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  1. Article ; Online: Associations between Google Search Trends for Symptoms and COVID-19 Confirmed and Death Cases in the United States.

    Abbas, Mostafa / Morland, Thomas B / Hall, Eric S / El-Manzalawy, Yasser

    International journal of environmental research and public health

    2021  Volume 18, Issue 9

    Abstract: We utilize functional data analysis techniques to investigate patterns of COVID-19 positivity and mortality in the US and their associations with Google search trends for COVID-19-related symptoms. Specifically, we represent state-level time series data ... ...

    Abstract We utilize functional data analysis techniques to investigate patterns of COVID-19 positivity and mortality in the US and their associations with Google search trends for COVID-19-related symptoms. Specifically, we represent state-level time series data for COVID-19 and Google search trends for symptoms as smoothed functional curves. Given these functional data, we explore the modes of variation in the data using functional principal component analysis (FPCA). We also apply functional clustering analysis to identify patterns of COVID-19 confirmed case and death trajectories across the US. Moreover, we quantify the associations between Google COVID-19 search trends for symptoms and COVID-19 confirmed case and death trajectories using dynamic correlation. Finally, we examine the dynamics of correlations for the top nine Google search trends of symptoms commonly associated with COVID-19 confirmed case and death trajectories. Our results reveal and characterize distinct patterns for COVID-19 spread and mortality across the US. The dynamics of these correlations suggest the feasibility of using Google queries to forecast COVID-19 cases and mortality for up to three weeks in advance. Our results and analysis framework set the stage for the development of predictive models for forecasting COVID-19 confirmed cases and deaths using historical data and Google search trends for nine symptoms associated with both outcomes.
    MeSH term(s) COVID-19 ; Forecasting ; Humans ; SARS-CoV-2 ; Search Engine ; United States/epidemiology
    Language English
    Publishing date 2021-04-25
    Publishing country Switzerland
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 2175195-X
    ISSN 1660-4601 ; 1661-7827
    ISSN (online) 1660-4601
    ISSN 1661-7827
    DOI 10.3390/ijerph18094560
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Article ; Online: Associations Between Google Search Trends for Symptoms and COVID-19 Confirmed and Death Cases in the United States

    Abbas, Mostafa / Morland, Thomas B. / Hall, Eric S. / El-Manzalawy, Yasser

    medRxiv

    Abstract: We utilize functional data analysis techniques to investigate patterns of COVID-19 positivity and mortality in the US and their associations with Google search trends for COVID-19 related symptoms. Specifically, we represent state-level time series data ... ...

    Abstract We utilize functional data analysis techniques to investigate patterns of COVID-19 positivity and mortality in the US and their associations with Google search trends for COVID-19 related symptoms. Specifically, we represent state-level time series data for COVID-19 and Google search trends for symptoms as smoothed functional curves. Given these functional data, we explore the modes of variation in the data using functional principal component analysis (FPCA). We also apply functional clustering analysis to identify patterns of COVID-19 confirmed case and death trajectories across the US. Moreover, we quantify the associations between Google COVID-19 search trends for symptoms and COVID-19 confirmed case and death trajectories using dynamic correlation. Finally, we examine the dynamics of correlations for the top nine Google search trends of symptoms commonly associated with COVID-19 confirmed case and death trajectories. Our results reveal and characterize distinct patterns for COVID-19 spread and mortality across the US. The dynamics of these correlations suggest the feasibility of using Google queries to forecast COVID-19 cases and mortality for up to three weeks in advance. Our results and analysis framework set the stage for the development of predictive models for forecasting COVID-19 confirmed cases and deaths using historical data and Google search trends for nine symptoms associated with both outcomes.
    Keywords covid19
    Language English
    Publishing date 2021-02-24
    Publisher Cold Spring Harbor Laboratory Press
    Document type Article ; Online
    DOI 10.1101/2021.02.22.21252254
    Database COVID19

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  3. Article ; Online: OASIS +: leveraging machine learning to improve the prognostic accuracy of OASIS severity score for predicting in-hospital mortality.

    El-Manzalawy, Yasser / Abbas, Mostafa / Hoaglund, Ian / Cerna, Alvaro Ulloa / Morland, Thomas B / Haggerty, Christopher M / Hall, Eric S / Fornwalt, Brandon K

    BMC medical informatics and decision making

    2021  Volume 21, Issue 1, Page(s) 156

    Abstract: Background: Severity scores assess the acuity of critical illness by penalizing for the deviation of physiologic measurements from normal and aggregating these penalties (also called "weights" or "subscores") into a final score (or probability) for ... ...

