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Legends: Folklore on Reddit [article]

Caitrin Armstrong, Derek Ruths
2020 arXiv   pre-print
In this paper we introduce Reddit legends, a collection of venerated old posts that have become famous on Reddit. To establish the utility of Reddit legends for both computational science/HCI and folkloristics, we investigate two main questions: (1) whether they can be considered folklore, i.e. if they have consistent form, cultural significance, and undergo spontaneous transmission, and (2) whether they can be studied in a systematic manner. Through several subtasks, including the creation of
more » ... typology, an analysis of references to Reddit legends, and an examination of some of the textual characteristics of referencing behaviour, we show that Reddit legends can indeed be considered as folklore and that they are amendable to systematic text-based approaches. We discuss how these results will enable future analyses of folklore on Reddit, including tracking subreddit-wide and individual-user behaviour, and the relationship of this behaviour to other cultural markers.
arXiv:2007.00750v1 fatcat:i52nyhe7bvgq7brbfe7uyqq5ka

The Residence History Inference Problem [article]

Derek Ruths, Caitrin Armstrong
2020 arXiv   pre-print
The use of online user traces for studies of human mobility has received significant attention in recent years. This growing body of work, and the more general importance of human migration patterns to government and industry, motivates the need for a formalized approach to the computational modeling of human mobility - in particular how and when individuals change their place of residence - from online traces. Prior work on this topic has skirted the underlying computational modeling of
more » ... ce inference, focusing on migration patterns themselves. As a result, to our knowledge, all prior work has employed heuristics to compute something like residence histories. Here, we formalize the residence assignment problem, which seeks, under constraints associated with the minimum length-of-stay at a residence, the most parsimonious sequence of residence periods and places that explains the movement history of an individual. Here we provide an exact solution for this problem and establish its algorithmic complexity. Because the calculation of optimal residence histories (under the assumptions of the model) is tractable, we believe that this method will be a valuable tool for future work on this topic.
arXiv:2003.04155v1 fatcat:ve6pjvgex5ahtbl3k7w6onpz3q

Challenges when identifying migration from geo-located Twitter data

Caitrin Armstrong, Ate Poorthuis, Matthew Zook, Derek Ruths, Thomas Soehl
2021 EPJ Data Science  
AbstractGiven the challenges in collecting up-to-date, comparable data on migrant populations the potential of digital trace data to study migration and migrants has sparked considerable interest among researchers and policy makers. In this paper we assess the reliability of one such data source that is heavily used within the research community: geolocated tweets. We assess strategies used in previous work to identify migrants based on their geolocation histories. We apply these approaches to
more » ... nfer the travel history of a set of Twitter users who regularly posted geolocated tweets between July 2012 and June 2015. In a second step we hand-code the entire tweet histories of a subset of the accounts identified as migrants by these methods. Upon close inspection very few of the accounts that are classified as migrants appear to be migrants in any conventional sense or international students. Rather we find these approaches identify other highly mobile populations such as frequent business or leisure travellers, or people who might best be described as "transnationals". For demographic research that draws on this kind of data to generate estimates of migration flows this high mis-classification rate implies that findings are likely sensitive to the adjustment model used. For most research trying to use these data to study migrant populations, the data will be of limited utility. We suspect that increasing the correct classification rate substantially will not be easy and may introduce other biases.
doi:10.1140/epjds/s13688-020-00254-7 fatcat:znvnlsvltfha5c6eawbgnthktq

Editorial: ML and AI Safety, Effectiveness and Explainability in Healthcare

David Benrimoh, Sonia Israel, Robert Fratila, Caitrin Armstrong, Kelly Perlman, Ariel Rosenfeld, Adam Kapelner
2021 Frontiers in Big Data  
The increasing performance of machine learning and artificial intelligence (ML/AI) models has led to them being encountered more frequently in daily life, including in clinical medicine (Bruckert et al.; Rosenfeld et al., 2021) . While concerns about the opaque "black box" nature of ML/AI tools are not new, the need for practical solutions to the interpretability problem has become more pressing as ML/AI devices move from the laboratory, through regulatory processes that have yet to fully catch
more » ... up to the state-of-the-art (Benrimoh et al., 2018a) , and to the bedside. This special edition targets three key domains in which innovation and clearer best practices are required for the implementation of ML/AI approaches in healthcare: ensuring safety, demonstrating effectiveness, and providing explainability. Notably, the first two have long been staples in the evaluation of drugs and medical devices (i.e., in order to be approved for human use, products must prove that they are safe and effective-often compared to a reasonable comparator) (Spławiński and Kuźniar, 2004) . The third requirement-that of explainability-appears to be unique to ML/AI, due to the challenge of explaining how models arrive at their increasingly accurate conclusions. Yet, upon closer examination, one might argue that the explainability criterion has been implied in the past: mechanisms of action of drugs and devices are generally described in their product documentation (Health Canada, 2014). However, this can be misleading. For instance, many drugs have known receptor binding profiles and putative mechanisms of actions, although the precise mechanisms by which they produce their effect remain unclear despite their widespread use in clinical practice. Prime examples of this are lithium (Shaldubina et al., 2001) and electroconvulsive therapy (Scott, 2011), both longstanding and highly effective treatments whose mechanisms of action remain controversial. Indeed, even the precise mechanism of general anesthesia is a subject of debate (Pleuvry, 2008) . As such, we must consider a compromise-that of sufficient explainability (Clarke and Kapelner). This involves answering the question: how much must we know about a model in order to determine that it is safe to use in clinical practice? The articles in this special edition begin to explore possible answers to this as well as other key questions in the application of ML/AI to healthcare contexts. Bruckert et al. propose a Comprehensible Artificial Intelligence (cAI) framework, which they describe as a "cookbook" approach for integrating explainability into ML/AI systems intended to support medical decision-making. Notably, the authors do not limit explainability to an understanding of general rules a model might use to make predictions, but rather extend it to an example-level approach where human-interpretable semantic information is passed from the
doi:10.3389/fdata.2021.727856 fatcat:cdatocpgcjf65gatqjicawmaui

