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Background In personalized medicine, clinicians and health policy makers must choose the most appropriate clinical trial and make predictions for the right patient during decisionmaking [1, 2]. This approach is used to individualize medical practice. At present, clinicians can predict diseases by many methods like diagnostic imaging technique [3-7] but with fewer predictive models. In recent years, predictive modeling has been successfully applied in the medical scenarios, including the identification of risk factors [8, 9] and early detection of disease onset [10, 11]. In addition, advances have been made in using predictive modeling to predict patient outcomes [2]. The traditional predictive modeling approach involves building a global predictive model using all available training data. However, this may not be the most suitable approach for personalized

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Based on the transformation thermotics theory, many novel thermal functionalities have been achieved, such as thermal cloaking, concentrating, camouflaging, etc. Here, we propose a kind of macroscale thermal diode-like black box based on two typical outcomes of transformation thermotics-the energy shielding and harvesting units. The proposed macroscale thermal black box, acting as a thermal diode, creates a new record of transient thermal rectification ratio that can be as high as about 50, far breaking the highest record of 2.6 in the literature. Most existing thermal diodes are in the steady state realm, which, however, is not a necessary requirement. The enlightenment here lies in the recapture of the transient behaviors of thermal diode, which is more practical and promising in the thermal computation applications as it is time-consuming to wait for thermal equilibrium. The proposed macroscale thermal diode-like black box is believed to promote the implementation of thermal rectifier related applications, like thermal diode, thermal-logic operation, and phononics.