Triangolo

Most terminally ill cancer patients would prefer to die at home, although a majority die in institutional settings. Despite long-standing research aimed at implementing and improving policies favouring end-of-life home care, questions remain as to the reasons for this discrepancy. The study applies machine-learning techniques to explore in detail the complicated network of factors and the cause-effect relationships affecting the place of death. By examining the complexity and causality, a better understanding can be gained of interventions by health care professionals that can facilitate dying at home.

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