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  1. Article ; Online: Bridging evidence-to-care gaps with mHealth: Designing a symptom checker for parents accessing knowledge translation resources on acute children's illnesses in a smartphone application.

    Benoit, James R A / Hartling, Lisa / Scott, Shannon D

    PEC innovation

    2023  Volume 2, Page(s) 100152

    Abstract: Background: Smartphone applications offer a novel platform for delivering health information to parents. This study created and evaluated an app-based symptom checker that recommends educational tools to parents based on their child's symptoms.: ... ...

    Abstract Background: Smartphone applications offer a novel platform for delivering health information to parents. This study created and evaluated an app-based symptom checker that recommends educational tools to parents based on their child's symptoms.
    Methods: Symptoms extracted from 23 knowledge translation (KT) tools for 10 children's illnesses comprised a set of plain-language symptoms. The symptom checker works by producing confusion matrices evaluating a child's reported symptoms against possible illnesses, comparing precision scores to examine how well each illness matched reported symptoms, and ordering possible illnesses by performance score. Performance was evaluated by extracting symptoms from 8 clinical vignettes, and examining correct first-try matches.
    Results: We created a final list of 54 plain-language symptoms. Visualizations of the symptom set creation process and logic mapping are presented, as well as images of the working symptom checker. The symptom checker matched 100% (8/8) of tested clinical vignettes to the appropriate illness resource.
    Discussion: Symptom checkers are a potentially useful tool to integrate into apps that parents use for their children's health. The design of these systems has the potential to change parents' relationship with technology, affecting both their adoption and acceptance of symptom checkers. Our design choices contribute to addressing current barriers to the adoption of symptom checkers, reducing functional, critical, and interactive literacy requirements for parents.
    Language English
    Publishing date 2023-04-01
    Publishing country Netherlands
    Document type Journal Article
    ISSN 2772-6282
    ISSN (online) 2772-6282
    DOI 10.1016/j.pecinn.2023.100152
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Article ; Online: Using Machine Learning to Predict Remission in Patients With Major Depressive Disorder Treated With Desvenlafaxine.

    Benoit, James R A / Dursun, Serdar M / Greiner, Russell / Cao, Bo / Brown, Matthew R G / Lam, Raymond W / Greenshaw, Andrew J

    Canadian journal of psychiatry. Revue canadienne de psychiatrie

    2021  Volume 67, Issue 1, Page(s) 39–47

    Abstract: Background: Major depressive disorder (MDD) is a common and burdensome condition that has low rates of treatment success for each individual treatment. This means that many patients require several medication switches to achieve remission; selecting an ... ...

    Abstract Background: Major depressive disorder (MDD) is a common and burdensome condition that has low rates of treatment success for each individual treatment. This means that many patients require several medication switches to achieve remission; selecting an effective antidepressant is typically a sequential trial-and-error process. Machine learning techniques may be able to learn models that can predict whether a specific patient will respond to a given treatment, before it is administered. This study uses baseline clinical data to create a machine-learned model that accurately predicts remission status for a patient after desvenlafaxine (DVS) treatment.
    Methods: We applied machine learning algorithms to data from 3,399 MDD patients (90% of the 3,776 subjects in 11 phase-III/IV clinical trials, each described using 92 features), to produce a model that uses 26 of these features to predict symptom remission, defined as an 8-week Hamilton Depression Rating Scale score of 7 or below. We evaluated that learned model on the remaining held-out 10% of the data (
    Results: Our resulting classifier, a trained linear support vector machine, had a holdout set accuracy of 69.0%, significantly greater than the probability of classifying a patient correctly by chance. We demonstrate that this learning process is stable by repeatedly sampling part of the training dataset and running the learner on this sample, then evaluating the learned model on the held-out instances of the training set; these runs had an average accuracy of 67.0% ± 1.8%.
    Conclusions: Our model, based on 26 clinical features, proved sufficient to predict DVS remission significantly better than chance. This may allow more accurate use of DVS without waiting 8 weeks to determine treatment outcome, and may serve as a first step toward changing psychiatric care by incorporating clinical assistive technologies using machine-learned models.
    MeSH term(s) Antidepressive Agents/therapeutic use ; Depressive Disorder, Major/diagnosis ; Desvenlafaxine Succinate/therapeutic use ; Humans ; Machine Learning ; Treatment Outcome
    Chemical Substances Antidepressive Agents ; Desvenlafaxine Succinate (ZB22ENF0XR)
    Language English
    Publishing date 2021-08-11
    Publishing country United States
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 304227-3
    ISSN 1497-0015 ; 0008-4824 ; 0706-7437
    ISSN (online) 1497-0015
    ISSN 0008-4824 ; 0706-7437
    DOI 10.1177/07067437211037141
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  3. Article: fMRI investigation of response inhibition, emotion, impulsivity, and clinical high-risk behavior in adolescents.

