Now showing items 1-11 of 11

    • Advancing Public Health through Computational Thematic Analysi 

      Gauthier, Robert P.; Wallace, James (Association for Computing Machinery (ACM), 2023-04-28)
      Public health researchers often use thematic analysis to develop human understandings of discussions from online communities. However, these analyses are difficult to conduct because of their and a general lack of programming ...
    • Critical Dynamics In Population Vaccinating Behavior 

      Pananos, A. Demetri; Bury, Thomas M.; Wang, Clara; Schonfeld, Justin; Mohanty, Sharada P.; Nyhan, Brendan; Salathe, Marcel; Bauch, Chris T. (National Academy of Sciences, 2017-12-26)
      Vaccine refusal can lead to renewed outbreaks of previously eliminated diseases and even delay global eradication. Vaccinating decisions exemplify a complex, coupled system where vaccinating behavior and disease dynamics ...
    • Exploratory Assessment of K-means Clustering to Classify 18F-Flutemetamol Brain PET as Positive or Negative 

      Zukotynski, Katherine; Black, Sandra E.; Kuo, Phillip H.; Bhan, Aparna; Adamo, Sabrina; Scott, Christopher J.M.; Lam, Benjamin; Masellis, Mario; Kumar, Sanjeev; Fischer, Corinne E.; Tartaglia, Maria Carmela; Lang, Anthony E.; Tang-Wai, David F.; Freedman, Morris; Vasdev, Neil; Gaudet, Vincent C. (Wolters Kluwer Health, 2021)
      Rationale: We evaluated K-means clustering to classify amyloid brain PETs as positive or negative. Patients and Methods: Sixty-six participants (31 men, 35 women; age range, 52–81 years) were recruited through a multicenter ...
    • "I Will Not Drink With You Today": A Topic-Guided Thematic Analysis of Addiction Recovery on Reddit 

      Gauthier, Robert; Costello, Mary Jean; Wallace, James (ACM, 2022-04)
      Recovery from addiction is a journey that requires a lifetime of support from a strong network of peers. Many people seek out this support through online communities, like those on Reddit. However, as these communities ...
    • Machine Learning in the Nuclear Medicine: Part 1-Introduction 

      Uribe, Carlos F.; Mathotaarachchi, Sulantha; Gaudet, Vincent C.; Smith, Kenneth C.; Rosa-Neto, Pedro; Benard, Francois; Black, Sandra E.; Zukotynski, Katherine (Society of Nuclear Medicine and Molecular Imaging, 2019-04)
      This article, the first in a 2-part series, provides an introduction to machine learning (ML) in a nuclear medicine context. This part addresses the history of ML and describes common algorithms, with illustrations of when ...
    • Machine Learning in the Nuclear Medicine: Part 2-Neural Networks and Clinical Aspects 

      Zukotynski, Katherine; Gaudet, Vincent C.; Uribe, Carlos F.; Mathotaarachchi, Sulantha; Smith, Kenneth C.; Rosa-Neto, Pedro; Benard, Francois; Black, Sandra E. (Society of Nuclear Medicine and Molecular Imaging, 2021-01)
      This article is the second part in our machine learning series. Part 1 provided a general overview of machine learning in nuclear medicine. Part 2 focuses on neural networks. We start with an example illustrating how neural ...
    • Modelling reduction and enrichment effects of urban stormwater best management practices on phosphorus at the watershed scale 

      Zhou, Bowen; Radosavljevic, Jovana; Parsons, Chris T.; Rezanezhad, Fereidoun; Passeport, Elodie; Van Cappellen, Philippe (University of Waterloo, 2023-05-18)
      A variety of best management practices (BMPs) are being implemented to attenuate the increasing eutrophication risk caused by excessive phosphorus (P) export via urban stormwater runoff. However, P reduction performance ...
    • Neural network enhanced measurement efficiency for molecular groundstates 

      Iouchtchenko, Dmitri; Gonthier, Jérôme F.; Perdomo-Ortiz, Alejandro; Melko, Roger (IOP Publishing, 2023-02-09)
      It is believed that one of the first useful applications for a quantum computer will be the preparation of groundstates of molecular Hamiltonians. A crucial task involving state preparation and readout is obtaining physical ...
    • Nonlinear model predictive control of a multiscale thin film deposition process using artificial neural networks 

      Kimaev, Grigoriy; Ricardez-Sandoval, Luis A. (Elsevier, 2019-11-02)
      The purpose of this study was to employ Artificial Neural Networks (ANNs) to develop data-driven models that would enable the shrinking horizon nonlinear model predictive control of a computationally intensive stochastic ...
    • Surface solar radiation and lake productivity: Investigating a global relationship 

      Ye, Jane (University of Waterloo, 2021-04-14)
      In recent decades, the intensity and frequency of lake algal blooms have been increasing worldwide. In addition to potentially toxic effects for humans and wildlife, intense algal blooms negatively impact recreation and ...
    • The use of random forests to classify amyloid brain PET 

      Zukotynski, Katherine; Gaudet, Vincent C.; Kuo, Phillip H.; Adamo, Sabrina; Goubran, Maged; Scott, Christopher; Bocti, Christian; Borrie, Michael; Chertkow, Howard; Frayne, Richard; Hsiung, Robin; Laforce, Robert; Noseworthy, Michael D.; Prato, Frank S.; Sahlas, Demetrios J.; Smith, Eric E.; Sossi, Vesna; Thiel, Alexander; Soucy, Jean-Paul; Tardif, Jean-Claude; Black, Sandra E. (Wolters Kluwer Health, 2019-10)
      Purpose: To evaluate random forests (RFs) as a supervised machine learning algorithm to classify amyloid brain PET as positive or negative for amyloid deposition and identify key regions of interest for stratification. Methods: ...


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