    Abstract Background: Severity scores assess the acuity of critical illness by penalizing for the deviation of physiologic measurements from normal and aggregating these penalties (also called "weights" or "subscores") into a final score (or probability) for quantifying the severity of critical illness (or the likelihood of in-hospital mortality). Although these simple additive models are human readable and interpretable, their predictive performance needs to be further improved.
    Methods: We present OASIS +, a variant of the Oxford Acute Severity of Illness Score (OASIS) in which an ensemble of 200 decision trees is used to predict in-hospital mortality based on the 10 same clinical variables in OASIS.
    Results: Using a test set of 9566 admissions extracted from the MIMIC-III database, we show that OASIS + outperforms nine previously developed severity scoring methods (including OASIS) in predicting in-hospital mortality. Furthermore, our results show that the supervised learning algorithms considered in our experiments demonstrated higher predictive performance when trained using the observed clinical variables as opposed to OASIS subscores.
    Conclusions: Our results suggest that there is room for improving the prognostic accuracy of the OASIS severity scores by replacing the simple linear additive scoring function with more sophisticated non-linear machine learning models such as RF and XGB.
    MeSH term(s) Hospital Mortality ; Humans ; Intensive Care Units ; Machine Learning ; Prognosis ; Retrospective Studies
    Language English
    Publishing date 2021-05-13
    Publishing country England
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 2046490-3
    ISSN 1472-6947 ; 1472-6947
    ISSN (online) 1472-6947
    ISSN 1472-6947
    DOI 10.1186/s12911-021-01517-7
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  4. Article ; Online: Effect of a Financial Incentive for Colorectal Cancer Screening Adherence on the Appropriateness of Colonoscopy Orders.

    Morland, Thomas B / Synnestvedt, Marie / Honeywell, Steven / Yang, Feifei / Armstrong, Katrina / Guerra, Carmen

    American journal of medical quality : the official journal of the American College of Medical Quality

    2017  Volume 32, Issue 3, Page(s) 292–298

    Abstract: Performance incentives for preventive care may encourage inappropriate testing, such as cancer screening for patients with short life expectancies. Defining screening colonoscopies for patients with a >50% 4-year mortality risk as inappropriate, the ... ...

    Abstract Performance incentives for preventive care may encourage inappropriate testing, such as cancer screening for patients with short life expectancies. Defining screening colonoscopies for patients with a >50% 4-year mortality risk as inappropriate, the authors performed a pre-post analysis assessing the effect of introducing a cancer screening incentive on the proportion of screening colonoscopy orders that were inappropriate. Among 2078 orders placed by 23 attending physicians in 4 academic general internal medicine practices, only 0.6% (n = 6/1057) of screening colonoscopy orders in the preintervention period and 0.6% (n = 6/1021) of screening colonoscopy orders in the postintervention period were deemed "inappropriate." This study found no evidence that the incentive led to an increase in inappropriate screening colonoscopy orders.
    Language English
    Publishing date 2017-05
    Publishing country United States
    Document type Journal Article
    ZDB-ID 1131772-3
    ISSN 1555-824X ; 1062-8606
    ISSN (online) 1555-824X
    ISSN 1062-8606
    DOI 10.1177/1062860616646848
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    In stock of ZB MED Cologne/Königswinter

    Zs.A 2436: Show issues Location:
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  5. Article ; Online: An ECG-based machine learning model for predicting new-onset atrial fibrillation is superior to age and clinical features in identifying patients at high stroke risk.

    Raghunath, Sushravya / Pfeifer, John M / Kelsey, Christopher R / Nemani, Arun / Ruhl, Jeffrey A / Hartzel, Dustin N / Ulloa Cerna, Alvaro E / Jing, Linyuan / vanMaanen, David P / Leader, Joseph B / Schneider, Gargi / Morland, Thomas B / Chen, Ruijun / Zimmerman, Noah / Fornwalt, Brandon K / Haggerty, Christopher M

    Journal of electrocardiology

    2022  Volume 76, Page(s) 61–65

    Abstract: Background: Several large trials have employed age or clinical features to select patients for atrial fibrillation (AF) screening to reduce strokes. We hypothesized that a machine learning (ML) model trained to predict AF risk from 12‑lead ... ...

