ola a dificultad para concentrarse y evitar posibles recordatorios en las oleadas posteriores. Los síntomas puente, mostraron similitudes y diferencias entre las redes transversales y longitudinales con irritabilidad/ira como el puente más influyente longitudinalmente. El DAG descubrió que la tristeza y los pensamientos intrusivos son las puertas de entrada a la aparición de otros síntomas.Conclusiones: Al combinar los análisis transversal y longitudinal, este estudio elucidó los síntomas centrales y puente y las posibles vías causales entre los síntomas de TEPT y de depresión. Se discuten las implicaciones clínicas y las limitaciones.

Spillover effect among independent carbon markets: evidence from China’s carbon markets

Carbon pricing is one of the key policy tools in the green recovery of the post-COVID-19 era. As linkages among ETSs worldwide are future trend, the carbon price spillover effects among markets are needed to be explored. This study examines the spillover effects and dynamic linkages of carbon prices using the example of China’s pilot carbon markets during 2015–2019, which are seemingly independent carbon markets. A structural vector error correction model and an improved directed acyclic graph approach are applied. The main results are as follows. First, the linkages among the five pilots demonstrate features of “two small-world networks.” Specifically, these are the Guangdong and Hubei network and the Beijing, Shenzhen and Shanghai network. Second, Shenzhen, Beijing and Hubei ranked as the top three pilots in terms of external spillover effect, accounting for 36.25%, 29.76%, and 25.59%, respectively. Second, Guangdong pilot has increasing influence on the Hubei, Shenzhen and Beijing pilots. Third, trading activities are positive contributors to the spillover, while the allowance illiquidity ratio and volatility are negative factors. The findings imply that to retain an expectable abatement costs in achieving the climate goals in green recovery, carbon prices in other potentially related markets should be considered by the policy maker in addition to its own policy design. © 2022, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.

Building Trust with a Contact Tracing Application: A Blockchain Approach

On March 11, 2020, the novel coronavirus (COVID-19) was declared a global pandemic. With no treatment or vaccine available at the time, it was necessary to rely on non-pharmaceutical methods for case identification and contact tracing. This kind of approach has good results in detecting and preventing tuberculosis, sexually transmitted infections, and vaccine-preventable diseases. Contact tracing and keeping safe distances are crucial to containing the spread of COVID-19. Nonetheless, contact tracing is a complex intervention, it involves quarantining and investigating close contacts. Manual contact tracing methods are slow, require a large amount of effort, and more often than not rely on the memory or assumptions of individuals. To combat these downsides, contact tracing applications were developed, resulting in quicker and more reliable recognition of infected individuals. However, because of the complex nature of these applications and their lack of transparency, a large portion of the population started doubting the privacy of the data collected. Soon after, many of these applications started to dwindle in the user department, which caused a feedback loop. “If fewer people are using the application, the application itself becomes useless, and there is no longer a reason to use it.” Is clear that the main issue behind their downfall was an overwhelming lack of trust. In response, this paper will analyze how the use of blockchain technology can help the development of a more transparent application. And describe how a proof of work based on this concept was implemented. On the same note, it will also approach why was Hyperledger Sawtooth chosen, instead of more popular solutions such as Bitcoin or Ethereum. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Is the Way Forward to Step Back? Documenting the Frequency With Which Study Goals Are Misaligned With Study Methods and Interpretations in the Epidemiologic Literature.

In any research study, there is an underlying process that should begin with a clear articulation of the study's goal. The study's goal drives this process; it determines many study features, including the estimand of interest, the analytic approaches that can be used to estimate it, and which coefficients, if any, should be interpreted. Misalignment can occur in this process when analytic approaches and/or interpretations do not match the study's goal; misalignment is potentially more likely to arise when study goals are ambiguously framed. In this study, misalignment in the observational epidemiologic literature was documented and how the framing of study goals contributes to misalignment was explored. The following 2 misalignments were examined: use of an inappropriate variable selection approach for the goal (a "goal-methods" misalignment) and interpretation of coefficients of variables for which causal considerations were not made (e.g., Table 2 Fallacy, a "goal-interpretation" misalignment). A random sample of 100 articles published 2014-2018 in the top 5 general epidemiology journals were reviewed. Most reviewed studies were causal, with either explicitly stated (n = 13; 13%) or associational-framed (n = 71; 69%) aims. Full alignment of goal-methods-interpretations was infrequent (n = 9; 9%), although clearly causal studies (n = 5 of 13; 38%) were more often fully aligned than were seemingly causal ones (n = 3 of 71; 4%). Goal-methods misalignments were common (n = 34 of 103; 33%), but most frequently, methods were insufficiently reported to draw conclusions (n = 47; 46%). Goal-interpretations misalignments occurred in 31% (n = 32) of the studies and occurred less often when the methods were aligned (n = 2; 2%) compared with when the methods were misaligned (n = 13; 13%).

