AIMC Topic: Quebec
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Assessing foraging landscape quality in Quebec's commercial beekeeping through remote sensing, machine learning, and survival analysis.
Journal of environmental management
Jan 21, 2025
Honey bees (Apis mellifera) play an important role in our agricultural systems. In recent years, beekeepers have reported high colony mortality rates in several parts of the world. Inadequate foraging landscapes are often cited as a major factor dete...
Traditional Methods Hold Their Ground Against Machine Learning in Predicting Potentially Inappropriate Medication Use in Older Adults.
Value in health : the journal of the International Society for Pharmacoeconomics and Outcomes Research
Jul 6, 2024
OBJECTIVES: Machine learning methods have gained much attention in health sciences for predicting various health outcomes but are scarcely used in pharmacoepidemiology. The ability to identify predictors of suboptimal medication use is essential for ...
Explainable artificial intelligence models for predicting risk of suicide using health administrative data in Quebec.
PloS one
Apr 3, 2024
Suicide is a complex, multidimensional event, and a significant challenge for prevention globally. Artificial intelligence (AI) and machine learning (ML) have emerged to harness large-scale datasets to enhance risk detection. In order to trust and ac...
Tree-Based Machine Learning to Identify Predictors of Psoriasis Incidence at the Neighborhood Level: A Populational Study from Quebec, Canada.
American journal of clinical dermatology
Mar 18, 2024
BACKGROUND: Psoriasis is a major global health burden affecting ~ 60 million people worldwide. Existing studies on psoriasis focused on individual-level health behaviors (e.g. diet, alcohol consumption, smoking, exercise) and characteristics as drive...
Explainable Machine Learning Model to Predict COVID-19 Severity Among Older Adults in the Province of Quebec.
Annals of family medicine
Jan 1, 2023
Context: Patients over the age of 65 years are more likely to experience higher severity and mortality rates than other populations from COVID-19. Clinicians need assistance in supporting their decisions regarding the management of these patients. Ar...
Exploring polypharmacy with artificial intelligence: data analysis protocol.
BMC medical informatics and decision making
Jul 20, 2021
BACKGROUND: Polypharmacy is common among older adults and it represents a public health concern, due to the negative health impacts potentially associated with the use of several medications. However, the large number of medication combinations and s...
Distant metastasis time to event analysis with CNNs in independent head and neck cancer cohorts.
Scientific reports
Mar 19, 2021
Deep learning models based on medical images play an increasingly important role for cancer outcome prediction. The standard approach involves usage of convolutional neural networks (CNNs) to automatically extract relevant features from the patient's...
How Good Is Machine Learning in Predicting All-Cause 30-Day Hospital Readmission? Evidence From Administrative Data.
Value in health : the journal of the International Society for Pharmacoeconomics and Outcomes Research
Sep 7, 2020
OBJECTIVES: Hospital readmission is a main cost driver for healthcare systems, but existing works often had poor or moderate predictive results. Although the available information differs in different studies, improving prediction is different from t...
When robots care: Public deliberations on how technology and humans may support independent living for older adults.
Social science & medicine (1982)
Jun 30, 2018
While assistive robots receive growing attention as a potential solution to support older adults to live independently, several scholars question the underlying social, ethical and health policy assumptions. One perplexing issue is determining whethe...
A land use regression model for ambient ultrafine particles in Montreal, Canada: A comparison of linear regression and a machine learning approach.
Environmental research
Dec 22, 2015
Existing evidence suggests that ambient ultrafine particles (UFPs) (<0.1µm) may contribute to acute cardiorespiratory morbidity. However, few studies have examined the long-term health effects of these pollutants owing in part to a need for exposure ...
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