    Abstract Background: Several large trials have employed age or clinical features to select patients for atrial fibrillation (AF) screening to reduce strokes. We hypothesized that a machine learning (ML) model trained to predict AF risk from 12‑lead electrocardiogram (ECG) would be more efficient than criteria based on clinical variables in indicating a population for AF screening to potentially prevent AF-related stroke.
    Methods: We retrospectively included all patients with clinical encounters in Geisinger without a prior history of AF. Incidence of AF within 1 year and AF-related strokes within 3 years of the encounter were identified. AF-related stroke was defined as a stroke where AF was diagnosed at the time of stroke or within a year after the stroke. The efficiency of five methods was evaluated for selecting a cohort for AF screening. The methods were selected from four clinical trials (mSToPS, GUARD-AF, SCREEN-AF and STROKESTOP) and the ECG-based ML model. We simulated patient selection for the five methods between the years 2011 and 2014 and evaluated outcomes for 1 year intervals between 2012 and 2015, resulting in a total of twenty 1-year periods. Patients were considered eligible if they met the criteria before the start of the given 1-year period or within that period. The primary outcomes were numbers needed to screen (NNS) for AF and AF-associated stroke.
    Results: The clinical trial models indicated large proportions of the population with a prior ECG for AF screening (up to 31%), coinciding with NNS ranging from 14 to 18 for AF and 249-359 for AF-associated stroke. At comparable sensitivity, the ECG ML model indicated a modest number of patients for screening (14%) and had the highest efficiency in NNS for AF (7.3; up to 60% reduction) and AF-associated stroke (223; up to 38% reduction).
    Conclusions: An ECG-based ML risk prediction model is more efficient than contemporary AF-screening criteria based on age alone or age and clinical features at indicating a population for AF screening to potentially prevent AF-related strokes.
    MeSH term(s) Humans ; Atrial Fibrillation/complications ; Atrial Fibrillation/diagnosis ; Atrial Fibrillation/drug therapy ; Electrocardiography ; Retrospective Studies ; Mass Screening ; Stroke/diagnosis
    Language English
    Publishing date 2022-11-08
    Publishing country United States
    Document type Journal Article
    ZDB-ID 410286-1
    ISSN 1532-8430 ; 0022-0736
    ISSN (online) 1532-8430
    ISSN 0022-0736
    DOI 10.1016/j.jelectrocard.2022.11.001
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    In stock of ZB MED Cologne/Königswinter

    Zs.A 701: Show issues Location:
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  6. Article ; Online: A prospective study of loss of consciousness in epilepsy using virtual reality driving simulation and other video games.

    Yang, Li / Morland, Thomas B / Schmits, Kristen / Rawson, Elizabeth / Narasimhan, Poojitha / Motelow, Joshua E / Purcaro, Michael J / Peng, Kathy / Raouf, Saned / Desalvo, Matthew N / Oh, Taemin / Wilkerson, Jerome / Bod, Jessica / Srinivasan, Aditya / Kurashvili, Pimen / Anaya, Joseph / Manza, Peter / Danielson, Nathan / Ransom, Christopher B /
    Huh, Linda / Elrich, Susan / Padin-Rosado, Jose / Naidu, Yamini / Detyniecki, Kamil / Hamid, Hamada / Farooque, Pue / Astur, Robert / Xiao, Bo / Duckrow, Robert B / Blumenfeld, Hal

    Epilepsy & behavior : E&B

    2010  Volume 18, Issue 3, Page(s) 238–246

    Abstract: Patients with epilepsy are at risk of traffic accidents when they have seizures while driving. However, driving is an essential part of normal daily life in many communities, and depriving patients of driving privileges can have profound consequences for ...

    Abstract Patients with epilepsy are at risk of traffic accidents when they have seizures while driving. However, driving is an essential part of normal daily life in many communities, and depriving patients of driving privileges can have profound consequences for their economic and social well-being. In the current study, we collected ictal performance data from a driving simulator and two other video games in patients undergoing continuous video/EEG monitoring. We captured 22 seizures in 13 patients and found that driving impairment during seizures differed in terms of both magnitude and character, depending on the seizure type. Our study documents the feasibility of a prospective study of driving and other behaviors during seizures through the use of computer-based tasks. This methodology may be applied to further describe differential driving impairment in specific types of seizures and to gain data on anatomical networks disrupted in seizures that impair consciousness and driving safety.
    MeSH term(s) Adolescent ; Adult ; Automobile Driving ; Child ; Computer-Aided Design ; Disability Evaluation ; Electroencephalography/methods ; Epilepsy/classification ; Epilepsy/complications ; Epilepsy/rehabilitation ; Feasibility Studies ; Female ; Humans ; Male ; Middle Aged ; Prospective Studies ; Psychomotor Performance ; Unconsciousness/etiology ; Unconsciousness/rehabilitation ; User-Computer Interface ; Video Games ; Video Recording ; Young Adult
    Language English
    Publishing date 2010-06-10
    Publishing country United States
    Document type Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
    ZDB-ID 2010587-3
    ISSN 1525-5069 ; 1525-5050
    ISSN (online) 1525-5069
    ISSN 1525-5050
    DOI 10.1016/j.yebeh.2010.04.011
    Database MEDical Literature Analysis and Retrieval System OnLINE

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