Population stratification for prediction of mortality in post-AKI patients

AKI is associated with increases in (1) post-discharge mortality risk, (2) length of hospital stay and (3) healthcare expenditures [19], as well as short term unplanned re-admissions and mid term progressive chronic conditions. Around 33% of AKI patients require unplanned re-admissions within 90 days after discharge and around 15% develop progressive chronic kidney disease over the first year after discharge [14, 16]. AKI is multi-factorial, and accurate follow up planning is challenging. Machine learning has been viewed as promising to build tools to support decision making in clinical follow-up planning. Broadly speaking, recent initiatives can be structured along two alternatives: 1. Tools grounded on prior medical expert knowledge, which is used to stratify patients according to meaningful attributes, in such way that specialised plans can be devised for each group of patients [5, 13, 15, 19, 23]. 2. Tools grounded on machine learning techniques, which take control of the planning process and build accurate decision procedures which, however, demand extreme care in selection of new patients, to ensure compliance with population definitions that are used during preparation of decision procedures [1, 2, 3, 7, 21, 22]. Compliance with ethical standards demands that such tools are fair, transparent, and optimised for the benefit of patients. Technical requirements to ensure ethical compliance must include algorithmic transparency to support fairness and transparency in decision making and optimised, goal-oriented patient stratification to ensure human-centred optimised performance. The research initiative presented in this article focused on the development of a tool to support clinical follow up planning for post-AKI patients after hospital discharge, with particular attention to ethical compliance based on technical requirements.