Oscar Perez-Concha

Lecturer

University of New South Wales

Australia

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About

Oscar Perez-Concha is a Lecturer at the University of New South Wales, Australia. His research primarily focuses on the application of artificial intelligence in health care, particularly in areas such as child maltreatment investigation, electronic medical records de-identification, and predictive modeling in intensive care settings. Additionally, he explores the implications of synthetic data sets in health education and the automated coding of medical data.

Articles (14)

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2025

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2024

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2023

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2023

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2023

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2023

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2023

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2022

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2022

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2022

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2021

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2020

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2020

Collaborators (3)

Jimmy Chun Yu Louie

Associate Professor and Course Director of Dietetics

Swinburne University of Technology

AUSTRALIA

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Louisa Jorm

University of New South Wales

AUSTRALIA

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James Kemp

University in Western Australia

AUSTRALIA

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Oscar Perez-Concha

Research Interests

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Articles (14)

Leveraging AI to Investigate Child Maltreatment Text Narratives: Promising Benefits and Addressable Risks

Enriching Data Science and Health Care Education: Application and Impact of Synthetic Data Sets Through the Health Gym Project

Web-Based Application Based on Human-in-the-Loop Deep Learning for Deidentifying Free-Text Data in Electronic Medical Records: Development and Usability Study

Continuous time recurrent neural networks: Overview and benchmarking at forecasting blood glucose in the intensive care unit

Automated ICD coding using extreme multi-label long text transformer-based models

Complex early childhood experiences: Characteristics of Northern Territory children across health, education and child protection data

The relationship between hyperglycaemia on admission and patient outcome is modified by hyperlactatemia and diabetic status: a retrospective analysis of the eICU collaborative research database

De-identifying Australian hospital discharge summaries: An end-to-end framework using ensemble of deep learning models

Uptake of Team Care Arrangements for adults newly diagnosed with cancer

Hierarchical label-wise attention transformer model for explainable ICD coding

A Machine Learning Approach to Predict the Added-Sugar Content of Packaged Foods

Incorporating real-world evidence into the development of patient blood glucose prediction algorithms for the ICU

Diagnostic and health service pathways to diagnosis of cancer-registry notified cancer of unknown primary site (CUP)

Benchmarking Deep Learning Architectures for Predicting Readmission to the ICU and Describing Patients-at-Risk

Collaborators (3)

Jimmy Chun Yu Louie

Louisa Jorm

James Kemp

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