GAN を用いた擬似データ生成による血中薬物濃度推定

抄録

Medical treatment cannot avoid the risk like side effects. In general, appropriate dosages and dosing intervals will vary among patients. The most important consideration in administration design is blood drug concentration of the patient, and it is necessary to estimate the concentration beforehand for the administration plan. However, since it is difficult to estimate personal blood drug concentration of patient, it is extremely difficult to precisely decide administration design. Additionally, observed data in pharmacy is value and few. In this study, we construct a Generative Adversarial Network model to generate similar data accurately. The proposed method can help training of Neural Network with small data and compared to models constructed in conventional studies and examined with methods. They are a statistical model and Neural Network model proposed in previous studies. As results, our model outperformed those methods．