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Recent Submissions
Assessing ML classification algorithms and NLP techniques for depression detection: An experimental case study
(Public Library of Science (PLOS), 2025) Lorenzoni, Giuliano; Tavares, Cristina; Nascimento, Nathalia; Alencar, Paulo; Cowan, Donald
Context and background. Depression has affected millions of people worldwide and has become one of the most common mental disorders. Early mental disorder detection can reduce costs for public health agencies and prevent other major comorbidities. Additionally, the shortage of specialized personnel is very concerning since depression diagnosis is highly dependent on expert professionals and is time-consuming. Research problems. Recent research has evidenced that machine learning (ML) and natural language processing (NLP) tools and techniques have significantly benefited the diagnosis of depression. However, there are still several challenges in the assessment of depression detection approaches in which other conditions such as post-traumatic stress disorder (PTSD) are present. These challenges include assessing alternatives in terms of data cleaning and pre-processing techniques, feature selection, and appropriate ML classification algorithms. Purpose of the study. This paper tackles such an assessment based on a case study that compares different ML classifiers, specifically in terms of data cleaning and pre-processing, feature selection, parameter setting, and model choices. Methodology. The experimental case study is based on the Distress Analysis Interview Corpus - Wizard-of-Oz (DAIC-WOZ) dataset, which is designed to support the diagnosis of mental disorders such as depression, anxiety, and PTSD. Major findings. Besides the assessment of alternative techniques, we were able to build models with accuracy levels around 84% with Random Forest and XGBoost models, which is significantly higher than the results from the comparable literature which presented the level of accuracy of 72% from the SVM model. Conclusions. More comprehensive assessments of ML classification algorithms and NLP techniques for depression detection can advance the state of the art in terms of improved experimental settings and performance.
Targets and trade-offs: Designing environmental water transactions to navigate compounding competition on the San Saba River in Texas
(Public Library of Science (PLOS), 2025) Wight, Charles; Garmany, Kyle; Smith, Ryan; Garrick, Dustin; Richter, Brian
In river basins experiencing water scarcity, water demands for freshwater ecosystems and water users increasingly compete with one another. Environmental water transactions (EWT) offer a mechanism for resolving this competition via a voluntary agreement in which existing water users are paid to modify the time, place and/or volume of their water right to provide an environmental benefit. However, the disconnect between surface water and groundwater management creates barriers to implementation and scaling of EWTs. We study EWTs addressing water scarcity in Texas’s San Saba River, focusing on targeting the location and timing to fulfill conservation objectives. We integrate recent hydrological studies to identify trends in groundwater-surface water interaction, prioritizing stream reaches for intervention and considering both geologic and anthropogenic drivers of scarcity. We analyze water rights and well data to estimate consumptive water demands during the irrigation season. We quantify the volumetric contribution of different portfolios of water rights paired with different types of EWT to assess their contributions to flow targets, including costs and benefits associated with each portfolio. Results demonstrate that the effectiveness of EWTs relies on coordinated spatial and temporal targeting within the context of hydrogeological settings and water users. We provide cost estimates for implementing four types of EWTs ranging from one season to perpetuity ($32,040 and $404,722 respectively) that can provide 3 cubic feet per second (cfs) (0.085 cubic meters) to help meet subsistence flows in the height of irrigation season (June-Aug). These costs are contextualized within a broader water governance context that considers the benefits to producers and the environment and underscores the importance of future policy to integrate groundwater-surface water interaction.
