LIVIVO - The Search Portal for Life Sciences

zur deutschen Oberfläche wechseln
Advanced search

Search results

Result 1 - 7 of total 7

Search options

  1. Book ; Online: Interaction Order Prediction for Temporal Graphs

    Bannur, Nayana / Srivastava, Mashrin / Vardhan, Harsha

    2023  

    Abstract: Link prediction in graphs is a task that has been widely investigated. It has been applied in various domains such as knowledge graph completion, content/item recommendation, social network recommendations and so on. The initial focus of most research ... ...

    Abstract Link prediction in graphs is a task that has been widely investigated. It has been applied in various domains such as knowledge graph completion, content/item recommendation, social network recommendations and so on. The initial focus of most research was on link prediction in static graphs. However, there has recently been abundant work on modeling temporal graphs, and consequently one of the tasks that has been researched is link prediction in temporal graphs. However, most of the existing work does not focus on the order of link formation, and only predicts the existence of links. In this study, we aim to predict the order of node interactions.
    Keywords Computer Science - Social and Information Networks ; Computer Science - Computation and Language ; Computer Science - Machine Learning
    Publishing date 2023-02-04
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

    Full text online

    More links

    Kategorien

    Inter-library loan at ZB MED

    Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.

  2. Article ; Online: Predicting COVID-19 case status from self-reported symptoms and behaviors using data from a massive online survey

    Srivastava, Mashrin / Reinhart, Alex / Mejia, Robin

    medRxiv

    Abstract: With the varying availability of RT-PCR testing for COVID-19 across time and location, there is a need for alternative methods of predicting COVID-19 case status. In this study, multiple machine learning (ML) models were trained and assessed for their ... ...

    Abstract With the varying availability of RT-PCR testing for COVID-19 across time and location, there is a need for alternative methods of predicting COVID-19 case status. In this study, multiple machine learning (ML) models were trained and assessed for their ability to accurately predict the COVID-19 case status using US COVID-19 Trends and Impact Survey (CTIS) data. The CTIS includes information on testing, symptoms, demographics, behaviors, and vaccination status. The best performing model was XGBoost, which achieved an F1 score of ≈ 94% in predicting whether an individual was COVID-19 positive or negative. This is a notable improvement on existing models for predicting COVID-19 case status and demonstrates the potential for ML methods to provide policy-relevant estimates.
    Keywords covid19
    Language English
    Publishing date 2023-02-07
    Publisher Cold Spring Harbor Laboratory Press
    Document type Article ; Online
    DOI 10.1101/2023.02.03.23285405
    Database COVID19

    Full text online

    Kategorien

    Inter-library loan at ZB MED

    Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.

  3. Article: Automated emergency paramedical response system.

    Srivastava, Mashrin / Suvarna, Saumya / Srivastava, Apoorva / Bharathiraja, S

    Health information science and systems

    2018  Volume 6, Issue 1, Page(s) 22

    Abstract: With the evolution of technology, the fields of medicine and science have also witnessed numerous advancements. In medical emergencies, a few minutes can be the difference between life and death. The obstacles encountered while providing medical ... ...

    Abstract With the evolution of technology, the fields of medicine and science have also witnessed numerous advancements. In medical emergencies, a few minutes can be the difference between life and death. The obstacles encountered while providing medical assistance can be eliminated by ensuring quicker care and accessible systems. To this effect, the proposed end-to-end system-automated emergency paramedical response system (AEPRS) is semi-autonomous and utilizes aerial distribution by drones, for providing medical supplies on site in cases of paramedical emergencies as well as for patients with a standing history of diseases. Security of confidential medical information is a major area of concern for patients. Confidentiality has been achieved by using decentralised distributed computing to ensure security for the users without involving third-party institutions. AEPRS focuses not only on urban areas but also on semi-urban and rural areas. In urban areas where access to internet is widely available, a healthcare chatbot caters to the individual users and provides a diagnosis based on the symptoms provided by the patients. In semi-urban and rural areas, community hospitals have the option of providing specialised healthcare in spite of the absence of a specialised doctor. Additionally, object recognition and face recognition by using the concept of edge AI enables deep neural networks to run on the edge, without the need for GPU or internet connectivity to connect to the cloud. AEPRS is an airborne emergency medical supply delivery system. It uses the data entered by the user to deduce the best possible solution, in case of an alerted emergency situation and responds to the user accordingly.
    Language English
    Publishing date 2018-11-13
    Publishing country England
    Document type Journal Article
    ZDB-ID 2697647-X
    ISSN 2047-2501
    ISSN 2047-2501
    DOI 10.1007/s13755-018-0061-1
    Database MEDical Literature Analysis and Retrieval System OnLINE

