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Big Data Analytics and AI in Mental Healthcare [article]

Ariel Rosenfeld, David Benrimoh, Caitrin Armstrong, Nykan Mirchi, Timothe Langlois-Therrien, Colleen Rollins, Myriam Tanguay-Sela, Joseph Mehltretter, Robert Fratila, Sonia Israel, Emily Snook, Kelly Perlman, Akiva Kleinerman (+4 others)
2019 arXiv   pre-print
Mental health conditions cause a great deal of distress or impairment; depression alone will affect 11% of the world's population. The application of Artificial Intelligence (AI) and big-data technologies to mental health has great potential for personalizing treatment selection, prognosticating, monitoring for relapse, detecting and helping to prevent mental health conditions before they reach clinical-level symptomatology, and even delivering some treatments. However, unlike similar
more » ... ns in other fields of medicine, there are several unique challenges in mental health applications which currently pose barriers towards the implementation of these technologies. Specifically, there are very few widely used or validated biomarkers in mental health, leading to a heavy reliance on patient and clinician derived questionnaire data as well as interpretation of new signals such as digital phenotyping. In addition, diagnosis also lacks the same objective 'gold standard' as in other conditions such as oncology, where clinicians and researchers can often rely on pathological analysis for confirmation of diagnosis. In this chapter we discuss the major opportunities, limitations and techniques used for improving mental healthcare through AI and big-data. We explore both the computational, clinical and ethical considerations and best practices as well as lay out the major researcher directions for the near future.
arXiv:1903.12071v1 fatcat:lxzhoy76qjaahezvq3344udn2q

Prediction of Suicidal Ideation in the Canadian Community Health Survey - Mental Health Component Using Deep Learning [article]

Sneha Desai, Myriam Tanguay-Sela, David Benrimoh, Robert Fratila, Eleanor Brown, Kelly Perlman, Ann John, Marcos DelPozo-Banos, Nancy Low, Sonia Israel, Lisa Palladini, Gustavo Turecki
2019 medRxiv   pre-print
Disclosure Myriam Tanguay-Sela and Sonia Israel are employees and shareholders of Aifred Health, a medical technology company that uses deep learning to increase treatment efficacy in psychiatry.  ... 
doi:10.1101/19010413 fatcat:e5lu6wrr5vbjdnormtjvmqzg7e

Identification of Suicidal Ideation in the Canadian Community Health Survey—Mental Health Component Using Deep Learning

Sneha Desai, Myriam Tanguay-Sela, David Benrimoh, Robert Fratila, Eleanor Brown, Kelly Perlman, Ann John, Marcos DelPozo-Banos, Nancy Low, Sonia Israel, Lisa Palladini, Gustavo Turecki
2021 Frontiers in Artificial Intelligence  
Suicidal ideation (SI) is prevalent in the general population, and is a risk factor for suicide. Predicting which patients are likely to have SI remains challenging. Deep Learning (DL) may be a useful tool in this context, as it can be used to find patterns in complex, heterogeneous, and incomplete datasets. An automated screening system for SI could help prompt clinicians to be more attentive to patients at risk for suicide.Methods: Using the Canadian Community Health Survey—Mental Health
more » ... nent, we trained a DL model based on 23,859 survey responses to classify patients with and without SI. Models were created to classify both lifetime SI and SI over the last 12 months. From 582 possible parameters we produced 96- and 21-feature versions of the models. Models were trained using an undersampling procedure that balanced the training set between SI and non-SI; validation was done on held-out data.Results: For lifetime SI, the 96 feature model had an Area under the receiver operating curve (AUC) of 0.79 and the 21 feature model had an AUC of 0.77. For SI in the last 12 months the 96 feature model had an AUC of 0.71 and the 21 feature model had an AUC of 0.68. In addition, sensitivity analyses demonstrated feature relationships in line with existing literature.Discussion: Although further study is required to ensure clinical relevance and sample generalizability, this study is an initial proof of concept for the use of DL to improve identification of SI. Sensitivity analyses can help improve the interpretability of DL models. This kind of model would help start conversations with patients which could lead to improved care and a reduction in suicidal behavior.
doi:10.3389/frai.2021.561528 pmid:34250463 pmcid:PMC8264793 fatcat:cmizx6ggefa2fo5ar76p6mjhve

Evaluating the Perceived Utility of an Artificial Intelligence-Powered Clinical Decision Support System for Depression Treatment Using a Simulation Centre [article]

