Acquired long QT syndrome (aLQTS) is often associated with poor clinical outcomes.The purpose of this study was to examine the important predictors of all-cause mortality of aLQTS patients by applying both random survival forest (RSF) and non-negative matrix factorization (NMF) analyses.Clinical characteristics and manually measured electrocardiographic (ECG) parameters were initially entered into the RSF model. Subsequently, latent variables identified using NMF were entered into the RSF as additional variables. The primary outcome was all-cause mortality.A total of 327 aLQTS patients were included. The RSF model identified 16 predictive factors with positive variable importance values: cancer, potassium, RR interval, calcium, age, JT interval, diabetes mellitus, QRS duration, QTp interval, chronic kidney disease, QTc interval, hypertension, QT interval, female, JTc interval, and cerebral hemorrhage. Increasing the number of latent features between ECG indices, which incorporated from n = 0 to n = 4 by NMF, maximally improved the prediction ability of the RSF-NMF model (C-statistic 0.77 vs 0.89).Cancer and serum potassium and calcium levels can predict all-cause mortality of aLQTS patients, as can ECG indicators including JTc and QRS. The present RSF-NMF model significantly improved mortality prediction.