Dynamic model for early detection of preterm labor
##plugins.themes.bootstrap3.article.main##
Abstract
Preterm labor is a major challenge in maternal and neonatal health because it contributes to high rates of newborn morbidity and mortality. Early detection is crucial, but conventional static approaches often fail to identify risks accurately and in a timely manner. This study proposes the development of a dynamic machine learning-based preterm birth risk prediction model using the Long Short-Term Memory (LSTM) architecture combined with the Bayesian Updating approach. The model is designed to process multivariate time-series data from various clinical sources such as EHR (electronic medical record), EHG (electrohysterography), CTG (cardiotocography), and vital signals collected longitudinally during pregnancy. By leveraging LSTM's ability to capture long-term temporal relationships and Bayesian probabilistic renewal mechanisms, the model is able to provide real-time and adaptive estimates of preterm labor risk on a weekly basis. Risk prediction results are visualized in the form of interactive graphs with risk categorization (low, medium, high) to support fast and accurate clinical interpretation. The study used simulated data on 500 pregnant patients and showed that the system can adjust risk predictions as new data comes in. This research makes a significant contribution to the development of artificial intelligence-based clinical decision support systems for pregnancy monitoring. Going forward, integration with real clinical data and external validation in the hospital environment is expected to improve the accuracy and implementability of the system in daily medical practice.
##plugins.themes.bootstrap3.article.details##
How to Cite
Nugraeny, L., Suhartini, S., Sumiatik, S., & Handayani, P. (2025). Dynamic model for early detection of preterm labor. International Journal of Basic and Applied Science, 13(4), 202–216. https://doi.org/10.35335/ijobas.v13i4.678
References
J.-A. Quinn et al., “Preterm birth: Case definition & guidelines for data collection, analysis, and presentation of immunisation safety data,” Vaccine, vol. 34, no. 49, pp. 6047–6056, 2016, doi: https://doi.org/10.1016/j.vaccine.2016.03.045.
S. R. Walani, “Global burden of preterm birth,” Int. J. Gynecol. Obstet., vol. 150, no. 1, pp. 31–33, 2020, doi: https://doi.org/10.1002/ijgo.13195.
R. Menon, “Preterm birth: a global burden on maternal and child health,” Pathog. Glob. Health, vol. 106, no. 3, pp. 139–140, 2012, [Online]. Available: https://www.tandfonline.com/doi/full/10.1179/204777312X13462106637729
X. Liang, Y. Lyu, J. Li, Y. Li, and C. Chi, “Global, regional, and national burden of preterm birth, 1990–2021: a systematic analysis from the global burden of disease study 2021,” Eclinicalmedicine, vol. 76, no. 102840, pp. 1–16, 2024, [Online]. Available: https://www.thelancet.com/journals/eclinm/article/PIIS2589-5370(24)00419-X/fulltext
H. Honest et al., “Screening to prevent spontaneous preterm birth: systematic reviews of accuracy and effectiveness literature with economic modelling,” Heal. Technol Assess, vol. 13, no. 43, pp. 1–627, 2009, doi: https://doi.org/10.1108/cgij.2010.24815aae.003.
C. Espinosa et al., “Data-driven modeling of pregnancy-related complications,” Trends Mol. Med., vol. 27, no. 8, pp. 762–776, 2021, [Online]. Available: https://www.cell.com/trends/molecular-medicine/fulltext/S1471-4914(21)00039-3
J. D. Iams, “Prediction and early detection of preterm labor,” Obstet. Gynecol., vol. 101, no. 2, pp. 402–412, 2003, [Online]. Available: https://journals.lww.com/greenjournal/abstract/2003/02000/prediction_and_early_detection_of_preterm_labor.30.aspx
L. E. X. ARAÚJO, “THE CHALLENGE OF TIME IN CLINICAL AI: LONGITUDINAL PREDICTIONS WITH VARIABLE HISTORY,” NOVA University Lisbon, 2023. [Online]. Available: https://run.unl.pt/bitstream/10362/178295/1/Araujo_2023.pdf
Y.-W. Lin, “Machine learning and time-series analysis in healthcare,” University of Illinois at Urbana-Champaign, 2022. [Online]. Available: https://www.ideals.illinois.edu/items/125055
G. Tsang, “Time-Series Embedded Feature Selection Using Deep Learning: Data Mining Electronic Health Records for Novel Biomarkers,” Swansea University, 2022. [Online]. Available: http://csvision.swan.ac.uk/uploads/Site/Publication/thesis.gavin.22.pdf
M. del A. Díaz Martínez, “Biomarker identification based on human electrohysterography for the early detection of risk in different obstetric scenarios: preterm birth, induction of labour and postpartum,” Universitat Politècnica de València, 2024. [Online]. Available: https://riunet.upv.es/handle/10251/206155
R. H. Chowdhury, Q. D. Hossain, and M. Ahmad, “Automated Method for Uterine Contraction Extraction and Classification of Term vs. Pre-term EHG Signals,” IEEE Access, vol. 12, no. 1, pp. 49363–49375, 2024, doi: https://doi.org/10.1109/ACCESS.2024.3384258.
