ArXiv Preprint
Prescription medications often impose temporal constraints on regular health
behaviors (RHBs) of patients, e.g., eating before taking medication. Violations
of such medical temporal constraints (MTCs) can result in adverse effects.
Detecting and predicting such violations before they occur can help alert the
patient. We formulate the problem of modeling MTCs and develop a
proof-of-concept solution, ActSafe, to predict violations of MTCs well ahead of
time. ActSafe utilizes a context-free grammar based approach for extracting and
mapping MTCs from patient education materials. It also addresses the challenges
of accurately predicting RHBs central to MTCs (e.g., medication intake). Our
novel behavior prediction model, HERBERT , utilizes a basis vectorization of
time series that is generalizable across temporal scale and duration of
behaviors, explicitly capturing the dependency between temporally collocated
behaviors. Based on evaluation using a real-world RHB dataset collected from 28
patients in uncontrolled environments, HERBERT outperforms baseline models with
an average of 51% reduction in root mean square error. Based on an evaluation
involving patients with chronic conditions, ActSafe can predict MTC violations
a day ahead of time with an average F1 score of 0.86.
Parker Seegmiller, Joseph Gatto, Abdullah Mamun, Hassan Ghasemzadeh, Diane Cook, John Stankovic, Sarah Masud Preum
2023-01-17
