AIMC Topic: Ontario

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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...
Prognosis Female Aged Humans Machine Learning Neoplasms Ontario Referral and Consultation Palliative Care Aged, 80 and over Male Middle Aged
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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...
Clay Machine Learning Nutrients Phosphorus Algorithms Ontario
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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.
Ontario Community Health Centers Artificial Intelligence Humans Qualitative Research Primary Health Care
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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 ...
Humans Male Ontario Robotics Laparoscopy Retrospective Studies Female Adult Robotic Surgical Procedures Hospitals, Teaching
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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...
Adenoma Natural Language Processing Algorithms Colonoscopy Ontario Humans
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Using deep-learning in fetal ultrasound analysis for diagnosis of cystic hygroma in the first trimester.

PloS one Jun 22, 2022
OBJECTIVE: To develop and internally validate a deep-learning algorithm from fetal ultrasound images for the diagnosis of cystic hygromas in the first trimester.
Lymphangioma, Cystic Pregnancy Female Ultrasonography, Prenatal Humans Deep Learning Ontario Pregnancy Trimester, First Chromosome Aberrations
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Deep learning of quantitative ultrasound multi-parametric images at pre-treatment to predict breast cancer response to chemotherapy.

Scientific reports Feb 10, 2022
In this study, a novel deep learning-based methodology was investigated to predict breast cancer response to neo-adjuvant chemotherapy (NAC) using the quantitative ultrasound (QUS) multi-parametric imaging at pre-treatment. QUS multi-parametric image...
Antineoplastic Agents Deep Learning Ultrasonography Carcinoma, Ductal, Breast Ontario Neoadjuvant Therapy Adult Breast Neoplasms Middle Aged Humans Female
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Comparing regression modeling strategies for predicting hometime.

BMC medical research methodology Jul 7, 2021
BACKGROUND: Hometime, the total number of days a person is living in the community (not in a healthcare institution) in a defined period of time after a hospitalization, is a patient-centred outcome metric increasingly used in healthcare research. Ho...
Humans Machine Learning Ontario Forecasting Models, Statistical Hospitalization Adult
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Prediction of preterm birth in nulliparous women using logistic regression and machine learning.

PloS one Jun 30, 2021
OBJECTIVE: To predict preterm birth in nulliparous women using logistic regression and machine learning.
Pregnancy Female Humans Premature Birth Pregnancy Trimester, First Logistic Models Machine Learning Parity ROC Curve Pregnancy Trimester, Second Retrospective Studies Young Adult Area Under Curve Ontario Adult
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Development and Validation of a Machine Learning Model Using Administrative Health Data to Predict Onset of Type 2 Diabetes.

JAMA network open May 3, 2021
IMPORTANCE: Systems-level barriers to diabetes care could be improved with population health planning tools that accurately discriminate between high- and low-risk groups to guide investments and targeted interventions.
Child Machine Learning Ontario Algorithms Diabetes Mellitus, Type 2 Adolescent Male Forecasting Incidence Aged Humans Middle Aged Cohort Studies Age of Onset Retrospective Studies Young Adult Aged, 80 and over Risk Assessment Decision Making, Computer-Assisted Adult Electronic Health Records Female
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