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Healthcare Big Data in Hong Kong: Development and Implementation of Artificial Intelligence-Enhanced Predictive Models for Risk Stratification.
Tse, Gary; Lee, Quinncy; Chou, Oscar Hou In; Chung, Cheuk To; Lee, Sharen; Chan, Jeffrey Shi Kai; Li, Guoliang; Kaur, Narinder; Roever, Leonardo; Liu, Haipeng; Liu, Tong; Zhou, Jiandong.
Afiliação
  • Tse G; School of Nursing and Health Studies, Hong Kong Metropolitan University, Hong Kong, China; Tianjin Key Laboratory of Ionic-Molecular Function of Cardiovascular Disease, Department of Cardiology, Tianjin Institute of Cardiology, Second Hospital of Tianjin Medical University, Tianjin 300211, China. El
  • Lee Q; Family Medicine Research Unit, Cardiovascular Analytics Group, PowerHealth Research Institute, Hong Kong, China.
  • Chou OHI; Family Medicine Research Unit, Cardiovascular Analytics Group, PowerHealth Research Institute, Hong Kong, China; Division of Clinical Pharmacology and Therapeutics, Department of Medicine, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong, China.
  • Chung CT; Family Medicine Research Unit, Cardiovascular Analytics Group, PowerHealth Research Institute, Hong Kong, China.
  • Lee S; Family Medicine Research Unit, Cardiovascular Analytics Group, PowerHealth Research Institute, Hong Kong, China.
  • Chan JSK; Family Medicine Research Unit, Cardiovascular Analytics Group, PowerHealth Research Institute, Hong Kong, China.
  • Li G; Department of Cardiovascular Medicine, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China.
  • Kaur N; Family Medicine Research Unit, Cardiovascular Analytics Group, PowerHealth Research Institute, Hong Kong, China; School of Cardiovascular Science & Metabolic Health, University of Glasgow, UK.
  • Roever L; Department of Clinical Research, Federal University of Uberlândia, Uberlândia, MG 38400384, Brazil.
  • Liu H; Research Centre for Intelligent Healthcare, Faculty of Health and Life Sciences, Coventry University, Coventry, UK.
  • Liu T; Tianjin Key Laboratory of Ionic-Molecular Function of Cardiovascular Disease, Department of Cardiology, Tianjin Institute of Cardiology, Second Hospital of Tianjin Medical University, Tianjin 300211, China.
  • Zhou J; Division of Health Science, Warwick Medical School, University of Warwick, Coventry, United Kingdom.
Curr Probl Cardiol ; 49(1 Pt B): 102168, 2024 Jan.
Article em En | MEDLINE | ID: mdl-37871712
ABSTRACT
Routinely collected electronic health records (EHRs) data contain a vast amount of valuable information for conducting epidemiological studies. With the right tools, we can gain insights into disease processes and development, identify the best treatment and develop accurate models for predicting outcomes. Our recent systematic review has found that the number of big data studies from Hong Kong has rapidly increased since 2015, with an increasingly common application of artificial intelligence (AI). The advantages of big data are that i) the models developed are highly generalisable to the population, ii) multiple outcomes can be determined simultaneously, iii) ease of cross-validation by for model training, development and calibration, iv) huge numbers of useful variables can be analyzed, v) static and dynamic variables can be analyzed, vi) non-linear and latent interactions between variables can be captured, vii) artificial intelligence approaches can enhance the performance of prediction models. In this paper, we will provide several examples (cardiovascular disease, diabetes mellitus, Brugada syndrome, long QT syndrome) to illustrate efforts from a multi-disciplinary team to identify data from different modalities to develop models using territory-wide datasets, with the possibility of real-time risk updates by using new data captured from patients. The benefit is that only routinely collected data are required for developing highly accurate and high-performance models. AI-driven models outperform traditional models in terms of sensitivity, specificity, accuracy, area under the receiver operating characteristic and precision-recall curve, and F1 score. Web and/or mobile versions of the risk models allow clinicians to risk stratify patients quickly in clinical settings, thereby enabling clinical decision-making. Efforts are required to identify the best ways of implementing AI algorithms on the web and mobile apps.
Assuntos

Texto completo: 1 Coleções: 01-internacional Contexto em Saúde: 1_ASSA2030 Base de dados: MEDLINE Assunto principal: Inteligência Artificial / Síndrome de Brugada Limite: Humans País/Região como assunto: Asia Idioma: En Revista: Curr Probl Cardiol Ano de publicação: 2024 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Contexto em Saúde: 1_ASSA2030 Base de dados: MEDLINE Assunto principal: Inteligência Artificial / Síndrome de Brugada Limite: Humans País/Região como assunto: Asia Idioma: En Revista: Curr Probl Cardiol Ano de publicação: 2024 Tipo de documento: Article