Demonstration of automated non-adherence and service disengagement risk monitoring with active follow-up for severe mental illness.
Niranjan BidargaddiGeoffrey SchraderHannah MylesK Oliver SchubertYasmin van KasterenTao ZhangTarun Joseph BastiampillaiElizabeth RougheadJorg StrobelPublished in: The Australian and New Zealand journal of psychiatry (2021)
Digitally automated monitoring for non-adherence risk is feasible and can be integrated into clinical workflows in community psychiatric and primary care settings. The technology may assist clinicians and services to detect non-adherence behaviour early, thereby triggering interventions that have the potential to reduce rates of mental health deterioration and acute illness relapse.
Keyphrases
- mental health
- mental illness
- primary care
- healthcare
- machine learning
- deep learning
- high throughput
- liver failure
- drug induced
- glycemic control
- early onset
- palliative care
- risk assessment
- type diabetes
- intensive care unit
- general practice
- respiratory failure
- adipose tissue
- metabolic syndrome
- single cell
- weight loss
- insulin resistance