Myriam Tanguay-Sela, David Benrimoh, Christina Popescu, Tamara Perez, Colleen Rollins, Emily Snook, Eryn Lundrigan, Caitrin Armstrong, Kelly Perlman, Robert Fratila, Joseph Mehltretter, Sonia Israel (+10 others)
2021 medRxiv   pre-print
AbstractAifred is a clinical decision support system (CDSS) that uses artificial intelligence to assist physicians in selecting treatments for major depressive disorder (MDD) by providing probabilities of remission for different treatment options based on patient characteristics. We evaluated the utility of the CDSS as perceived by physicians participating in simulated clinical interactions. Twenty psychiatry and family medicine staff and residents completed a study in which each physician had
more » ... hree 10-minute clinical interactions with standardized patients portraying mild, moderate, and severe episodes of MDD. During these scenarios, physicians were given access to the CDSS, which they could use in their treatment decisions. The perceived utility of the CDSS was assessed through self-report questionnaires, scenario observations, and interviews. 60% of physicians perceived the CDSS to be a useful tool in their treatment-selection process, with family physicians perceiving the greatest utility. Moreover, 50% of physicians would use the tool for all patients with depression, with an additional 35% noting they would reserve the tool for more severe or treatment-resistant patients. Furthermore, clinicians found the tool to be useful in discussing treatment options with patients. The efficacy of this CDSS and its potential to improve treatment outcomes must be further evaluated in clinical trials.
doi:10.1101/2021.04.21.21255899 fatcat:zuuyky752nbbxlyckgarz2ijqu

A Mixed-Methods Feasibility Study of a Novel AI-Enabled, Web-Based, Clinical Decision Support System for the Treatment of Major Depression in Adults [article]

Sabrina Qassim, Grace L Golden, Dominique Slowey, Mary Sarfas, Kate Whitmore, Tamara Perez, Elizabeth Strong, Eryn Lundrigan, Marie-Jeanne Fradette, Jacob Baxter, Bennet Desormeau, Myriam Tanguay-Sela (+16 others)
2022 medRxiv   pre-print
., 2020; Tanguay-Sela et al., 2021) . The current paper reports on the results of an in-clinic Feasibility study of the Aifred CDSS.  ...  both patients and physicians found the tool easy to use and feasible in a clinical setting, building on the results from our previous simulation-center based ease-of-use study (Benrimoh et al., 2021; Tanguay-Sela  ... 
doi:10.1101/2022.01.14.22269265 fatcat:z6ivhtleerfhvdqjv6bwa4lpem

Using a Simulation Centre to Evaluate the Effect of an Artificial Intelligence-Powered Clinical Decision Support System for Depression Treatment on the Physician-Patient Interaction [article]

David Benrimoh, Myriam Tanguay-Sela, Kelly Perlman, Sonia Israel, Joseph Mehltretter, Caitrin Armstrong, Robert Fratila, Sagar V. Parikh, Jordan F. Karp, Katherine Heller, Ipsit V. Vahia, Daniel M. Blumberger (+19 others)
2020 medRxiv   pre-print
Objective: Aifred is an artificial intelligence (AI)-powered clinical decision support system (CDSS) for the treatment of major depression. Here, we explore use of a simulation centre environment in evaluating the usability of Aifred, particularly its impact on the physician-patient interaction. Methods: Twenty psychiatry and family medicine attending staff and residents were recruited to complete a 2.5-hour study at a clinical interaction simulation centre with standardized patients. Each
more » ... cian had the option of using the CDSS to inform their treatment choice in three 10-minute clinical scenarios with standardized patients portraying mild, moderate, and severe episodes of major depression. Feasibility and acceptability data were collected through self-report questionnaires, scenario observations, interviews, and standardized patient feedback. Results: All twenty participants completed the study. Initial results indicate that the tool was acceptable to clinicians and feasible for use during clinical encounters. Clinicians indicated a willingness to use the tool in real clinical practice, a significant degree of trust in the AI's predictions to assist with treatment selection, and reported that the tool helped increase patient understanding of and trust in treatment. The simulation environment allowed for the evaluation of the tool's impact on the physician-patient interaction. Conclusions: The simulation centre allowed for direct observations of clinician use and impact of the tool on the clinician-patient interaction prior to clinical studies. It may therefore offer a useful and important environment in the early testing of new technological tools. The present results will inform further tool development and clinician training materials.
doi:10.1101/2020.03.20.20039255 fatcat:y5fq2ol3v5gjrpem6vs4432oru