    Full text online

    More links

    Kategorien

    Order via subito

    This service is chargeable due to the Delivery terms set by subito. Orders including an article and supplementary material will be classified as separate orders. In these cases, fees will be demanded for each order.

  4. Article: Clinico-Genomic Analysis Reveals Mutations Associated with COVID-19 Disease Severity: Possible Modulation by RNA Structure

    Mehta, Priyanka / Alle, Shanmukh / Chaturvedi, Anusha / Swaminathan, Aparna / Saifi, Sheeba / Maurya, Ranjeet / Chattopadhyay, Partha / Devi, Priti / Chauhan, Ruchi / Kanakan, Akshay / Vasudevan, Janani Srinivasa / Sethuraman, Ramanathan / Chidambaram, Subramanian / Srivastava, Mashrin / Chakravarthi, Avinash / Jacob, Johnny / Namagiri, Madhuri / Konala, Varma / Jha, Sujeet /
    Priyakumar, U. Deva / Vinod, P. K. / Pandey, Rajesh

    Pathogens. 2021 Aug. 31, v. 10, no. 9

    2021  

    Abstract: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) manifests a broad spectrum of clinical presentations, varying in severity from asymptomatic to mortality. As the viral infection spread, it evolved and developed into many variants of concern. ... ...

    Abstract Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) manifests a broad spectrum of clinical presentations, varying in severity from asymptomatic to mortality. As the viral infection spread, it evolved and developed into many variants of concern. Understanding the impact of mutations in the SARS-CoV-2 genome on the clinical phenotype and associated co-morbidities is important for treatment and preventionas the pandemic progresses. Based on the mild, moderate, and severe clinical phenotypes, we analyzed the possible association between both, the clinical sub-phenotypes and genomic mutations with respect to the severity and outcome of the patients. We found a significant association between the requirement of respiratory support and co-morbidities. We also identified six SARS-CoV-2 genome mutations that were significantly correlated with severity and mortality in our cohort. We examined structural alterations at the RNA and protein levels as a result of three of these mutations: A26194T, T28854T, and C25611A, present in the Orf3a and N protein. The RNA secondary structure change due to the above mutations can be one of the modulators of the disease outcome. Our findings highlight the importance of integrative analysis in which clinical and genetic components of the disease are co-analyzed. In combination with genomic surveillance, the clinical outcome-associated mutations could help identify individuals for priority medical support.
    Keywords COVID-19 infection ; RNA ; Severe acute respiratory syndrome coronavirus 2 ; disease severity ; genome ; genomics ; monitoring ; mortality ; pandemic ; phenotype
    Language English
    Dates of publication 2021-0831
    Publishing place Multidisciplinary Digital Publishing Institute
    Document type Article
    ZDB-ID 2695572-6
    ISSN 2076-0817
    ISSN 2076-0817
    DOI 10.3390/pathogens10091109
    Database NAL-Catalogue (AGRICOLA)

    More links

    Kategorien

    Order via subito

    This service is chargeable due to the Delivery terms set by subito. Orders including an article and supplementary material will be classified as separate orders. In these cases, fees will be demanded for each order.

  5. Article: Clinico-Genomic Analysis Reveals Mutations Associated with COVID-19 Disease Severity: Possible Modulation by RNA Structure.

    Mehta, Priyanka / Alle, Shanmukh / Chaturvedi, Anusha / Swaminathan, Aparna / Saifi, Sheeba / Maurya, Ranjeet / Chattopadhyay, Partha / Devi, Priti / Chauhan, Ruchi / Kanakan, Akshay / Vasudevan, Janani Srinivasa / Sethuraman, Ramanathan / Chidambaram, Subramanian / Srivastava, Mashrin / Chakravarthi, Avinash / Jacob, Johnny / Namagiri, Madhuri / Konala, Varma / Jha, Sujeet /
    Priyakumar, U Deva / Vinod, P K / Pandey, Rajesh

    Pathogens (Basel, Switzerland)

    2021  Volume 10, Issue 9

    Abstract: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) manifests a broad spectrum of clinical presentations, varying in severity from asymptomatic to mortality. As the viral infection spread, it evolved and developed into many variants of concern. ... ...

