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Additional file 1:Table S1. ICD-9-CM diagnosis codes involved for patients with other psychiatric illnesses. Table S2. ICD-9-CM diagnosis and procedure codes for hepatic complications, liver cirrhosis, liver transplantation, HCC, and HCC treatment. Table S3. List of viral serological markers retrieved. Table S4. Drug codes of antiviral treatment used in Hospital Authority internally. Table S5. List of liver-related events for each psychiatric illness of the patients in details. Table S6. List of chronic liver diseases in patients with different psychiatric illness. Table S7. List of chronic liver diseases in 1,461 patients with liver-related events. Table S8. Causes of death in patients with psychiatric illness. Figure S1. Study patient flow. Figure S2. Expected cumulative incidence of hepatocellular carcinoma based on population data and observed cumulative incidence of hepatocellular carcinoma in patients with (A) drug-induced mental disorders, (B) alcohol-induced mental disorders, (C) psychotic disorders, and (D) mood disorders with age- and sex-standardization.

Regular medical records are useful for medical practitioners to analyze and monitor patient health status especially for those with chronic disease, but such records are usually incomplete due to unpunctuality and absence of patients. In order to resolve the missing data problem over time, tensor-based model is suggested for missing data imputation in recent papers because this approach makes use of low rank tensor assumption for highly correlated data. However, when the time intervals between records are long, the data correlation is not high along temporal direction and such assumption is not valid. To address this problem, we propose to decompose a matrix with missing data into its latent factors. Then, the locally linear constraint is imposed on these factors for matrix completion in this paper. By using a publicly available dataset and two medical datasets collected from hospital, experimental results show that the proposed algorithm achieves the best performance by comparing with the existing methods.