2024 年 47 巻 10 号 p. 1594-1599
To conduct clinical pharmacy research, we often face the limitations of conventional statistical methods and single-center observational study. To overcome these issues, we have conducted data-driven research using machine learning methods and medical big data. Decision tree analysis, one of the typical machine learning methods, has a flowchart-like structure that allows users to easily and quantitatively evaluate the occurrence percentage of events due to the combination of multiple factors by answering related questions with Yes or No. Using this feature, we first developed a risk prediction model for acute kidney injury caused by vancomycin, a condition we frequently encounter in clinical practice. Additionally, by replacing the prediction target from a binary variable (i.e., presence or absence of adverse drug reactions) to a continuous variable (i.e., drug dosage), we built a model to estimate the initial dose of vancomycin required to reach the optimal blood level recommended by guidelines. We found its accuracy to be better than that of conventional dose-setting algorithms. Moreover, employing Japanese medical big data such as the claims database helped us overcome the major limitations of conventional clinical pharmacy research such as institutional bias caused by single-center studies. We demonstrated that the combined use of machine learning and medical big data could generate high-quality evidence leveraging the strengths of each approach. Data-driven clinical pharmacy research using machine learning and medical big data has enabled researchers to surpass the limitations of conventional research and produce clinically valuable findings.
