Skip to main content

Explainable Deep Learning with Human Feedback for Perioperative Complications Prediction

  • Conference paper
  • First Online:
Advanced Intelligent Computing Technology and Applications (ICIC 2024)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14863))

Included in the following conference series:

  • 544 Accesses

Abstract

Health problems are very common among pregnant women, and seemingly normal pregnant women may experience physiological disorders, which can lead to perinatal complications, greatly endangering the health of pregnant women and their newborns. Timely identification, provision of relevant resources, and timely response are the key to preventing serious complications and mortality in delivery women. The current predictive models used in medicine have an imbalance between interpretability and accuracy. In addition, there is a lack of utilization of knowledge in the field of medical expert domain knowledge, which is a waste. The method proposed in this article combines deep learning with regular decision trees to ensure high accuracy while improving its interpretability. In addition, adding expert domain knowledge and providing additional useful information can improve model performance.

J. Wang and G. Wu—Contributed equally to this work.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Arabi Belaghi, R., Beyene, J., McDonald, S.D.: Prediction of preterm birth in nulliparous women using logistic regression and machine learning. PLoS ONE 16(6), e0252025 (2021)

    Article  Google Scholar 

  2. Sumathi, A., Meganathan, S.: Gestational diabetes mellitus (GDM data set) (2022). https://doi.org/10.34740/KAGGLE/DSV/3245285, https://www.kaggle.com/dsv/3245285

  3. Bertini, A., Salas, R., Chabert, S., Sobrevia, L., Pardo, F.: Using machine learning to predict complications in pregnancy: a systematic review. Front. Bioeng. Biotechnol. 9, 780389 (2022)

    Article  Google Scholar 

  4. Bogren, M., Denovan, A., Kent, F., Berg, M., Linden, K.: Impact of the helping mothers survive bleeding after birth learning programme on care provider skills and maternal health outcomes in low-income countries—an integrative review. Women Birth 34(5), 425–434 (2021)

    Article  Google Scholar 

  5. Che, Z., Kale, D., Li, W., Bahadori, M.T., Liu, Y.: Deep computational phenotyping. In: Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 507–516 (2015)

    Google Scholar 

  6. Chen, J.H., Asch, S.M.: Machine learning and prediction in medicine—beyond the peak of inflated expectations. N. Engl. J. Med. 376(26), 2507 (2017)

    Article  Google Scholar 

  7. Choi, E., Bahadori, M.T., Schuetz, A., Stewart, W.F., Sun, J.: Doctor AI: predicting clinical events via recurrent neural networks. In: Machine Learning for Healthcare Conference, pp. 301–318. PMLR (2016)

    Google Scholar 

  8. Correia, A.H., Lecue, F.: Human-in-the-loop feature selection. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, pp. 2438–2445 (2019)

    Google Scholar 

  9. Cubillos, G., et al.: Development of machine learning models to predict gestational diabetes risk in the first half of pregnancy. BMC Pregnancy Childbirth 23(1), 1–18 (2023)

    Article  Google Scholar 

  10. Doomah, Y.H., Xu, S.Y., Cao, L.X., Liang, S.L., Nuer-Allornuvor, G.F., Ying, X.Y.: A fuzzy expert system to predict the risk of postpartum hemorrhage. Acta Inform. Med. 27(5), 318 (2019)

    Article  Google Scholar 

  11. Finlayson, K., Crossland, N., Bonet, M., Downe, S.: What matters to women in the postnatal period: a meta-synthesis of qualitative studies. PLoS ONE 15(4), e0231415 (2020)

    Article  Google Scholar 

  12. Frizzell, J.D., et al.: Prediction of 30-day all-cause readmissions in patients hospitalized for heart failure: comparison of machine learning and other statistical approaches. JAMA cardiology 2(2), 204–209 (2017)

    Article  Google Scholar 

  13. Gupta, K., Balyan, K., Lamba, B., Puri, M., Sengupta, D., Kumar, M.: Ultrasound placental image texture analysis using artificial intelligence to predict hypertension in pregnancy. J. Matern. Fetal Neonatal Med. 35(25), 5587–5594 (2022)

    Article  Google Scholar 

  14. Jhee, J.H., et al.: Prediction model development of late-onset preeclampsia using machine learning-based methods. PLoS ONE 14(8), e0221202 (2019)

    Article  Google Scholar 

  15. Krishnamoorthy, S., Liu, Y., Liu, K.: A novel oppositional binary crow search algorithm with optimal machine learning based postpartum hemorrhage prediction model. BMC Pregnancy Childbirth 22(1), 560 (2022)

