The problem of missing data has made it difficult to analyze Electronic Health Records (EHR). In EHR data, the “missingness” often results from the low-rank property: each patient is considered a mixture of prototypical patients, and certain types of patients will have similar missing entries in their records. However, most existing methods to deal with missing data fail to capture this low-rank property of missing data. Hence we propose to use matrix factorization and matrix completion methods to perform prediction in the presence of missing data. We validated our methods in the task of post-surgical complication prediction and experimental results show that our method can improve the prediction accuracy significantly.