Publications

Social isolation is linked to classical risk factors of Alzheimer’s disease-related dementias
Kimia Shafighi
Sylvia Villeneuve
P. Rosa-Neto
AmanPreet Badhwar
Judes Poirier
Vaibhav Sharma
Yasser Iturria-Medina
Patricia P. Silveira
Laurette Dubé
David C. Glahn
Danilo Bzdok
Alzheimer’s disease and related dementias is a major public health burden – compounding over upcoming years due to longevity. Recently, … (voir plus)clinical evidence hinted at the experience of social isolation in expediting dementia onset. In 502,506 UK Biobank participants and 30,097 participants from the Canadian Longitudinal Study of Aging, we revisited traditional risk factors for developing dementia in the context of loneliness and lacking social support. Across these measures of subjective and objective social deprivation, we have identified strong links between individuals’ social capital and various indicators of Alzheimer’s disease and related dementias risk, which replicated across both population cohorts. The quality and quantity of daily social encounters had deep connections with key aetiopathological factors, which represent 1) personal habits and lifestyle factors, 2) physical health, 3) mental health, and 4) societal and external factors. Our population-scale assessment suggest that social lifestyle determinants are linked to most neurodegeneration risk factors, highlighting them promising targets for preventive clinical action.
2021-09-15
bioRxiv (preprint)
doi.org
On the estimation of discrete choice models to capture irrational customer behaviors
Sanjay Dominik Jena
Andrea Lodi
Claudio Sole
The random utility maximization model is by far the most adopted framework to estimate consumer choice behavior. However, behavioral economi… (voir plus)cs has provided strong empirical evidence of irrational choice behaviors, such as halo effects, that are incompatible with this framework. Models belonging to the random utility maximization family may therefore not accurately capture such irrational behavior. Hence, more general choice models, overcoming such limitations, have been proposed. However, the flexibility of such models comes at the price of increased risk of overfitting. As such, estimating such models remains a challenge. In this work, we propose an estimation method for the recently proposed generalized stochastic preference choice model, which subsumes the family of random utility maximization models and is capable of capturing halo effects. In particular, we propose a column-generation method to gradually refine the discrete choice model based on partially ranked preference sequences. Extensive computational experiments indicate that our model, explicitly accounting for irrational preferences, can significantly boost the predictive accuracy on both synthetic and real-world data instances. Summary of Contribution: In this work, we propose an estimation method for the recently proposed generalized stochastic preference choice model, which subsumes the family of random utility maximization models and is capable of capturing halo effects. Specifically, we show how to use partially ranked preferences to efficiently model rational and irrational customer types from transaction data. Our estimation procedure is based on column generation, where relevant customer types are efficiently extracted by expanding a treelike data structure containing the customer behaviors. Furthermore, we propose a new dominance rule among customer types whose effect is to prioritize low orders of interactions among products. An extensive set of experiments assesses the predictive accuracy of the proposed approach by comparing it against rank-based methods with only rational preferences and with more general benchmarks from the literature. Our results show that accounting for irrational preferences can boost predictive accuracy by 12.5% on average when tested on a real-world data set from a large chain of grocery and drug stores.
2021-09-08
ArXiv (preprint)
doi.org
arxiv.org
Rapid simultaneous acquisition of macromolecular tissue volume, susceptibility, and relaxometry maps
Fang Frank Yu
Susie Yi Huang
Thomas Witzel
Ashwin Kumar
Congyu Liao
Tanguy Duval
Julien Cohen-Adad
Berkin Bilgic
Purpose A major obstacle to the clinical implementation of quantitative MR is the lengthy acquisition time required to derive multi-contrast… (voir plus) parametric maps. We sought to reduce the acquisition time for quantitative susceptibility mapping (QSM) and macromolecular tissue volume (MTV) by acquiring both contrasts simultaneously by leveraging their redundancies. The Joint Virtual Coil concept with generalized autocalibrating partially parallel acquisitions (JVC-GRAPPA) was applied to reduce acquisition time further. Methods Three adult volunteers were imaged on a 3T scanner using a multi-echo 3D GRE sequence acquired at three head orientations. MTV, QSM, R2*, T1, and proton density maps were reconstructed. The same sequence (GRAPPA R=4) was performed in subject #1 with a single head orientation for comparison. Fully sampled data was acquired in subject #2, from which retrospective undersampling was performed (R=6 GRAPPA and R=9 JVC-GRAPPA). Prospective undersampling was performed in subject #3 (R=6 GRAPPA and R=9 JVC-GRAPPA) using gradient blips to shift k-space sampling in later echoes. Results Subject #1’s multi-orientation and single-orientation MTV maps were not significantly different based on RMSE. For subject #2, the retrospectively undersampled JVC-GRAPPA and GRAPPA generated similar results as fully sampled data. This approach was validated with the prospectively undersampled images in subject #3. Using QSM, R2*, and MTV, the contributions of myelin and iron content to susceptibility was estimated. Conclusion We have developed a novel strategy to simultaneously acquire data for the reconstruction of five intrinsically co-registered 1-mm isotropic resolution multi-parametric maps, with a scan time of 6 minutes using JVC-GRAPPA.