    Brown, Matthew R G / Benoit, James R A / Juhás, Michal / Dametto, Ericson / Tse, Tiffanie T / MacKay, Marnie / Sen, Bhaskar / Carroll, Alan M / Hodlevskyy, Oleksandr / Silverstone, Peter H / Dolcos, Florin / Dursun, Serdar M / Greenshaw, Andrew J

    Frontiers in systems neuroscience

    2015  Volume 9, Page(s) 124

    Abstract: High-risk behavior in adolescents is associated with injury, mental health problems, and poor outcomes in later life. Improved understanding of the neurobiology of high-risk behavior and impulsivity shows promise for informing clinical treatment and ... ...

    Abstract High-risk behavior in adolescents is associated with injury, mental health problems, and poor outcomes in later life. Improved understanding of the neurobiology of high-risk behavior and impulsivity shows promise for informing clinical treatment and prevention as well as policy to better address high-risk behavior. We recruited 21 adolescents (age 14-17) with a wide range of high-risk behavior tendencies, including medically high-risk participants recruited from psychiatric clinics. Risk tendencies were assessed using the Adolescent Risk Behavior Screen (ARBS). ARBS risk scores correlated highly (0.78) with impulsivity scores from the Barratt Impulsivity scale (BIS). Participants underwent 4.7 Tesla functional magnetic resonance imaging (fMRI) while performing an emotional Go/NoGo task. This task presented an aversive or neutral distractor image simultaneously with each Go or NoGo stimulus. Risk behavior and impulsivity tendencies exhibited similar but not identical associations with fMRI activation patterns in prefrontal brain regions. We interpret these results as reflecting differences in response inhibition, emotional stimulus processing, and emotion regulation in relation to participant risk behavior tendencies and impulsivity levels. The results are consistent with high impulsivity playing an important role in determining high risk tendencies in this sample containing clinically high-risk adolescents.
    Language English
    Publishing date 2015-09-29
    Publishing country Switzerland
    Document type Journal Article
    ZDB-ID 2453005-0
    ISSN 1662-5137
    ISSN 1662-5137
    DOI 10.3389/fnsys.2015.00124
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  4. Article: Neural correlates of high-risk behavior tendencies and impulsivity in an emotional Go/NoGo fMRI task.

    Brown, Matthew R G / Benoit, James R A / Juhás, Michal / Lebel, R M / MacKay, Marnie / Dametto, Ericson / Silverstone, Peter H / Dolcos, Florin / Dursun, Serdar M / Greenshaw, Andrew J

    Frontiers in systems neuroscience

    2015  Volume 9, Page(s) 24

    Abstract: Improved neuroscientific understanding of high-risk behaviors such as alcohol binging, drug use, and unsafe sex will lead to therapeutic advances for high-risk groups. High-risk behavior often occurs in an emotionally-charged context, and behavioral ... ...

    Abstract Improved neuroscientific understanding of high-risk behaviors such as alcohol binging, drug use, and unsafe sex will lead to therapeutic advances for high-risk groups. High-risk behavior often occurs in an emotionally-charged context, and behavioral inhibition and emotion regulation play important roles in risk-related decision making. High impulsivity is an important potential contributor to high-risk behavior tendencies. We explored the relationships between high-risk behavior tendencies, impulsivity, and fMRI brain activations in an emotional Go/NoGo task. This task presented emotional distractor pictures (aversive vs. neutral) simultaneously with Go/NoGo stimuli (square vs. circle) that required a button press or withholding of the press, respectively. Participants' risk behavior tendencies were assessed with the Cognitive Appraisal of Risky Events (CARE) scale. The Barratt Impulsivity Scale 11 (BIS) was used to assess participant impulsivity. Individuals with higher CARE risk scores exhibited reduced activation related to response inhibition (NoGo-Go) in right orbital frontal cortex (OFC) and ventromedial prefrontal cortex. These regions did not show a significant relationship with impulsivity scores. Conversely, more impulsive individuals showed reduced emotion-related activity (aversive-neutral distractors) in dorsomedial prefrontal cortex, perigenual anterior cingulate cortex, and right posterior OFC. There were distinct neural correlates of high-risk behavior tendency and impulsivity in terms of brain activity in the emotional Go/NoGo task. This dissociation supports the conception of high-risk behavior tendency as a distinct construct from that of impulsivity. Our results suggest that treatment for high-risk behavior may be more effective with a nuanced approach that does not conflate high impulsivity necessarily with high-risk behavior tendencies.
    Language English
    Publishing date 2015-03-10
    Publishing country Switzerland
    Document type Journal Article
    ZDB-ID 2453005-0
    ISSN 1662-5137
    ISSN 1662-5137
    DOI 10.3389/fnsys.2015.00024
    Database MEDical Literature Analysis and Retrieval System OnLINE

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