AIMC Topic: Ontario

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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 Deep Learning Ontario Pregnancy Trimester, First Chromosome Aberrations Humans Pregnancy Female Ultrasonography, Prenatal
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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...
Humans Female Adult Ultrasonography Ontario Neoadjuvant Therapy Carcinoma, Ductal, Breast Breast Neoplasms Middle Aged Antineoplastic Agents Deep Learning
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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...
Hospitalization Adult Ontario Models, Statistical Humans Machine Learning Forecasting
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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.
Humans Machine Learning Retrospective Studies Pregnancy Female Premature Birth Ontario Parity Logistic Models Pregnancy Trimester, Second Area Under Curve Young Adult Pregnancy Trimester, First Adult ROC Curve
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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.
Female Aged Aged, 80 and over Young Adult Algorithms Forecasting Retrospective Studies Electronic Health Records Adolescent Incidence Cohort Studies Child Adult Male Middle Aged Risk Assessment Humans Machine Learning Decision Making, Computer-Assisted Ontario Diabetes Mellitus, Type 2 Age of Onset
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Cohort profile: St. Michael's Hospital Tuberculosis Database (SMH-TB), a retrospective cohort of electronic health record data and variables extracted using natural language processing.

PloS one Mar 3, 2021
BACKGROUND: Tuberculosis (TB) is a major cause of death worldwide. TB research draws heavily on clinical cohorts which can be generated using electronic health records (EHR), but granular information extracted from unstructured EHR data is limited. T...
Information Storage and Retrieval Ontario Natural Language Processing Tuberculosis Humans Databases, Factual Retrospective Studies Electronic Health Records Female Male Hospitals
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Accuracy of Algorithms to Identify People with Atopic Dermatitis in Ontario Routinely Collected Health Databases.

The Journal of investigative dermatology Feb 9, 2021
Electronic Health Records Ontario Data Collection Dermatitis, Atopic Cost of Illness Administrative Claims, Healthcare Humans Machine Learning Databases, Factual
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Application of machine learning to identify predators of stocked fish in Lake Ontario: using acoustic telemetry predation tags to inform management.

Journal of fish biology Nov 3, 2020
Understanding predator-prey interactions and food web dynamics is important for ecosystem-based management in aquatic environments, as they experience increasing rates of human-induced changes, such as the addition and removal of fishes. To quantify ...
Animal Identification Systems Trout Food Chain Predatory Behavior Telemetry Fisheries Ontario Salmonidae Machine Learning Acoustics Animals Ecosystem Lakes
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Conditionally positive: a qualitative study of public perceptions about using health data for artificial intelligence research.

BMJ open Oct 28, 2020
OBJECTIVES: Given widespread interest in applying artificial intelligence (AI) to health data to improve patient care and health system efficiency, there is a need to understand the perspectives of the general public regarding the use of health data ...
Adult Humans Male Artificial Intelligence Ontario Female Middle Aged Medical Informatics Aged Qualitative Research Public Opinion Focus Groups
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Identifying drugs with disease-modifying potential in Parkinson's disease using artificial intelligence and pharmacoepidemiology.

Pharmacoepidemiology and drug safety May 14, 2020
PURPOSE: The aim of the study was to assess the feasibility of an approach combining computational methods and pharmacoepidemiology to identify potentially disease-modifying drugs in Parkinson's disease (PD).
Ontario Parkinson Disease Dexamethasone Antiparkinson Agents Pharmacoepidemiology Theophylline Humans Insurance Claim Review Artificial Intelligence Aged Pentoxifylline Female Aged, 80 and over Male Case-Control Studies
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