Delirium is a common but underdiagnosed state of disturbed attention and cognition that afflicts one in four older hospital inpatients. It is independently associated with a longer length of hospital stay, mortality, accelerated cognitive decline and new-onset dementia.
Risk stratification models enable clinicians to identify patients at high risk of an adverse event and intervene where appropriate. The advent of wearables, genomics, and dynamic datasets within electronic health records (EHRs) provides big data to which machine learning (ML) can be applied to individualise clinical risk prediction. ML is a subset of artificial intelligence that uses advanced computer programmes to learn patterns and associations within large datasets and develop models (or algorithms), which can then be applied to new data in rapidly producing predictions or classifications, including diagnoses.
The objectives of this review from Strating et al. were to: (1) provide a more contemporary overview of research on all ML delirium prediction models designed for use in the inpatient setting; (2) characterise them according to their stage of development, validation and deployment; and (3) assess the extent to which their performance and utility in clinical practice have been evaluated.