Low-dose CT (LDCT) has been commonly used for lung cancer screening and it is much desirable to computerize the image analysis for risk evaluation to reduce healthcare disparities. While informative structural image features can be extracted from medical images using state-of-the-art deep neural networks, other quantitative clinical measurements can also contribute to the overall assessment but are often ignored by researchers and also difficult to obtain. This work introduces a multi-task learning framework, which can simultaneously extract image features from LDCT images and estimate the clinical measurements for all-cause mortality risk prediction. The proposed method is a hybrid neural network with multi-scale input and multi-task supervision labels. The presented work shows that the extracted feature vectors have improved mortality prediction as they are generated to include both abstracted image features and high-level clinical knowledge.