2021-09-04
Magnetic Resonance in Medicine (publié)
doi.org
Sleep spindles track cortical learning patterns for memory consolidation
Marit Petzka
Alex Chatburn
Ian Charest
George M. Balanos
Bernhard P. Staresina
2021-09-03
Current Biology (publié)
doi.org
Sleep spindles track cortical learning patterns for memory consolidation
Marit Petzka
Alex Chatburn
Ian Charest
G. Balanos
Bernhard P Staresina
2021-09-03
Current Biology (published)
doi.org
Application of Artificial Intelligence in Community-Based Primary Health Care: Systematic Scoping Review and Critical Appraisal
Samira Abbasgholizadeh-Rahimi
France Légaré
Gauri Sharma
Patrick Archambault
Hervé Tchala Vignon Zomahoun
Sam Chandavong
Nathalie Rheault
Sabrina T Wong
Lyse Langlois
Yves Couturier
Jose L Salmeron
Marie-Pierre Gagnon
Jean Légaré
Background Research on the integration of artificial intelligence (AI) into community-based primary health care (CBPHC) has highlighted seve… (voir plus)ral advantages and disadvantages in practice regarding, for example, facilitating diagnosis and disease management, as well as doubts concerning the unintended harmful effects of this integration. However, there is a lack of evidence about a comprehensive knowledge synthesis that could shed light on AI systems tested or implemented in CBPHC. Objective We intended to identify and evaluate published studies that have tested or implemented AI in CBPHC settings. Methods We conducted a systematic scoping review informed by an earlier study and the Joanna Briggs Institute (JBI) scoping review framework and reported the findings according to PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analysis-Scoping Reviews) reporting guidelines. An information specialist performed a comprehensive search from the date of inception until February 2020, in seven bibliographic databases: Cochrane Library, MEDLINE, EMBASE, Web of Science, Cumulative Index to Nursing and Allied Health Literature (CINAHL), ScienceDirect, and IEEE Xplore. The selected studies considered all populations who provide and receive care in CBPHC settings, AI interventions that had been implemented, tested, or both, and assessed outcomes related to patients, health care providers, or CBPHC systems. Risk of bias was assessed using the Prediction Model Risk of Bias Assessment Tool (PROBAST). Two authors independently screened the titles and abstracts of the identified records, read the selected full texts, and extracted data from the included studies using a validated extraction form. Disagreements were resolved by consensus, and if this was not possible, the opinion of a third reviewer was sought. A third reviewer also validated all the extracted data. Results We retrieved 22,113 documents. After the removal of duplicates, 16,870 documents were screened, and 90 peer-reviewed publications met our inclusion criteria. Machine learning (ML) (41/90, 45%), natural language processing (NLP) (24/90, 27%), and expert systems (17/90, 19%) were the most commonly studied AI interventions. These were primarily implemented for diagnosis, detection, or surveillance purposes. Neural networks (ie, convolutional neural networks and abductive networks) demonstrated the highest accuracy, considering the given database for the given clinical task. The risk of bias in diagnosis or prognosis studies was the lowest in the participant category (4/49, 4%) and the highest in the outcome category (22/49, 45%). Conclusions We observed variabilities in reporting the participants, types of AI methods, analyses, and outcomes, and highlighted the large gap in the effective development and implementation of AI in CBPHC. Further studies are needed to efficiently guide the development and implementation of AI interventions in CBPHC settings.
2021-09-01
Journal of Medical Internet Research (publié)
doi.org
Automated Traceability for Domain Modelling Decisions Empowered by Artificial Intelligence
Rijul Saini
Gunter Mussbacher
Jin Guo
Jörg Kienzle
Domain modelling abstracts real-world entities and their relationships in the form of class diagrams for a given domain problem space. Model… (voir plus)lers often perform domain modelling to reduce the gap between understanding the problem description which expresses requirements in natural language and the concise interpretation of these requirements. However, the manual practice of domain modelling is both time-consuming and error-prone. These issues are further aggravated when problem descriptions are long, which makes it hard to trace modelling decisions from domain models to problem descriptions or vice-versa leading to completeness and conciseness issues. Automated support for tracing domain modelling decisions in both directions is thus advantageous. In this paper, we propose an automated approach that uses artificial intelligence techniques to extract domain models along with their trace links. We present a traceability information model to enable traceability of modelling decisions in both directions and provide its proof-of-concept in the form of a tool. The evaluation on a set of unseen problem descriptions shows that our approach is promising with an overall median F2 score of 82.04%. We conduct an exploratory user study to assess the benefits and limitations of our approach and present the lessons learned from this study.
