TY - CHAP
T1 - Electronic health record for artificial intelligence health care, and application to liver disease
AU - Yang, Hsuan Chia
AU - Li, Yu Chuan
N1 - Publisher Copyright:
© 2023 Elsevier Inc. All rights reserved.
PY - 2023/1/1
Y1 - 2023/1/1
N2 - The development of artificial intelligence (AI) combined with the application of electronic health records (EHRs) has made precision medicine possible, tailoring liver disease prediction and treatment for various patient populations. Extended from precision medicine, the concepts of precision health and earlier medicine provide a wide-ranging scope to cover health promotion, disease prevention, and disease management. With the help of Taiwan's National Health Insurance claims databases, AI-based earlier medicine has realized liver disease prevention that is actionable, accurate, timely, and individualized. In this chapter, we provide two examples of EHRs used to predict fatty liver and liver cancer, introducing their data preprocessing, variable selection, machine learning model building, and model validation. Such predictive tools with promising performance enable high-risk patients to take preemptive actions. Moreover, from the perspective of a learning health care system, issues such as persistent data collection and integration, people's acceptance, and the availability of a system remain future challenges.
AB - The development of artificial intelligence (AI) combined with the application of electronic health records (EHRs) has made precision medicine possible, tailoring liver disease prediction and treatment for various patient populations. Extended from precision medicine, the concepts of precision health and earlier medicine provide a wide-ranging scope to cover health promotion, disease prevention, and disease management. With the help of Taiwan's National Health Insurance claims databases, AI-based earlier medicine has realized liver disease prevention that is actionable, accurate, timely, and individualized. In this chapter, we provide two examples of EHRs used to predict fatty liver and liver cancer, introducing their data preprocessing, variable selection, machine learning model building, and model validation. Such predictive tools with promising performance enable high-risk patients to take preemptive actions. Moreover, from the perspective of a learning health care system, issues such as persistent data collection and integration, people's acceptance, and the availability of a system remain future challenges.
KW - Artificial intelligence
KW - Earlier medicine
KW - Electronic health record
KW - Precision health
KW - Prediction of liver disease
UR - http://www.scopus.com/inward/record.url?scp=85176329844&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85176329844&partnerID=8YFLogxK
U2 - 10.1016/B978-0-323-99136-0.00010-6
DO - 10.1016/B978-0-323-99136-0.00010-6
M3 - Chapter
AN - SCOPUS:85176329844
SN - 9780323993760
SP - 81
EP - 91
BT - Artificial Intelligence, Machine Learning, and Deep Learning in Precision Medicine in Liver Diseases
PB - Elsevier
ER -