AIMC Topic: Ontario
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Prediction of Social Engagement in Long-Term Care Homes by Sex: A Population-Based Analysis Using Machine Learning.
Journal of applied gerontology : the official journal of the Southern Gerontological Society
Oct 12, 2024
The objective of this study was to use population-based clinical assessment data to build and evaluate machine-learning models for predicting social engagement among female and male residents of long-term care (LTC) homes. Routine clinical assessment...
Prediction of preterm birth in multiparous women using logistic regression and machine learning approaches.
Scientific reports
Sep 20, 2024
To predict preterm birth (PTB) in multiparous women, comparing machine learning approaches with traditional logistic regression. A population-based cohort study was conducted using data from the Ontario Better Outcomes Registry and Network (BORN). Th...
Identifying Psychosocial and Ecological Determinants of Enthusiasm In Youth: Integrative Cross-Sectional Analysis Using Machine Learning.
JMIR public health and surveillance
Sep 12, 2024
BACKGROUND: Understanding the factors contributing to mental well-being in youth is a public health priority. Self-reported enthusiasm for the future may be a useful indicator of well-being and has been shown to forecast social and educational succes...
Comparison of machine learning and conventional statistical modeling for predicting readmission following acute heart failure hospitalization.
American heart journal
Jul 31, 2024
INTRODUCTION: Developing accurate models for predicting the risk of 30-day readmission is a major healthcare interest. Evidence suggests that models developed using machine learning (ML) may have better discrimination than conventional statistical mo...
Socio-demographic predictors of not having private dental insurance coverage: machine-learning algorithms may help identify the disadvantaged.
BMC public health
May 23, 2024
BACKGROUND: For accessing dental care in Canada, approximately 62% of the population has employment-based insurance, 6% have some publicly funded coverage, and 32% have to pay out-of pocket. Those with no insurance or public coverage find dental care...
Machine Learning to Allocate Palliative Care Consultations During Cancer Treatment.
Journal of clinical oncology : official journal of the American Society of Clinical Oncology
Feb 15, 2024
PURPOSE: For patients with advanced cancer, early consultations with palliative care (PC) specialists reduce costs, improve quality of life, and prolong survival. However, capacity limitations prevent all patients from receiving PC shortly after diag...
Application of classification machine learning algorithms for characterizing nutrient transport in a clay plain agricultural watershed.
Journal of environmental management
Sep 6, 2023
Excess nutrients in surface water and groundwater can lead to water quality deterioration in available water resources. Thus, the classification of nutrient concentrations in water resources has gained significant attention during recent decades. Mac...
Perceptions of Artificial Intelligence Use in Primary Care: A Qualitative Study with Providers and Staff of Ontario Community Health Centres.
Journal of the American Board of Family Medicine : JABFM
Mar 22, 2023
PURPOSE: To understand staff and health care providers' views on potential use of artificial intelligence (AI)-driven tools to help care for patients within a primary care setting.
The evolving use of robotic surgery: a population-based analysis.
Surgical endoscopy
Oct 17, 2022
INTRODUCTION: Robotic surgery has integrated into the healthcare system despite limited evidence demonstrating its clinical benefit. Our objectives were (i) to describe secular trends and (ii) patient- and system-level determinants of the receipt of ...
Validation of a natural language processing algorithm to identify adenomas and measure adenoma detection rates across a health system: a population-level study.
Gastrointestinal endoscopy
Jul 16, 2022
BACKGROUND AND AIMS: Measuring adenoma detection rates (ADRs) at the population level is challenging because pathology reports are often reported in an unstructured format; further, there is significant variation in reporting methods across instituti...
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