E. Tuncer, “Classification of Term and Preterm Birth Data from Elektrohisterogram (EHG) Data by Empirical Wavelet Transform Based Machine Learning Methods,” Balk. J. Electr. Comput. Eng., vol. 12, no. 2, pp. 119–126, 2024, doi: https://doi.org/10.17694/bajece.1405536.
T. R. Jossou et al., “N-beats as an EHG signal forecasting method for labour prediction in full term pregnancy,” Electronics, vol. 11, no. 22, p. 3739, 2022, doi: https://doi.org/10.3390/electronics11223739.
H. T. Nguyen et al., “Multivariate longitudinal data for survival analysis of cardiovascular event prediction in young adults: insights from a comparative explainable study,” BMC Med. Res. Methodol., vol. 23, no. 1, p. 23, 2023, [Online]. Available: https://link.springer.com/article/10.1186/s12874-023-01845-4
U. S. Kesmodel, “Cross‐sectional studies–what are they good for?,” Acta Obstet. Gynecol. Scand., vol. 97, no. 4, pp. 388–393, 2018, doi: https://doi.org/10.1111/aogs.13331.
C. Maier, J. B. Thatcher, V. Grover, and Y. K. Dwivedi, “Cross-sectional research: A critical perspective, use cases, and recommendations for IS research,” 2023, Elsevier. doi: https://doi.org/10.1016/j.ijinfomgt.2023.102625.
F. Nieto del Amor, “Design and assessment of a computer-assisted artificial intelligence system for predicting preterm labor in women attending regular check-ups. Emphasis in imbalance data learning technique,” Universitat Politècnica de València, 2023. [Online]. Available: https://riunet.upv.es/handle/10251/200900
J. Xu, Z. Chen, J. Zhang, Y. Lu, X. Yang, and A. Pumir, “Realistic preterm prediction based on optimized synthetic sampling of EHG signal,” Comput. Biol. Med., vol. 136, no. 2, p. 104644, 2021, doi: https://doi.org/10.1016/j.compbiomed.2021.104644.
L. Liu et al., “Wearable Sensors, Data Processing, and Artificial Intelligence in Pregnancy Monitoring: A Review,” Sensors, vol. 24, no. 19, p. 6426, 2024, doi: https://doi.org/10.3390/s24196426.
X. Wen and W. Li, “Time series prediction based on LSTM-attention-LSTM model,” IEEE access, vol. 11, no. 11, pp. 48322–48331, 2023, doi: https://doi.org/10.1109/ACCESS.2023.3276628.
X. Song et al., “Time-series well performance prediction based on Long Short-Term Memory (LSTM) neural network model,” J. Pet. Sci. Eng., vol. 186, no. 2, p. 106682, 2020, doi: https://doi.org/10.1016/j.petrol.2019.106682.
S. A. Ebrahim, J. Poshtan, S. M. Jamali, and N. A. Ebrahim, “Quantitative and qualitative analysis of time-series classification using deep learning,” IEEE Access, vol. 8, no. 2, pp. 90202–90215, 2020, doi: https://doi.org/10.1109/ACCESS.2020.2993538.
G. Ciaburro and G. Iannace, “Machine learning-based algorithms to knowledge extraction from time series data: A review,” Data, vol. 6, no. 6, p. 55, 2021, doi: https://doi.org/10.3390/data6060055.
I. H. Sarker, H. Alqahtani, F. Alsolami, A. I. Khan, Y. B. Abushark, and M. K. Siddiqui, “Context pre-modeling: an empirical analysis for classification based user-centric context-aware predictive modeling,” J. Big Data, vol. 7, no. 51, pp. 1–23, 2020, [Online]. Available: https://link.springer.com/article/10.1186/s40537-020-00328-3
N. Pindya, “Optimizing Data Quality with a Scoring-Based Cleansing Framework,” University of Twente, 2024. [Online]. Available: https://purl.utwente.nl/essays/103281
H. Li et al., “FetalCare: A Home Telemonitoring System for Wearable Fetal-ECG and EHG Acquisition During Pregnancy,” IEEE Sens. J., vol. 9, no. 1, pp. 15175–15186, 2024, doi: https://doi.org/10.1109/JSEN.2024.3374332.
S. Hochreiter and J. Schmidhuber, “Long short-term memory,” Neural Comput., vol. 9, no. 8, pp. 1735–1780, 1997, doi: https://doi.org/10.1162/neco.1997.9.8.1735.