Big data directed acyclic graph model for real-time COVID-19 twitter stream detection.

Every day, large-scale data are continuously generated on social media as streams, such as Twitter, which inform us about all events around the world in real-time. Notably, Twitter is one of the effective platforms to update countries leaders and scientists during the coronavirus (COVID-19) pandemic. Other people have also used this platform to post their concerns about the spread of this virus and a rapid increase of death cases globally. The aim of this work is to detect anomalous events associated with COVID-19 from Twitter. To this end, we propose a distributed Directed Acyclic Graph topology framework to aggregate and process large-scale real-time tweets related to COVID-19. The core of our system is a novel lightweight algorithm that can automatically detect anomaly events. In addition, our system can also identify, cluster, and visualize important keywords in tweets. On 18 August 2020, our model detected the highest anomaly since many tweets mentioned the casualties' updates and the debates on the pandemic that day. We obtained the three most commonly listed terms on Twitter: "covid", "death", and "Trump" (21,566, 11,779, and 4761 occurrences, respectively), with the highest TF-IDF score for these terms: "people" (0.63637), "school" (0.5921407) and "virus" (0.57385). From our clustering result, the word "death", "corona", and "case" are grouped into one cluster, where the word "pandemic", "school", and "president" are grouped as another cluster. These terms were located near each other on vector space so that they were clustered, indicating people's most concerned topics on Twitter.

Impact of COVID-19 on Lifestyle, Personal Attitudes, and Mental Health Among Korean Medical Students: Network Analysis of Associated Patterns.

The current COVID-19 pandemic have affected our daily lifestyle, pressed us with fear of infection, and thereby changed life satisfaction and mental health. The current study investigated influencing cascade of changes during the COVID-19 among the lifestyle, personal attitudes, and life (dis)satisfaction for medical students, using network-based approaches. This cross-sectional survey used self-reports of 454 medical students during June and July of 2020. Depressive mood, anxiety, and intention to drop out of school were observed in 11.9, 18.5, and 38.3% of medical students, respectively. Directed acyclic graph that estimated directional propagation of the COVID-19 in medical students' daily lives initiated from the perception of unexpected event, propagated to nervous and stressed feeling, trouble relaxing, feeling like a failure, and were followed by trouble concentrating, feeling loss of control for situation, and fear of infecting colleagues. These six features were also principal mediators within the intra-individual covariance networks comprised of changed lifestyle, personal attitude, and mental health at COVID-19 pandemic. Psychosocial supports targeting nervousness, trouble relaxing and concentrating, fear of spreading infection to colleagues, feelings of a failure or loss of situational control are required for better mental health of medical students during the COVID-19 pandemic.

Factors Affecting COVID-19 Outcomes in Cancer Patients: A First Report From Guy's Cancer Center in London.

Background: There is insufficient evidence to support clinical decision-making for cancer patients diagnosed with COVID-19 due to the lack of large studies. Methods: We used data from a single large UK Cancer Center to assess the demographic/clinical characteristics of 156 cancer patients with a confirmed COVID-19 diagnosis between 29 February and 12 May 2020. Logistic/Cox proportional hazards models were used to identify which demographic and/or clinical characteristics were associated with COVID-19 severity/death. Results: 128 (82%) presented with mild/moderate COVID-19 and 28 (18%) with a severe case of the disease. An initial cancer diagnosis >24 months before COVID-19 [OR: 1.74 (95% CI: 0.71-4.26)], presenting with fever [6.21 (1.76-21.99)], dyspnea [2.60 (1.00-6.76)], gastro-intestinal symptoms [7.38 (2.71-20.16)], or higher levels of C-reactive protein [9.43 (0.73-121.12)] were linked with greater COVID-19 severity. During a median follow-up of 37 days, 34 patients had died of COVID-19 (22%). Being of Asian ethnicity [3.73 (1.28-10.91)], receiving palliative treatment [5.74 (1.15-28.79)], having an initial cancer diagnosis >24 months before [2.14 (1.04-4.44)], dyspnea [4.94 (1.99-12.25)], and increased CRP levels [10.35 (1.05-52.21)] were positively associated with COVID-19 death. An inverse association was observed with increased levels of albumin [0.04 (0.01-0.04)]. Conclusions: A longer-established diagnosis of cancer was associated with increased severity of infection as well as COVID-19 death, possibly reflecting the effects a more advanced malignant disease has on this infection. Asian ethnicity and palliative treatment were also associated with COVID-19 death in cancer patients.