PREVENT

Preterm Birth Prevention in Everyday Settings

Preterm Birth Prevention in Everyday Settings Contact Person:

Amir M. Rahmani

Other PIs/Investigators/PhD students:

Pasi Liljeberg
Anna Axelin
HannaKaisa Niela-Vilen
Iman Azimi
Johanna Saariko
Fatemeh Sarhaddi

Partners:
TYKS Hospital Funding Agency:

Academy of Finland

Project Summary:

Preterm birth (PTB) is the most common cause of neonatal deaths. Due to the high rate of PTBs (15M/y), it is extremely beneficial to identify the women at risk at an early stage and prevent PTB. Physiological parameters could help us to uncover and model multifactorial processes that lead to PTB. Continuous monitoring of such parameters holds significant promise to successful prevention. Internet of Things (IoT) technologies can be leveraged to create the ability to perform such monitoring throughout pregnancy. In this project, we tackle PTB issues by proposing an IoT platform tailored for PTB prevention for everyday settings. Our core contributions are 1) a customized architecture including a set of wearable electronic devices that are feasible for 7-9 months of continuous monitoring, 2) a personalized PTB prevention solution using artificial intelligence methods, and 3) a comprehensive performance assessment via the implementation of this monitoring in clinical trials.

Publications:

– Hannakaisa Niela-Vilen, Amir M. Rahmani, Pasi Liljeberg, and Anna Axelin, “Being ‘A Google Mom’ or Securely Monitored at Home – Perceptions of Remote Monitoring in Maternity Care,” Wiley’s Journal of Advanced Nursing, 2019.
– Iman Azimi, Olugbenga Oti, Sina Labbaf, Hannakaisa Niela-Vilen, Anna Axelin, Nikil Dutt, Pasi Liljeberg, and Amir M. Rahmani, “Personalized Maternal Sleep Quality Assessment: An Objective IoT-based Longitudinal Study,” IEEE ACCESS Journal (IEEE-ACCESS), 2019.
– Iman Azimi, Tapio Pahikkala, Amir M. Rahmani, Hannakaisa Niela-Vilen, Anna Axelin, and Pasi Liljeberg, “Missing Data Resilient Decision-making for Healthcare IoT through Personalization: A Case Study on Maternal Health,” Elsevier Journal of Future Generation Computer Systems (Elsevier-FGCS), 2019.