R. AlSaad, Q. Malluhi, and S. Boughorbel, “PredictPTB: an interpretable preterm birth prediction model using attention-based recurrent neural networks,” BioData Min., vol. 15, no. 1, p. 6, 2022, [Online]. Available: https://link.springer.com/article/10.1186/s13040-022-00289-8
D. Asfaw, I. Jordanov, L. Impey, A. Namburete, R. Lee, and A. Georgieva, “Multimodal Deep Learning for Predicting Adverse Birth Outcomes Based on Early Labour Data,” 2023. doi: 10.3390/bioengineering10060730.
Y. Zhang, S. Lu, Y. Wu, W. Hu, and Z. Yuan, “The prediction of preterm birth using time-series technology-based machine learning: retrospective cohort study,” JMIR Med. Informatics, vol. 10, no. 6, p. e33835, 2022, doi: https://doi.org/10.2196/33835.
S. R. Walani, “Global burden of preterm birth,” Int. J. Gynecol. Obstet., vol. 150, no. 1, pp. 31–33, 2020, doi: https://doi.org/10.1002/ijgo.13195.
R. Menon, “Preterm birth: a global burden on maternal and child health,” Pathog. Glob. Health, vol. 106, no. 3, pp. 139–140, 2012, [Online]. Available: https://www.tandfonline.com/doi/full/10.1179/204777312X13462106637729
X. Liang, Y. Lyu, J. Li, Y. Li, and C. Chi, “Global, regional, and national burden of preterm birth, 1990–2021: a systematic analysis from the global burden of disease study 2021,” Eclinicalmedicine, vol. 76, no. 102840, pp. 1–16, 2024, [Online]. Available: https://www.thelancet.com/journals/eclinm/article/PIIS2589-5370(24)00419-X/fulltext
H. Honest et al., “Screening to prevent spontaneous preterm birth: systematic reviews of accuracy and effectiveness literature with economic modelling,” Heal. Technol Assess, vol. 13, no. 43, pp. 1–627, 2009, doi: https://doi.org/10.1108/cgij.2010.24815aae.003.
C. Espinosa et al., “Data-driven modeling of pregnancy-related complications,” Trends Mol. Med., vol. 27, no. 8, pp. 762–776, 2021, [Online]. Available: https://www.cell.com/trends/molecular-medicine/fulltext/S1471-4914(21)00039-3
J. D. Iams, “Prediction and early detection of preterm labor,” Obstet. Gynecol., vol. 101, no. 2, pp. 402–412, 2003, [Online]. Available: https://journals.lww.com/greenjournal/abstract/2003/02000/prediction_and_early_detection_of_preterm_labor.30.aspx
L. E. X. ARAÚJO, “THE CHALLENGE OF TIME IN CLINICAL AI: LONGITUDINAL PREDICTIONS WITH VARIABLE HISTORY,” NOVA University Lisbon, 2023. [Online]. Available: https://run.unl.pt/bitstream/10362/178295/1/Araujo_2023.pdf
Y.-W. Lin, “Machine learning and time-series analysis in healthcare,” University of Illinois at Urbana-Champaign, 2022. [Online]. Available: https://www.ideals.illinois.edu/items/125055
G. Tsang, “Time-Series Embedded Feature Selection Using Deep Learning: Data Mining Electronic Health Records for Novel Biomarkers,” Swansea University, 2022. [Online]. Available: http://csvision.swan.ac.uk/uploads/Site/Publication/thesis.gavin.22.pdf
M. del A. Díaz Martínez, “Biomarker identification based on human electrohysterography for the early detection of risk in different obstetric scenarios: preterm birth, induction of labour and postpartum,” Universitat Politècnica de València, 2024. [Online]. Available: https://riunet.upv.es/handle/10251/206155
R. H. Chowdhury, Q. D. Hossain, and M. Ahmad, “Automated Method for Uterine Contraction Extraction and Classification of Term vs. Pre-term EHG Signals,” IEEE Access, vol. 12, no. 1, pp. 49363–49375, 2024, doi: https://doi.org/10.1109/ACCESS.2024.3384258.
E. Tuncer, “Classification of Term and Preterm Birth Data from Elektrohisterogram (EHG) Data by Empirical Wavelet Transform Based Machine Learning Methods,” Balk. J. Electr. Comput. Eng., vol. 12, no. 2, pp. 119–126, 2024, doi: https://doi.org/10.17694/bajece.1405536.
T. R. Jossou et al., “N-beats as an EHG signal forecasting method for labour prediction in full term pregnancy,” Electronics, vol. 11, no. 22, p. 3739, 2022, doi: https://doi.org/10.3390/electronics11223739.
