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CPP : Cardiovascular Prevention and Pharmacotherapy

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2 "Machine learning"
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Original Article
Modeling of Changes in Creatine Kinase after HMG-CoA Reductase Inhibitor Prescription
Hun-Sung Kim, Jiyoung Min, Jiwon Shinn, Oak-Kee Hong, Jang-Won Son, Seong-Su Lee, Sung-Rae Kim, Soon Jib Yoo
Cardiovasc Prev Pharmacother. 2021;3(4):115-123.   Published online October 31, 2021
DOI: https://doi.org/10.36011/cpp.2021.3.e15
  • 1,447 View
  • 17 Download
Abstract PDF
Background
Statin-associated muscle symptoms are one of the side effects that physicians should consider when prescribing statins. In this study, creatine kinase (CK) levels were measured following statin prescription, and various factors affecting the CK levels were determined using machine learning.
Methods
Changes in the CK were observed every 3 months for a 12-month period in patients who received statins for the first time at Seoul St. Mary's Hospital. For each visit, we developed four basic models based on changes in the CK levels. Extreme gradient boosting, a scalable end-to-end tree boosting algorithm, which employs a decision-tree-based ensemble machine learning algorithm, was used for the prediction of changes in the CK.
Results
A total of 23,860 patients were included. Among them, 19 patients (0.08%) had increased CK levels of 2,000 IU·L−1 or more 3 months after statin prescription, and 65 patients (0.27%) exhibited CK levels of over 2,000 IU·L−1 at least once during the 12-month study period. The area under the receiver operator characteristic of each model for each visit was 0.709–0.769, and the accuracy was 0.700–0.803. In each of the models, the variables that had the strongest influence on changes in the CK were sex and previous CK value.
Conclusions
Through machine learning, factors influencing changes in the CK were identified. These results will provide the basis for future research, through which the optimal parameters of the CK prediction model can be found and the model can be used in clinical applications.
Special Article
Perceptron: Basic Principles of Deep Neural Networks
Eung-Hee Kim, Hun-Sung Kim
Cardiovasc Prev Pharmacother. 2021;3(3):64-72.   Published online July 31, 2021
DOI: https://doi.org/10.36011/cpp.2021.3.e9
  • 1,163 View
  • 24 Download
Abstract PDF
Big data, artificial intelligence, machine learning, and deep learning have received considerable attention in the medical field. Attempts to use such machine learning in areas where medical decisions are difficult or necessary are continuously being made. To date, there have been many attempts to solve problems associated with the use of machine learning by using deep learning; hence, physicians should also have basic knowledge in this regard. Deep neural networks are one of the most actively studied methods in the field of machine learning. The perceptron is one of these artificial neural network models, and it can be considered as the starting point of artificial neural network models. Perceptrons receive various inputs and produce one output. In a perceptron, various weights (ω) are given to various inputs, and as ω becomes larger, it becomes an important factor. In other words, a perceptron is an algorithm with both input and output. When an input is provided, the output is produced according to a set rule. In this paper, the decision rules of the perceptron and its basic principles are examined. The intent is to provide a wide range of physicians with an understanding of the latest machine-learning methodologies based on deep neural networks.

CPP : Cardiovascular Prevention and Pharmacotherapy