Authors:
Muhammad Abubakar
1
;
Amina Bibi
1
;
Rashad Hussain
1
;
Zohra Bibi
1
;
Asma Gul
2
;
Zahid Bashir
2
;
Salman Noshear Arshad
1
;
Momin Ayub Uppal
1
and
Safee Ullah Chaudhary
1
Affiliations:
1
Lahore University of Management Sciences, Pakistan
;
2
Shalamar Institute of Health Sciences, Pakistan
Keyword(s):
Antenatal Care, Mobile Health, Smartphone Health Monitoring, Decision Support Systems.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Business Analytics
;
Cardiovascular Technologies
;
Cloud Computing
;
Computing and Telecommunications in Cardiology
;
Data Engineering
;
Decision Support Systems
;
Decision Support Systems, Remote Data Analysis
;
Devices
;
Distributed and Mobile Software Systems
;
e-Health
;
Health Engineering and Technology Applications
;
Health Information Systems
;
Healthcare Management Systems
;
Human-Computer Interaction
;
Knowledge-Based Systems
;
Mobile Technologies
;
Mobile Technologies for Healthcare Applications
;
Neural Rehabilitation
;
Neurotechnology, Electronics and Informatics
;
Physiological Computing Systems
;
Platforms and Applications
;
Software Engineering
;
Symbolic Systems
;
Wearable Sensors and Systems
Abstract:
The provision of Antenatal Care (ANC) for pregnant women plays a vital role in ensuring infant and maternal health. Limited access to antenatal care in Low and Middle Income Countries (LMIC) results in high Infant and Maternal Mortality Rate (IMR and MMR, respectively). In this work, we propose a cloud-based clinical Decision Support System (DSS) integrated with a wearable health-sensor network for patient self-diagnosis and real time health monitoring. Patient assessment is performed by evaluating the human-input coupled with sensor-generated symptomatic information using a Bayesian network driven DSS. High risk pregnancies can be identified and monitored along with dispensing of consultant advice directly to the patient. Patient and disease incidence data is stored on the cloud for tuning probabilities of the Bayesian network towards improving accuracy of predicting anomalies within the epidemiological context. The system therefore, aims to control IMR and MMR by providing ubiquito
us access to ANC in LMICs. A scaled-up implementation of the proposed system can help reduce patient influx at the limited tertiary care centers by referring low-risk cases to primary or secondary care establishments.
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