H. T. Nguyen et al., “Multivariate longitudinal data for survival analysis of cardiovascular event prediction in young adults: insights from a comparative explainable study,” BMC Med. Res. Methodol., vol. 23, no. 1, p. 23, 2023, [Online]. Available: https://link.springer.com/article/10.1186/s12874-023-01845-4
U. S. Kesmodel, “Cross‐sectional studies–what are they good for?,” Acta Obstet. Gynecol. Scand., vol. 97, no. 4, pp. 388–393, 2018, doi: https://doi.org/10.1111/aogs.13331.
C. Maier, J. B. Thatcher, V. Grover, and Y. K. Dwivedi, “Cross-sectional research: A critical perspective, use cases, and recommendations for IS research,” 2023, Elsevier. doi: https://doi.org/10.1016/j.ijinfomgt.2023.102625.
F. Nieto del Amor, “Design and assessment of a computer-assisted artificial intelligence system for predicting preterm labor in women attending regular check-ups. Emphasis in imbalance data learning technique,” Universitat Politècnica de València, 2023. [Online]. Available: https://riunet.upv.es/handle/10251/200900
J. Xu, Z. Chen, J. Zhang, Y. Lu, X. Yang, and A. Pumir, “Realistic preterm prediction based on optimized synthetic sampling of EHG signal,” Comput. Biol. Med., vol. 136, no. 2, p. 104644, 2021, doi: https://doi.org/10.1016/j.compbiomed.2021.104644.
L. Liu et al., “Wearable Sensors, Data Processing, and Artificial Intelligence in Pregnancy Monitoring: A Review,” Sensors, vol. 24, no. 19, p. 6426, 2024, doi: https://doi.org/10.3390/s24196426.
X. Wen and W. Li, “Time series prediction based on LSTM-attention-LSTM model,” IEEE access, vol. 11, no. 11, pp. 48322–48331, 2023, doi: https://doi.org/10.1109/ACCESS.2023.3276628.
X. Song et al., “Time-series well performance prediction based on Long Short-Term Memory (LSTM) neural network model,” J. Pet. Sci. Eng., vol. 186, no. 2, p. 106682, 2020, doi: https://doi.org/10.1016/j.petrol.2019.106682.
S. A. Ebrahim, J. Poshtan, S. M. Jamali, and N. A. Ebrahim, “Quantitative and qualitative analysis of time-series classification using deep learning,” IEEE Access, vol. 8, no. 2, pp. 90202–90215, 2020, doi: https://doi.org/10.1109/ACCESS.2020.2993538.
G. Ciaburro and G. Iannace, “Machine learning-based algorithms to knowledge extraction from time series data: A review,” Data, vol. 6, no. 6, p. 55, 2021, doi: https://doi.org/10.3390/data6060055.
I. H. Sarker, H. Alqahtani, F. Alsolami, A. I. Khan, Y. B. Abushark, and M. K. Siddiqui, “Context pre-modeling: an empirical analysis for classification based user-centric context-aware predictive modeling,” J. Big Data, vol. 7, no. 51, pp. 1–23, 2020, [Online]. Available: https://link.springer.com/article/10.1186/s40537-020-00328-3
N. Pindya, “Optimizing Data Quality with a Scoring-Based Cleansing Framework,” University of Twente, 2024. [Online]. Available: https://purl.utwente.nl/essays/103281
H. Li et al., “FetalCare: A Home Telemonitoring System for Wearable Fetal-ECG and EHG Acquisition During Pregnancy,” IEEE Sens. J., vol. 9, no. 1, pp. 15175–15186, 2024, doi: https://doi.org/10.1109/JSEN.2024.3374332.
S. Hochreiter and J. Schmidhuber, “Long short-term memory,” Neural Comput., vol. 9, no. 8, pp. 1735–1780, 1997, doi: https://doi.org/10.1162/neco.1997.9.8.1735.
R. AlSaad, Q. Malluhi, and S. Boughorbel, “PredictPTB: an interpretable preterm birth prediction model using attention-based recurrent neural networks,” BioData Min., vol. 15, no. 1, p. 6, 2022, [Online]. Available: https://link.springer.com/article/10.1186/s13040-022-00289-8
D. Asfaw, I. Jordanov, L. Impey, A. Namburete, R. Lee, and A. Georgieva, “Multimodal Deep Learning for Predicting Adverse Birth Outcomes Based on Early Labour Data,” 2023. doi: 10.3390/bioengineering10060730.
Y. Zhang, S. Lu, Y. Wu, W. Hu, and Z. Yuan, “The prediction of preterm birth using time-series technology-based machine learning: retrospective cohort study,” JMIR Med. Informatics, vol. 10, no. 6, p. e33835, 2022, doi: https://doi.org/10.2196/33835.
Copyright and Licensing
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.