Simulation study to evaluate when Plasmode simulation is superior to parametric simulation in comparing classification methods on high-dimensional data
(Public Library of Science (PLOS), 2025) Stolte, Marieka; Schreck, Nicholas; Slynko, Alla; Saadati, Maral; Benner, Axel; Rahnenfuhrer, Jorg; Bommert, Andrea
Simulation studies, especially neutral comparison studies, are crucial for evaluating and comparing statistical methods as they investigate whether methods work as intended and can guide an appropriate method choice. Typically, the term simulation refers to parametric simulation, i.e. computer experiments using pseudo-random numbers. For these, the full data-generating process (DGP) and outcome-generating model (OGM) are known within the simulation. However, the specification of realistic DGPs might be difficult in practice leading to oversimplified assumptions. The problem is more severe for higher-dimensional data as the number of parameters to specify typically increases with the number of variables in the data. Plasmode simulation, which is a combination of resampling covariates from a real-life dataset from the DGP of interest together with a specified OGM is often claimed to solve this problem since no explicit specification of the DGP is necessary. However, this claim is not well supported by empirical results. Here, parametric and Plasmode simulations are compared in the context of a method comparison study for binary classification methods. We focus on studies conducted with some specific data type or application in mind whose true, unknown data-generating mechanism is mimicked. The performance of Plasmode and parametric comparison studies for estimating classifier performance is compared as well as their ability to reproduce the true method ranking. The influence of misspecifications of the DGP on the results of parametric simulation and of misspecifications of the OGM on the results of parametric and Plasmode simulation are investigated. Moreover, different resampling strategies are compared for Plasmode comparison studies. The study finds that misspecifications of the DGP and OGM negatively influence the ability of the comparison studies to estimate the classification performances and method rankings. The best choice of the resampling strategy in Plasmode simulation depends on the concrete scenario.
Efficacy of a new nanoemulsion artificial tear in dry eye disease management: Study protocol for a prospective cohort study
(Public Library of Science (PLOS), 2025) Liao, Xulin; Guo, Biyue; Bian, Jingfang; Li, Peter H.; Tse, Jimmy S. H.; Ngo, William; Zhou, Lei; Lam, Thomas
Background
Dry eye disease (DED) is a complex ocular disorder with a significant prevalence worldwide, especially in the Asian population. This study aimed to investigate changes in dry eye symptoms and signs following regular use of a new nanoemulsion eye drop, Systane COMPLETE Multi-Dose Preservative-Free (MDPF), in patients with mild to moderate DED in the Asian population.
Methods and design
This is a prospective cohort study (ClinicalTrials.gov identifier: NCT06188260) that aims to recruit approximately 40 patients from the Asian population suffering from mild to moderate DED. Mild to moderate DED is defined according to the Tear Film and Ocular Surface Society (TFOS) Dry Eye Workshop (DEWS) II diagnostic criteria, including an Ocular Surface Disease Index (OSDI) score between 13-32, and with at least one of the following positive signs: corneal staining, Non-Invasive Tear Breakup Time (NITBUT), or osmolarity. The proposed follow-up period is 3 months. Patients undergo three assessments: baseline before using the eye drops, and follow-up visits after 2 weeks and 3 months regular use of the eye drops (four times daily). The primary outcome is the change in the OSDI score at 2 weeks.
Discussion
The results examine the dry eye symptoms before and after using the new nanoemulsion eye drop, Systane COMPLETE MDPF, in a cohort of mild to moderate DED sufferers. The findings may provide new treatment options for dry eye sufferers with significant clinical implications.
The System is Broken
(University of Waterloo, 2025-07-03) Jeethan, Breanne
The System is Broken is an exhibition that is a visual reflection of my experiences as a healthcare worker in an emergency department. The works represent abstract scenes of the clinical workspace. They are comprised of monoprints, digital prints, UV ink paintings, sculptural etched glass, and lightboxes. With the use of internal angiogram brain scans and collected dressing materials from the clinical setting, it is a response to the fast-paced, stressful environment of the hospital that is rife with trauma, high emotions, and anguish. Working between the emergency room and the studio, I balance my life between the two workplaces as a fuel to create work. The series speaks both to my continuous navigating of in-betweenness, but also to the overarching hierarchical nature of the medical system. By manipulating and distorting found imagery created by various medical technologies, abnormalities in the imagery are created to signal the bureaucratic structures and power imbalance of the healthcare system.