Detection of Behavioral Anomalies in Medication Adherence Patterns Among Patients With Serious Mental Illness Engaged With a Digital Medicine System.
Jonathan KnightsZahra HeidaryJeffrey M CochranPublished in: JMIR mental health (2020)
Digital medicine systems offer new opportunities to inform treatment decisions and provide complementary information about medication adherence. This paper introduces the concept of adherence volatility and develops a new type of contextual anomaly detection, which does not require an a priori definition of normal and allows expectations to evolve with shifting behavior, removing the need to rely on training data or static reference sequences. Retrospective analysis from clinical trial data highlights that such an approach could provide new opportunities to meaningfully engage patients about potential shifts in their ingestion behavior; however, this framework is not intended to replace clinical judgment, rather to highlight elements of data that warrant attention. The evidence provided here identifies new areas for research and seems to justify additional explorations in this area.
Keyphrases
- mental illness
- electronic health record
- clinical trial
- big data
- mental health
- end stage renal disease
- ejection fraction
- newly diagnosed
- loop mediated isothermal amplification
- prognostic factors
- type diabetes
- working memory
- randomized controlled trial
- metabolic syndrome
- healthcare
- adipose tissue
- machine learning
- artificial intelligence
- study protocol
- deep learning
- social media
- combination therapy
- replacement therapy