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Chloe Pou-Prom

Showing results (1-10 of 9) with videos related to

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Diabetes, Obesity & Metabolism|June 18, 2021
Using machine learning to predict severe hypoglycaemia in hospitalMichael Fralick, David Dai, Chloe Pou-Prom, et al.
JAMIA Open|December 3, 2025
Evaluating sociodemographic bias in a deployed machine-learned patient deterioration modelMichael Colacci, Chloe Pou-Prom, Arjumand Siddiqi, et al.
Thrombosis Research|December 6, 2021
Developing and validating natural language processing algorithms for radiology reports compared to ICD-10 codes for identifying venous thromboembolism in hospitalized medical patientsAmol A Verma, Hassan Masoom, Chloe Pou-Prom, et al.
JMIR Medical Informatics|May 4, 2021
Automating Stroke Data Extraction From Free-Text Radiology Reports Using Natural Language Processing: Instrument Validation StudyAmy Y X Yu, Zhongyu A Liu, Chloe Pou-Prom, et al.
JAMIA Open|January 23, 2025
pyDeid: an improved, fast, flexible, and generalizable rule-based approach for deidentification of free-text medical recordsVaakesan Sundrelingam, Shireen Parimoo, Frances Pogacar, et al.
Neuroradiology|August 1, 2022
Rule-based natural language processing for automation of stroke data extraction: a validation studyDane Gunter, Paulo Puac-Polanco, Olivier Miguel, et al.
Critical Care Explorations|May 8, 2023
Developing and Validating a Prediction Model For Death or Critical Illness in Hospitalized Adults, an Opportunity for Human-Computer CollaborationAmol A Verma, Chloe Pou-Prom, Liam G McCoy, et al.
CMAJ : Canadian Medical Association Journal = Journal De L'Association Medicale Canadienne|September 16, 2024
Clinical evaluation of a machine learning-based early warning system for patient deteriorationAmol A Verma, Therese A Stukel, Michael Colacci, et al.
Plos One|March 3, 2021
Cohort profile: St. Michael's Hospital Tuberculosis Database (SMH-TB), a retrospective cohort of electronic health record data and variables extracted using natural language processingDavid Landsman, Ahmed Abdelbasit, Christine Wang, et al.
Pageof 1

Showing results (1-10 of 9) with videos related to

Sort By:
Pageof 1
Diabetes, Obesity & Metabolism|June 18, 2021
Using machine learning to predict severe hypoglycaemia in hospitalMichael Fralick, David Dai, Chloe Pou-Prom, et al.
JAMIA Open|December 3, 2025
Evaluating sociodemographic bias in a deployed machine-learned patient deterioration modelMichael Colacci, Chloe Pou-Prom, Arjumand Siddiqi, et al.
Thrombosis Research|December 6, 2021
Developing and validating natural language processing algorithms for radiology reports compared to ICD-10 codes for identifying venous thromboembolism in hospitalized medical patientsAmol A Verma, Hassan Masoom, Chloe Pou-Prom, et al.
JMIR Medical Informatics|May 4, 2021
Automating Stroke Data Extraction From Free-Text Radiology Reports Using Natural Language Processing: Instrument Validation StudyAmy Y X Yu, Zhongyu A Liu, Chloe Pou-Prom, et al.
JAMIA Open|January 23, 2025
pyDeid: an improved, fast, flexible, and generalizable rule-based approach for deidentification of free-text medical recordsVaakesan Sundrelingam, Shireen Parimoo, Frances Pogacar, et al.
Neuroradiology|August 1, 2022
Rule-based natural language processing for automation of stroke data extraction: a validation studyDane Gunter, Paulo Puac-Polanco, Olivier Miguel, et al.
Critical Care Explorations|May 8, 2023
Developing and Validating a Prediction Model For Death or Critical Illness in Hospitalized Adults, an Opportunity for Human-Computer CollaborationAmol A Verma, Chloe Pou-Prom, Liam G McCoy, et al.
CMAJ : Canadian Medical Association Journal = Journal De L'Association Medicale Canadienne|September 16, 2024
Clinical evaluation of a machine learning-based early warning system for patient deteriorationAmol A Verma, Therese A Stukel, Michael Colacci, et al.
Plos One|March 3, 2021
Cohort profile: St. Michael's Hospital Tuberculosis Database (SMH-TB), a retrospective cohort of electronic health record data and variables extracted using natural language processingDavid Landsman, Ahmed Abdelbasit, Christine Wang, et al.
Pageof 1