Prediction of a due date based on the pregnancy history data using machine learning. / Metsker, Oleg; Kopanitsa, Georgy; Komlichenko, Eduard; Yanushanets, Maria; Bolgova, Ekaterina.
pHealth 2020 - Proceedings of the 17th International Conference on Wearable Micro and Nano Technologies for Personalized Health. ed. / Bernd Blobel; Lenka Lhotska; Peter Pharow; Filipe Sousa. IOS Press, 2020. p. 104-108 (Studies in Health Technology and Informatics; Vol. 273).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Research › peer-review
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TY - GEN
T1 - Prediction of a due date based on the pregnancy history data using machine learning
AU - Metsker, Oleg
AU - Kopanitsa, Georgy
AU - Komlichenko, Eduard
AU - Yanushanets, Maria
AU - Bolgova, Ekaterina
N1 - Publisher Copyright: © 2020 The authors and IOS Press.
PY - 2020/9/4
Y1 - 2020/9/4
N2 - Prediction of a labor due date is important especially for the pregnancies with high risk of complications where a special treatment is needed. This is especially valid in the countries with multilevel health care institutions like Russia. In Russia medical organizations are distributed into national, regional and municipal levels. Organizations of each level can provide treatment of different types and quality. For example, pregnancies with low risk of complications are routed to the municipal hospitals, moderate risk pregnancies are routed to the reginal and high risk of complications are routed to the hospitals of the national level. In the situation of resource deficiency especially on the national level it is necessary to plan admission date and a treatment team in advance to provide the best possible care. When pregnancy data is not standardized and semantically interoperable, data driven models. We have retrospectively analyzed electronic health records from the perinatal Center of the Almazov perinatal medical center in Saint-Petersburg, Russia. The dataset was exported from the medical information system. It consisted of structured and semi structured data with the total of 73115 lines for 12989 female patients. The proposed due date prediction data-driven model allows a high accuracy prediction to allow proper resource planning. The models are based on the real-world evidence and can be applied with limited amount of predictors.
AB - Prediction of a labor due date is important especially for the pregnancies with high risk of complications where a special treatment is needed. This is especially valid in the countries with multilevel health care institutions like Russia. In Russia medical organizations are distributed into national, regional and municipal levels. Organizations of each level can provide treatment of different types and quality. For example, pregnancies with low risk of complications are routed to the municipal hospitals, moderate risk pregnancies are routed to the reginal and high risk of complications are routed to the hospitals of the national level. In the situation of resource deficiency especially on the national level it is necessary to plan admission date and a treatment team in advance to provide the best possible care. When pregnancy data is not standardized and semantically interoperable, data driven models. We have retrospectively analyzed electronic health records from the perinatal Center of the Almazov perinatal medical center in Saint-Petersburg, Russia. The dataset was exported from the medical information system. It consisted of structured and semi structured data with the total of 73115 lines for 12989 female patients. The proposed due date prediction data-driven model allows a high accuracy prediction to allow proper resource planning. The models are based on the real-world evidence and can be applied with limited amount of predictors.
KW - Due Date
KW - Machine learning
KW - Prediction
KW - Pregnancy
KW - Random Forest
UR - http://www.scopus.com/inward/record.url?scp=85092437474&partnerID=8YFLogxK
U2 - 10.3233/SHTI200622
DO - 10.3233/SHTI200622
M3 - Conference contribution
C2 - 33087598
AN - SCOPUS:85092437474
T3 - Studies in Health Technology and Informatics
SP - 104
EP - 108
BT - pHealth 2020 - Proceedings of the 17th International Conference on Wearable Micro and Nano Technologies for Personalized Health
A2 - Blobel, Bernd
A2 - Lhotska, Lenka
A2 - Pharow, Peter
A2 - Sousa, Filipe
PB - IOS Press
T2 - 17th International Conference on Wearable Micro and Nano Technologies for Personalized Health, pHealth 2020
Y2 - 14 September 2020 through 16 September 2020
ER -
ID: 87784125