Hemorrhagic transformation (HT) is one of the common complications in patients with acute ischemic stroke (AIS). This study aims to investigate the value of different thresholds of Tmax generated from perfusion-weighted MR imaging (PWI) and the apparent diffusion coefficient (ADC) value in the prediction of HT in AIS. A total of 156 AIS patients were enrolled in this study, with 55 patients in the HT group and 101 patients in non-HT group. The clinical baseline data and multi-parametric MRI findings were compared between HT and non-HT groups to identify indicators related to HT. The optimal parameters for predicting HT and the corresponding cutoff values were obtained using the receiver operating characteristic curve analysis of the volumes of ADC < 620 × 10−6 mm2/s and Tmax > 6 s, 8 s, and 10 s. The results showed that the volumes of ADC < 620 × 10−6 mm2/s and Tmax > 6 s, 8 s, and 10 s in the HT group were all significantly larger than that in the non-HT group and were all independent risk factors for HT. Early measurement of the volume of Tmax > 10 s had the highest value, with a cutoff lesion volume of 10.5 mL.

Although matrix factorization model has become the major method in the collaborative filtering,it ignores the combined influence of the user bias and latentitems characteristics on recommendation quality.Therefore,this research proposed a collective matrix factorization algorithm,which factorizes items rating matrix and items co-occurrence matrix to amend user bias based on matrix factorization model.The experimental results from different benchmark datasets prove the rationality of the combined factorization algorithm,and indicate greater improvement in the ranking-based metrics in comparison with the traditional matrix factorization model.