    Abstract Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) manifests a broad spectrum of clinical presentations, varying in severity from asymptomatic to mortality. As the viral infection spread, it evolved and developed into many variants of concern. Understanding the impact of mutations in the SARS-CoV-2 genome on the clinical phenotype and associated co-morbidities is important for treatment and preventionas the pandemic progresses. Based on the mild, moderate, and severe clinical phenotypes, we analyzed the possible association between both, the clinical sub-phenotypes and genomic mutations with respect to the severity and outcome of the patients. We found a significant association between the requirement of respiratory support and co-morbidities. We also identified six SARS-CoV-2 genome mutations that were significantly correlated with severity and mortality in our cohort. We examined structural alterations at the RNA and protein levels as a result of three of these mutations: A26194T, T28854T, and C25611A, present in the Orf3a and N protein. The RNA secondary structure change due to the above mutations can be one of the modulators of the disease outcome. Our findings highlight the importance of integrative analysis in which clinical and genetic components of the disease are co-analyzed. In combination with genomic surveillance, the clinical outcome-associated mutations could help identify individuals for priority medical support.
    Language English
    Publishing date 2021-08-31
    Publishing country Switzerland
    Document type Journal Article
    ZDB-ID 2695572-6
    ISSN 2076-0817
    ISSN 2076-0817
    DOI 10.3390/pathogens10091109
    Database MEDical Literature Analysis and Retrieval System OnLINE

    More links

    Kategorien

    Order via subito

    This service is chargeable due to the Delivery terms set by subito. Orders including an article and supplementary material will be classified as separate orders. In these cases, fees will be demanded for each order.

  6. Article ; Online: COVID-19 Risk Stratification and Mortality Prediction in Hospitalized Indian Patients: Harnessing clinical data for public health benefits.

    Alle, Shanmukh / Kanakan, Akshay / Siddiqui, Samreen / Garg, Akshit / Karthikeyan, Akshaya / Mehta, Priyanka / Mishra, Neha / Chattopadhyay, Partha / Devi, Priti / Waghdhare, Swati / Tyagi, Akansha / Tarai, Bansidhar / Hazarik, Pranjal Pratim / Das, Poonam / Budhiraja, Sandeep / Nangia, Vivek / Dewan, Arun / Sethuraman, Ramanathan / Subramanian, C /
    Srivastava, Mashrin / Chakravarthi, Avinash / Jacob, Johnny / Namagiri, Madhuri / Konala, Varma / Dash, Debasish / Sethi, Tavpritesh / Jha, Sujeet / Agrawal, Anurag / Pandey, Rajesh / Vinod, P K / Priyakumar, U Deva

    PloS one

    2022  Volume 17, Issue 3, Page(s) e0264785

    Abstract: The variability of clinical course and prognosis of COVID-19 highlights the necessity of patient sub-group risk stratification based on clinical data. In this study, clinical data from a cohort of Indian COVID-19 hospitalized patients is used to develop ... ...

