|Title||Employing time-series forecasting to historical medical data: an application towards early prognosis within elderly health monitoring environments|
|Publication Type||Conference Paper|
|Year of Publication||2014|
|Authors||Billis, Antonis, and Bamidis Panagiotis|
|Conference Name||AI-AM/NetMed@ ECAI|
|Conference Location||Prague, Chech Republic|
|Keywords||time series forecasting|
This work describes a first attempt to apply time-series forecasting analysis to health historical data in order to perform prediction of early pathological signs within telehealth applications, such as the Ambient Assisted Living environments for the elderly. A benchmark of state-of-the-art learning methods were applied to a set of artificial time-series data, simulating hypertensive patient profiles, based on blood pressure measurements. Results provided a fair proof of our initial hypothesis. Based on this first experimentation, our plans are to further investigate these findings in real –life or lab settings with seniors, thus proving the usefulness of time-series forecasting as a monitoring tool and an early prognosis mechanism in telehealth systems.