This paper investigates the antenna selection problem in massive multiple-input multiple-out (MIMO) systems under incomplete channel state information (CSI), with a particular interest on risk-aware planning subjected to practical constraints such as transmit power budgets and quality of services (QoS). Due to a very large number of antennas, obtaining complete channel measurements becomes a cost-prohibitive, energy-inefficient and spectral-inefficient task. To reduce pilot overhead, incomplete CSI and antenna selection (AS) are expected in practical massive MIMO systems. However, most existing AS algorithms heavily rely on the complete CSI, which imposes a high probability of violating the practical constraints in the scenarios of our interests. Motivated by this, we propose a joint channel prediction and antenna selection framework (JCPAS) which efficiently performs AS and is robust against the incomplete CSI and practical constraints. The proposed framework comprises i) a channel tracker which estimates the channel dynamics based on historical incomplete observations, and ii) a risk-aware Monte Carlo tree search (RA-MCTS) algorithm which utilizes the estimated channel dynamics to select antennas in a risk-aware manner. Simulation results show that the proposed RA-MCTS not only achieves much lower energy consumption compared to the existing typical algorithms, but also significantly reduces the probability of violating the practical constraints.