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

Treatment selection using prototyping in latent-space with application to depression treatment

Akiva Kleinerman, Ariel Rosenfeld, David Benrimoh, Robert Fratila, Caitrin Armstrong, Joseph Mehltretter, Eliyahu Shneider, Amit Yaniv-Rosenfeld, Jordan Karp, Charles F. Reynolds, Gustavo Turecki, Adam Kapelner (+1 others)
2021 PLoS ONE  
Investigation: Akiva Kleinerman, David Benrimoh, Robert Fratila, Caitrin Armstrong, Joseph Mehltretter, Eliyahu Shneider, Gustavo Turecki, Adam Kapelner.  ...  Methodology: Akiva Kleinerman, David Benrimoh, Robert Fratila, Caitrin Armstrong, Amit Yaniv-Rosenfeld. Project administration: Ariel Rosenfeld. Resources: Akiva Kleinerman, Ariel Rosenfeld.  ... 
doi:10.1371/journal.pone.0258400 pmid:34767577 pmcid:PMC8589171 fatcat:ifhvdy5xhranhpcoee6uzakvji

Differential Treatment Benefit Prediction for Treatment Selection in Depression: A Deep Learning Analysis of STAR*D and CO-MED Data

Joseph Mehltretter, Robert Fratila, David Benrimoh, Adam Kapelner, Kelly Perlman, Emily Snook, Sonia Israel, Caitrin Armstrong, Marc Miresco, Gustavo Turecki
2020 Computational Psychiatry  
Depression affects one in nine people, but treatment response rates remain low. There is significant potential in the use of computational modeling techniques to predict individual patient responses and thus provide more personalized treatment. Deep learning is a promising computational technique that can be used for differential treatment selection based on predicted remission probability. Using Sequenced Treatment Alternatives to Relieve Depression (STAR*D) and Combining Medications to
more » ... Depression Outcomes (CO-MED) trial data, we employed deep neural networks to predict remission after feature selection. Treatments included were citalopram, escitalopram, bupropion SR plus escitalopram, and venlafaxine plus mirtazapine. Differential treatment benefit was estimated in terms of improvement of population remission rates after application of the model for treatment selection using two approaches: (1) using predictions generated directly from the model (the predicted improvement approach) and (2) using bootstrapping for sample generation and then estimating population remission rate for patients who actually received the drug predicted by the model compared to the general population (the actual improvement approach). Our deep learning model predicted remission in a pooled CO-MED/STAR*D dataset (including four treatments) with an area under the curve of 0.69 using 17 input features. Our actual improvement analysis showed a statistically significant 2.48% absolute improvement (corresponding to a 7.2% relative improvement) in population remission rate (p = 0.01, CI 2.48% ± 0.5%). Our model serves as proof-of-concept that deep learning approaches, with further refinement and work to address concerns about differences between studies when multiple datasets are used for training, may have utility in differential prediction of antidepressant response when selecting from a number of treatment options.
doi:10.1162/cpsy_a_00029 fatcat:x6q7e3t2tzhtjihis7xslte2ly

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
PCPs have been found to perceive the treatment of patients with depression as challenging, feeling that these patients place a high demand on their psychological resources (McPherson and Armstrong, 2012  ... 
doi:10.1101/2021.04.21.21255899 fatcat:zuuyky752nbbxlyckgarz2ijqu

Genetic Contributors of Efficacy and Adverse Metabolic Effects of Chlorthalidone in African Americans from the Genetics of Hypertension Associated Treatments (GenHAT) Study

