Health indexes are useful tools for monitoring the health condition of a population and can be used to guide healthcare policy of governments. However, most health indexes are constructed by using statistical methods to summarize recent adverse events (e.g., mortality). Information from these tools may reflect merely the impact of prior health policy holistically and can hardly indicate the most recent dynamics and its impact on future health conditions. As the advancements in medications and medical techniques rapidly evolve, there is a need of new health indexes that can reflect the most recent predictive health condition of a population and can easily be summarized with respect of any sub-population of interest. In this work, we develop a novel health index by using deep learning technique on a large-scale and longitudinal population based electronic health record (EHR). Three deep neural network (DNN) models were trained to predict 4-year event rates of mortality, hospitalization and cancer occurrence at an individual-level. Platt calibration approach was used to transform DNN output scores into estimated event risks. A novel health index is then constructed by weighted scoring these calibrated event risks. This individual-level health index not only provide a better predictive power but can also be flexibly summarized for different regions or sub-populations of interest - hence providing objective insights to develop precise personal or national policy beyond conventional health index.