AIMC Topic: Republic of Korea
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Long-term PM exposure and the clinical application of machine learning for predicting incident atrial fibrillation.
Scientific reports
Oct 1, 2020
Clinical impact of fine particulate matter (PM) air pollution on incident atrial fibrillation (AF) had not been well studied. We used integrated machine learning (ML) to build several incident AF prediction models that include average hourly measurem...
Korean clinical entity recognition from diagnosis text using BERT.
BMC medical informatics and decision making
Sep 30, 2020
BACKGROUND: While clinical entity recognition mostly aims at electronic health records (EHRs), there are also the demands of dealing with the other type of text data. Automatic medical diagnosis is an example of new applications using a different dat...
Performance of a Deep Learning Algorithm Compared with Radiologic Interpretation for Lung Cancer Detection on Chest Radiographs in a Health Screening Population.
Radiology
Sep 22, 2020
Background The performance of a deep learning algorithm for lung cancer detection on chest radiographs in a health screening population is unknown. Purpose To validate a commercially available deep learning algorithm for lung cancer detection on ches...
A data-driven approach to a chemotherapy recommendation model based on deep learning for patients with colorectal cancer in Korea.
BMC medical informatics and decision making
Sep 22, 2020
BACKGROUND: Clinical Decision Support Systems (CDSSs) have recently attracted attention as a method for minimizing medical errors. Existing CDSSs are limited in that they do not reflect actual data. To overcome this limitation, we propose a CDSS base...
Development of machine learning-based clinical decision support system for hepatocellular carcinoma.
Scientific reports
Sep 9, 2020
There is a significant discrepancy between the actual choice for initial treatment option for hepatocellular carcinoma (HCC) and recommendations from the currently used BCLC staging system. We develop a machine learning-based clinical decision suppor...
Real-World Implications of a Rapidly Responsive COVID-19 Spread Model with Time-Dependent Parameters via Deep Learning: Model Development and Validation.
Journal of medical Internet research
Sep 9, 2020
BACKGROUND: The COVID-19 pandemic has caused major disruptions worldwide since March 2020. The experience of the 1918 influenza pandemic demonstrated that decreases in the infection rates of COVID-19 do not guarantee continuity of the trend.
XGBoost-Based Framework for Smoking-Induced Noncommunicable Disease Prediction.
International journal of environmental research and public health
Sep 7, 2020
Smoking-induced noncommunicable diseases (SiNCDs) have become a significant threat to public health and cause of death globally. In the last decade, numerous studies have been proposed using artificial intelligence techniques to predict the risk of d...
Predicting Psychological Distress Amid the COVID-19 Pandemic by Machine Learning: Discrimination and Coping Mechanisms of Korean Immigrants in the U.S.
International journal of environmental research and public health
Aug 20, 2020
The current study examined the predictive ability of discrimination-related variables, coping mechanisms, and sociodemographic factors on the psychological distress level of Korean immigrants in the U.S. amid the COVID-19 pandemic. Korean immigrants ...
Artificial Intelligence-Driven Oncology Clinical Decision Support System for Multidisciplinary Teams.
Sensors (Basel, Switzerland)
Aug 20, 2020
Watson for Oncology (WfO) is a clinical decision support system driven by artificial intelligence. In Korea, WfO is used by multidisciplinary teams (MDTs) caring for cancer patients. This study aimed to investigate the effect of WfO use on hospital s...
Prediction of 1-Year Mortality from Acute Myocardial Infarction Using Machine Learning.
The American journal of cardiology
Jul 26, 2020
Risk stratification at hospital discharge could be instrumental in guiding postdischarge care. In this study, the risk models for 1-year mortality using machine learning (ML) were evaluated for guiding management of acute myocardial infarction (AMI) ...
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