2021-09-01
IEEE International Requirements Engineering Conference (publié)
doi.org
DoMoBOT: A Modelling Bot for Automated and Traceable Domain Modelling
Rijul Saini
Gunter Mussbacher
Jin Guo
Jörg Kienzle
In the initial phases of the software development cycle, domain modelling is typically performed to transform informal requirements expresse… (voir plus)d in natural language into concise and analyzable domain models. These models capture the key concepts of an application domain and their relationships in the form of class diagrams. Building domain models manually is often a time-consuming and labor-intensive task. The current approaches which aim to extract domain models automatically, are inadequate in providing insights into the modelling decisions taken by extractor systems. This inhibits modellers to quickly confirm the completeness and conciseness of extracted domain models. To address these challenges, we present DoMoBOT, a domain modelling bot that uses a traceability knowledge graph to enable traceability of modelling decisions from extracted domain model elements to requirements and vice-versa. In this tool demo paper, we showcase how the implementation and architecture of DoMoBOT facilitate modellers to extract domain models and gain insights into the modelling decisions taken by our bot.
2021-09-01
IEEE International Requirements Engineering Conference (publié)
doi.org
Full-Scale Information Diffusion Prediction With Reinforced Recurrent Networks
Cheng Yang
Hao Wang
Jian Tang
Chuan Shi
Maosong Sun
Ganqu Cui
Zhiyuan Liu
Information diffusion prediction is an important task, which studies how information items spread among users. With the success of deep lear… (voir plus)ning techniques, recurrent neural networks (RNNs) have shown their powerful capability in modeling information diffusion as sequential data. However, previous works focused on either microscopic diffusion prediction, which aims at guessing who will be the next influenced user at what time, or macroscopic diffusion prediction, which estimates the total numbers of influenced users during the diffusion process. To the best of our knowledge, few attempts have been made to suggest a unified model for both microscopic and macroscopic scales. In this article, we propose a novel full-scale diffusion prediction model based on reinforcement learning (RL). RL incorporates the macroscopic diffusion size information into the RNN-based microscopic diffusion model by addressing the nondifferentiable problem. We also employ an effective structural context extraction strategy to utilize the underlying social graph information. Experimental results show that our proposed model outperforms state-of-the-art baseline models on both microscopic and macroscopic diffusion predictions on three real-world datasets.
2021-09-01
IEEE Transactions on Neural Networks and Learning Systems (published)
doi.org
Full-Scale Information Diffusion Prediction With Reinforced Recurrent Networks
Cheng Yang
Hao Wang
Jian Tang
Chuan Shi
Maosong Sun
Ganqu Cui
Zhiyuan Liu
Information diffusion prediction is an important task, which studies how information items spread among users. With the success of deep lear… (voir plus)ning techniques, recurrent neural networks (RNNs) have shown their powerful capability in modeling information diffusion as sequential data. However, previous works focused on either microscopic diffusion prediction, which aims at guessing who will be the next influenced user at what time, or macroscopic diffusion prediction, which estimates the total numbers of influenced users during the diffusion process. To the best of our knowledge, few attempts have been made to suggest a unified model for both microscopic and macroscopic scales. In this article, we propose a novel full-scale diffusion prediction model based on reinforcement learning (RL). RL incorporates the macroscopic diffusion size information into the RNN-based microscopic diffusion model by addressing the nondifferentiable problem. We also employ an effective structural context extraction strategy to utilize the underlying social graph information. Experimental results show that our proposed model outperforms state-of-the-art baseline models on both microscopic and macroscopic diffusion predictions on three real-world datasets.
2021-09-01
IEEE Transactions on Neural Networks and Learning Systems (publié)
doi.org
Promoting and Optimizing the Use of 3D-Printed Objects in Spontaneous Recognition Memory Tasks in Rodents: A Method for Improving Rigor and Reproducibility
Mehreen Inayat
Arely Cruz-Sanchez
Hayley H. A. Thorpe
Jude A. Frie
Blake Richards
Jibran Y. Khokhar
Maithe Arruda-Carvalho
2021-09-01
eNeuro (publié)
doi.org
A Data Mining Analysis of Cross-Regional Study of Apparel Consumption
Osmud Rahman
Benjamin Fung
2021-08-28
Comprehensible Science (publié)
doi.org