    Abstract The variability of clinical course and prognosis of COVID-19 highlights the necessity of patient sub-group risk stratification based on clinical data. In this study, clinical data from a cohort of Indian COVID-19 hospitalized patients is used to develop risk stratification and mortality prediction models. We analyzed a set of 70 clinical parameters including physiological and hematological for developing machine learning models to identify biomarkers. We also compared the Indian and Wuhan cohort, and analyzed the role of steroids. A bootstrap averaged ensemble of Bayesian networks was also learned to construct an explainable model for discovering actionable influences on mortality and days to outcome. We discovered blood parameters, diabetes, co-morbidity and SpO2 levels as important risk stratification features, whereas mortality prediction is dependent only on blood parameters. XGboost and logistic regression model yielded the best performance on risk stratification and mortality prediction, respectively (AUC score 0.83, AUC score 0.92). Blood coagulation parameters (ferritin, D-Dimer and INR), immune and inflammation parameters IL6, LDH and Neutrophil (%) are common features for both risk and mortality prediction. Compared with Wuhan patients, Indian patients with extreme blood parameters indicated higher survival rate. Analyses of medications suggest that a higher proportion of survivors and mild patients who were administered steroids had extreme neutrophil and lymphocyte percentages. The ensemble averaged Bayesian network structure revealed serum ferritin to be the most important predictor for mortality and Vitamin D to influence severity independent of days to outcome. The findings are important for effective triage during strains on healthcare infrastructure.
    MeSH term(s) Adolescent ; Adult ; Aged ; Aged, 80 and over ; Bayes Theorem ; COVID-19/epidemiology ; COVID-19/etiology ; COVID-19/mortality ; Child ; China/epidemiology ; Female ; Hospitalization/statistics & numerical data ; Humans ; India/epidemiology ; Machine Learning ; Male ; Middle Aged ; Models, Statistical ; Risk Assessment/methods ; Risk Factors ; Young Adult
    Language English
    Publishing date 2022-03-17
    Publishing country United States
    Document type Journal Article ; Research Support, Non-U.S. Gov't ; Research Support, U.S. Gov't, Non-P.H.S.
    ZDB-ID 2267670-3
    ISSN 1932-6203 ; 1932-6203
    ISSN (online) 1932-6203
    ISSN 1932-6203
    DOI 10.1371/journal.pone.0264785
    Database MEDical Literature Analysis and Retrieval System OnLINE

    More links

    Kategorien

    Order via subito

    This service is chargeable due to the Delivery terms set by subito. Orders including an article and supplementary material will be classified as separate orders. In these cases, fees will be demanded for each order.

  7. Article ; Online: COVID-19 Risk Stratification and Mortality Prediction in Hospitalized Indian Patients

    Alle, Shanmukh / Siddiqui, Samreen / Kanakan, Akshay / Garg, Akshit / Karthikeyan, Akshaya / Mishra, Neha / Waghdhare, Swati / Tyagi, Akansha / Tarai, Bansidhar / Hazarika, Pranjal Pratim / Das, Poonam / Budhiraja, Sandeep / Nangia, Vivek / Dewan, Arun / Sethuraman, Ramanathan / Subramanian, C. / Srivastava, Mashrin / Chakravarthi, Avinash / Jacob, Johnny /
    Namagiri, Madhuri / Konala, Varma / Dash, Debasish / Jha, Sujeet / Pandey, Rajesh / Agrawal, Anurag / Vinod, P K / Priyakumar, U. Deva

    medRxiv

    Abstract: The clinical course of coronavirus disease 2019 (COVID-19) infection is highly variable with the vast majority recovering uneventfully but a small fraction progressing to severe disease and death. Appropriate and timely supportive care can reduce ... ...

    Abstract The clinical course of coronavirus disease 2019 (COVID-19) infection is highly variable with the vast majority recovering uneventfully but a small fraction progressing to severe disease and death. Appropriate and timely supportive care can reduce mortality and it is critical to evolve better patient risk stratification based on simple clinical data, so as to perform effective triage during strains on the healthcare infrastructure. This study presents risk stratification and mortality prediction models based on usual clinical data from 544 COVID-19 patients from New Delhi, India using machine learning methods. A Random Forest classifier yielded the best performance on risk stratification (F1 score of 0.81). A logistic regression model yielded the best performance on mortality prediction (F1 score of 0.71). Significant biomarkers for predicting risk and mortality were identified. Examination of the data in comparison to a similar dataset with a Wuhan cohort of 375 patients was undertaken to understand the much lower mortality rates in India and the possible reasons thereof. The comparison indicated higher survival rate in the Delhi cohort even when patients had similar parameters as the Wuhan patients who died. Steroid administration was very frequent in Delhi patients, especially in surviving patients whose biomarkers indicated severe disease. This study helps in identifying the high-risk patient population and suggests treatment protocols that may be useful in countries with high mortality rates.
    Keywords covid19
    Language English
    Publishing date 2020-12-22
    Publisher Cold Spring Harbor Laboratory Press
    Document type Article ; Online
    DOI 10.1101/2020.12.19.20248524
    Database COVID19

    Full text online

    Kategorien

    Inter-library loan at ZB MED

    Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.

To top