Nicole D. Armstrong, Vinodh Srinivasasainagendra, Lakshmi Manasa S. Chekka, Nam H. K. Nguyen, Noor A. Nahid, Alana C. Jones, Rikki M. Tanner, Bertha A. Hidalgo, Nita A. Limdi, Steven A. Claas, Yan Gong, Caitrin W. McDonough (+5 others)
2022 Genes  
Hypertension is a leading risk factor for cardiovascular disease mortality. African Americans (AAs) have the highest prevalence of hypertension in the United States, and to alleviate the burden of hypertension in this population, better control of blood pressure (BP) is needed. Previous studies have shown considerable interpersonal differences in BP response to antihypertensive treatment, suggesting a genetic component. Utilizing data from 4297 AA participants randomized to chlorthalidone from
more » ... he Genetics of Hypertension Associated Treatments (GenHAT) study, we aimed to identify variants associated with the efficacy of chlorthalidone. An additional aim was to find variants that contributed to changes in fasting glucose (FG) in these individuals. We performed genome-wide association analyses on the change of systolic and diastolic BP (SBP and DBP) over six months and FG levels over 24 months of treatment. We sought replication in the International Consortia of Pharmacogenomics Studies. We identified eight variants statistically associated with BP response and nine variants associated with FG response. One suggestive LINC02211-CDH9 intergenic variant was marginally replicated with the same direction of effect. Given the impact of hypertension in AAs, this study implies that understanding the genetic background for BP control and glucose changes during chlorthalidone treatment may help prevent adverse cardiovascular events in this population.
doi:10.3390/genes13071260 pmid:35886043 pmcid:PMC9319619 fatcat:nyzfg7e435bsxb4mucng3t3gem

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
The objective of this paper is to discuss perceived clinical utility and impact on physician-patient relationship of a novel, artificial-intelligence (AI) enabled clinical decision support system (CDSS) for use in the treatment of adults with major depression. Patients had a baseline appointment, followed by a minimum of two appointments with the CDSS. For both physicians and patients, study exit questionnaires and interviews were conducted to assess perceived clinical utility, impact on
more » ... -physician relationship, and understanding and trust in the CDSS. 17 patients consented to participate in the study, of which 14 completed. 86% of physicians (6/7) felt the information provided by the CDSS provided a more comprehensive understanding of patient situations and 71% (5/7) felt the information was helpful. 86% of physicians (6/7) reported the AI/predictive model was useful when making treatment decisions. 62% of patients (8/13) reported improvement in their care as a result of the tool. 46% of patients (6/13) felt the app significantly or somewhat improved their relationship with their physicians; 54% felt it did not change. 71% of physicians (5/7) and 62% of patients (8/13) rated they trusted the tool. Qualitative results are analyzed and presented. Findings suggest physicians perceived the tool as useful in conducting appointments and used it while making treatment decisions. Physicians and patients generally found the tool trustworthy, and it may have positive effects on physician-patient relationships.
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

Page 5317 of Psychological Abstracts Vol. 81, Issue 11 [page]

1994 Psychological Abstracts  
, Hubert E., 39599, 42217 Armstrong, Robert W., 41666 Armstrong, Thomas J., 43352 Arnett, Amy A., 42784 Arnold, Robert M., 42994 Arnow, Bruce, 42039 Arnt, J., 40450 Aro, Hillevi M., 41460 Arom, Simha,  ...  ., 40929 Anderson, Britt, 39963 Anderson, Caitrin-Jane, 40165 Anderson, Gerard F., 42040 Anderson, Geri, 41343 Anderson, Harlene, 42221 Anderson, Hugh P., 43187 Anderson, Janis L., 41247 Anderson, Jennifer  ... 

Genetic Contributions to Early and Late Onset Ischemic Stroke [article]

Thomas Jaworek, Huichun Xu, Brady Gaynor, John Cole, Kristiina Rannikmae, Tara Stanne, Liisa Tomppo, Vida Abedi, Philippe Amouyel, Nicole Armstrong, John Attia, Steven Bell (+105 others)
2021 medRxiv   pre-print
Objective: To determine the contribution of common genetic variants to risk of early onset ischemic stroke (IS). Methods: We performed a meta-analysis of genome-wide association studies of early onset IS, ages 18-59, using individual level data or summary statistics in 16,927 cases and 576,353 non-stroke controls from 48 different studies across North America, Europe, and Asia. We further compared effect sizes at our most genome-wide significant loci between early and late onset IS and compared
more » ... polygenic risk scores for venous thromboembolism between early versus later onset IS. Results: We observed an association between early onset IS and ABO, a known stroke locus. The effect size of the peak ABO SNP, rs8176685, was significantly larger in early compared to late onset IS (OR 1.17 (95% C.I.: 1.11-1.22) vs 1.05 (0.99-1.12); p for interaction = 0.008). Analysis of genetically determined ABO blood groups revealed that early onset IS cases were more likely to have blood group A and less likely to have blood group O compared to both non-stroke controls and to late onset IS cases. Using polygenic risk scores, we observed that greater genetic risk for venous thromboembolism, another prothrombotic condition, was more strongly associated with early, compared to late, onset IS (p=0.008). Conclusion: The ABO locus, genetically predicted blood group A, and higher genetic propensity for venous thrombosis are more strongly associated with early onset IS, compared with late onset IS, supporting a stronger role of prothrombotic factors in early onset IS.
doi:10.1101/2021.11.06.21265795 fatcat:f4xmduzqj5hm5lsqm2j4genk2e

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
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