TY - GEN
T1 - Fuzzy inference system for risk evaluation in gestational diabetes mellitus
AU - Sanchez, Carlos Salort
AU - Smyth, Suzanne
AU - Tully, Elizabeth
AU - Griffin, Joanna
AU - Heaphy, Luke
AU - Redmond, Niamh
AU - Breathnach, Fionnuala
AU - Baumbach, Jan
AU - Axenie, Cristian
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/10
Y1 - 2019/10
N2 - Remote monitoring health data analysis holds the potential to reduce pregnancy complications, improve patients' quality of life, enhance the efficiency of healthcare delivery and reduce healthcare costs. In this paper, we present a method based on fuzzy inference systems to monitor pregnancies complicated by gestational diabetes mellitus (GDM). The system is simple, fast, flexible and exploits domain expertise in assessing risk levels according to capillary glucose levels from women with GDM. We show that this approach generates an interpretable input, which is valuable in medical applications. To prove the capabilities of the system, we present prediction results from 50 real-world patients and show that the system obtains relevant glycaemic-control data comparable to current monitoring methods that rely on periodic face-to-face physician review. Our systems achieves 95% accuracy. Moreover, we show that the difference in predictions account for a more personalized treatment.
AB - Remote monitoring health data analysis holds the potential to reduce pregnancy complications, improve patients' quality of life, enhance the efficiency of healthcare delivery and reduce healthcare costs. In this paper, we present a method based on fuzzy inference systems to monitor pregnancies complicated by gestational diabetes mellitus (GDM). The system is simple, fast, flexible and exploits domain expertise in assessing risk levels according to capillary glucose levels from women with GDM. We show that this approach generates an interpretable input, which is valuable in medical applications. To prove the capabilities of the system, we present prediction results from 50 real-world patients and show that the system obtains relevant glycaemic-control data comparable to current monitoring methods that rely on periodic face-to-face physician review. Our systems achieves 95% accuracy. Moreover, we show that the difference in predictions account for a more personalized treatment.
KW - E-Health
KW - Fuzzy Inference System
KW - Gestational Diabetes Mellitus
UR - http://www.scopus.com/inward/record.url?scp=85078575463&partnerID=8YFLogxK
U2 - 10.1109/BIBE.2019.00177
DO - 10.1109/BIBE.2019.00177
M3 - Conference contribution
AN - SCOPUS:85078575463
T3 - Proceedings - 2019 IEEE 19th International Conference on Bioinformatics and Bioengineering, BIBE 2019
SP - 947
EP - 952
BT - Proceedings - 2019 IEEE 19th International Conference on Bioinformatics and Bioengineering, BIBE 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 19th International Conference on Bioinformatics and Bioengineering, BIBE 2019
Y2 - 28 October 2019 through 30 October 2019
ER -