Using a simulation centre to evaluate preliminary acceptability and impact of an artificial intelligence-powered clinical decision support system for depression treatment on the physician-patient interaction

David Benrimoh, Myriam Tanguay-Sela, Kelly Perlman, Sonia Israel, Joseph Mehltretter, Caitrin Armstrong, Robert Fratila, Sagar V Parikh, Jordan F Karp, Katherine Heller, Ipsit V Vahia, Daniel M Blumberger (+19 others)
2021 BJPsych Open  
Recently, artificial intelligence-powered devices have been put forward as potentially powerful tools for the improvement of mental healthcare. An important question is how these devices impact the physician-patient interaction. Aifred is an artificial intelligence-powered clinical decision support system (CDSS) for the treatment of major depression. Here, we explore the use of a simulation centre environment in evaluating the usability of Aifred, particularly its impact on the
more » ... interaction. Twenty psychiatry and family medicine attending staff and residents were recruited to complete a 2.5-h study at a clinical interaction simulation centre with standardised patients. Each physician had the option of using the CDSS to inform their treatment choice in three 10-min clinical scenarios with standardised patients portraying mild, moderate and severe episodes of major depression. Feasibility and acceptability data were collected through self-report questionnaires, scenario observations, interviews and standardised patient feedback. All 20 participants completed the study. Initial results indicate that the tool was acceptable to clinicians and feasible for use during clinical encounters. Clinicians indicated a willingness to use the tool in real clinical practice, a significant degree of trust in the system's predictions to assist with treatment selection, and reported that the tool helped increase patient understanding of and trust in treatment. The simulation environment allowed for the evaluation of the tool's impact on the physician-patient interaction. The simulation centre allowed for direct observations of clinician use and impact of the tool on the clinician-patient interaction before clinical studies. It may therefore offer a useful and important environment in the early testing of new technological tools. The present results will inform further tool development and clinician training materials.
doi:10.1192/bjo.2020.127 pmid:33403948 pmcid:PMC8058891 fatcat:l5uwjqp7pvhs3ese5w34sw4ghq

Evaluating the Clinical Feasibility of an Artificial Intelligence-Powered, Web-Based Clinical Decision Support System for the Treatment of Depression in Adults: Longitudinal Feasibility Study

Christina Popescu, Grace Golden, David Benrimoh, Myriam Tanguay-Sela, Dominique Slowey, Eryn Lundrigan, Jérôme Williams, Bennet Desormeau, Divyesh Kardani, Tamara Perez, Colleen Rollins, Sonia Israel (+20 others)
2022
BACKGROUND: Approximately two-thirds of patients with major depressive disorder do not achieve remission during their first treatment. There has been increasing interest in the use of digital, artificial intelligence-powered clinical decision support systems (CDSSs) to assist physicians in their treatment selection and management, improving the personalization and use of best practices such as measurement-based care. Previous literature shows that for digital mental health tools to be
more » ... , the tool must be easy for patients and physicians to use and feasible within existing clinical workflows. OBJECTIVE: This study aims to examine the feasibility of an artificial intelligence-powered CDSS, which combines the operationalized 2016 Canadian Network for Mood and Anxiety Treatments guidelines with a neural network-based individualized treatment remission prediction. METHODS: Owing to the COVID-19 pandemic, the study was adapted to be completed entirely remotely. A total of 7 physicians recruited outpatients diagnosed with major depressive disorder according to the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition criteria. Patients completed a minimum of one visit without the CDSS (baseline) and 2 subsequent visits where the CDSS was used by the physician (visits 1 and 2). The primary outcome of interest was change in appointment length after the introduction of the CDSS as a proxy for feasibility. Feasibility and acceptability data were collected through self-report questionnaires and semistructured interviews. RESULTS: Data were collected between January and November 2020. A total of 17 patients were enrolled in the study; of the 17 patients, 14 (82%) completed the study. There was no significant difference in appointment length between visits (introduction of the tool did not increase appointment length; F2,24=0.805; mean squared error 58.08; P=.46). In total, 92% (12/13) of patients and 71% (5/7) of physicians felt that the tool was easy to use; 62% (8/13) of patients and 71% (5/7) of phy [...]
doi:10.17863/cam.79604 fatcat:ypo4xmznazhulen2lzi6e662ia