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Home hemodialysis (HD) was first introduced in the 1960s with a rapid increase in its use due to inability of dialysis units to accommodate patient demand. A sharp decline was subsequently seen with expanding outpatient dialysis facilities and changes in reimbursement policies. In the last decade, with emerging reports of benefits with home HD and more user-friendly equipment, there has been resurgence in home HD. However, home HD remains underutilized with considerable variations between and within countries. This paper will review the history of home HD, elaborate on its established benefits, identify some of the barriers in uptake of this modality and expand on potential strategies to overcome these barriers. [ABSTRACT FROM AUTHOR]

Patients with advanced cancer are burdened physically and psychologically, so there is an urgent need to pay more attention to their health-related quality of life (HRQOL). With an expected clinical endpoint prediction, over-treatment can be effectively eliminated by the means of palliative care at the right time. This paper develops a deep learning based approach for cancer clinical endpoint prediction based on patient's electronic health records (EHR). Due to the pervasive existence of categorical information in EHR, it brings unavoidably obstacles to the effective numerical learning algorithms. To address this issue, we propose a novel cross-field categorical attributes embedding (CCAE) model to learn a vectorized representation for cancer patients in attribute-level by orders, in which the strong semantic coupling among categorical variables are well exploited. By transforming the order-dependency modeling into a sequence learning task in an ingenious way, recurrent neural network is adopted to capture the semantic relevance among multi-order representations. Experimental results from the SEER-Medicare EHR dataset have illustrated that the proposed model can achieve competitive prediction performance compared with other baselines. [ABSTRACT FROM AUTHOR]