Polynomial regression real patient state estimate for clinical decision-making

Abstract

With the progress of the times, science and technology are changing with each passing day. Clinical decision has become more and more important in medicine nowadays. Clinical decision not only helps clinicians to get immediately crucial decisions; but also provides advices to inexperienced clinicians. In the early days, clinicians could only rely on their own experience and medical reports to make decisions. This process that clinicians analyze patients was very time-consuming. In order to solve these problems, we developed a scoring model. We can analyze patient conditions according to the value of each parameter by using the patient data collected by the hospital. Through computer analysis, evaluations, predictions and optimizations, the suitable model for clinicians and patients can be built. In this paper, we propose a nonlinear polynomial regression approach as a model for predicting patient health scores. The model that predicts patient health score fits multiple researches and clinical examinations through computer simulations. The predicted results are corresponded to the real results when we use the model. With the benefit of the model, it would be easier for clinicians to make clinical decision. In conclusion, our model can not only analyze patient’s conditions, but also predict patient health score via the support of appropriate parameters. This model has the potential to become a valuable tool for clinicians on clinical decision-making in the near future.