    Article  Google Scholar 

  16. Lipton, Z.C.: The mythos of model interpretability: in machine learning, the concept of interpretability is both important and slippery. Queue 16(3), 31–57 (2018)

    Article  Google Scholar 

  17. Liu, J., et al.: Machine learning-based prediction of postpartum hemorrhage after vaginal delivery: combining bleeding high risk factors and uterine contraction curve. Arch. Gynecol. Obstet. 306(4), 1015–1025 (2022)

    Article  Google Scholar 

  18. Malacova, E., et al.: Stillbirth risk prediction using machine learning for a large cohort of births from western Australia, 1980–2015. Sci. Rep. 10(1), 5354 (2020)

    Article  Google Scholar 

  19. de Marvao, A., Dawes, T.J., Howard, J.P., O’Regan, D.P.: Artificial intelligence and the cardiologist: what you need to know for 2020. Heart 106(5), 399–400 (2020)

    Article  Google Scholar 

  20. Mennickent, D., Rodríguez, A., Opazo, M., Riedel, C., Castro, E., Eriz-Salinas, A., et al.: Machine learning applied in maternal and fetal health: a narrative review focused on pregnancy diseases and complications. Front. Endocrinol. (Lausanne). 14, 1130139 (2023)

    Google Scholar 

  21. Mennickent, D., Rodríguez, A., Farías-Jofré, M., Araya, J., Guzmán-Gutiérrez, E.: Machine learning-based models for gestational diabetes mellitus prediction before 24–28 weeks of pregnancy: a review. Artif. Intell. Med. 132, 102378 (2022)

    Google Scholar 

  22. Miotto, R., Li, L., Kidd, B.A., Dudley, J.T.: Deep patient: an unsupervised representation to predict the future of patients from the electronic health records. Sci. Rep. 6(1), 1–10 (2016)

    Article  Google Scholar 

  23. Mosaraf, M.P.: Postpartum depression (2023). https://doi.org/10.34740/KAGGLE/DS/2830731, https://www.kaggle.com/ds/2830731

  24. Neary, C., Naheed, S., McLernon, D., Black, M.: Predicting risk of postpartum haemorrhage: a systematic review. BJOG: Int. J. Obstetr. Gynaecol. 128(1), 46–53 (2021)

    Google Scholar 

  25. Ragavi, V., Shanthi, P., Ananth, J., Aswathy, H.: A review on major complications in the pregnancies of women using deep learning algorithms. In: Predicting Pregnancy Complications Through Artificial Intelligence and Machine Learning, pp. 227–243 (2023)

    Google Scholar 

  26. Rezaeian, A., Rezaeian, M., Khatami, S.F., Khorashadizadeh, F., Moghaddam, F.P.: Prediction of mortality of premature neonates using neural network and logistic regression. J. Ambient Intell. Humaniz. Comput. 13(3), 1269–1277 (2022)

    Google Scholar 

  27. Sumathi, A., Meganathan, S.: Ensemble classifier technique to predict gestational diabetes mellitus (GDM). Comput. Syst. Sci. Eng. 40(1), 313–325 (2022)

    Article  Google Scholar 

  28. Sumathi, A., Meganathan, S., Ravisankar, B.V.: An intelligent gestational diabetes diagnosis model using deep stacked autoencoder. Comput. Mater. Continua 69(3), 3109–3126 (2021)

    Article  Google Scholar 

  29. Williams, R.J.: Simple statistical gradient-following algorithms for connectionist reinforcement learning. Mach. Learn. 8, 229–256 (1992)

    Article  Google Scholar 

  30. Wu, M., Hughes, M., Parbhoo, S., Zazzi, M., Roth, V., Doshi-Velez, F.: Beyond sparsity: tree regularization of deep models for interpretability. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018)

    Google Scholar 

  31. Wu, Y.T., et al.: Early prediction of gestational diabetes mellitus in the Chinese population via advanced machine learning. J. Clin. Endocrinol. Metab. 106(3), e1191–e1205 (2021)

    Article  Google Scholar 

Download references

Acknowledgments

This work is supported in part by the National Natural Science Funds for Distinguished Young Scholar under Grant 62325307, in part by the National Nature Science Foundation of China under Grants 62072315, 62073225, 62176164, and 62203134, in part by the Shenzhen Science and Technology Program under grants KCXFZ20230731094001003, JCYJ20210324093808021, and JCYJ2022053110281 7040, in part by the Scientific Instrument Developing Project of Shenzhen University under Grant 2023YQ019.

Author information

Authors and Affiliations

Authors

Corresponding authors

Correspondence to Yuantao Li or Jie Chen .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Wang, J. et al. (2024). Explainable Deep Learning with Human Feedback for Perioperative Complications Prediction. In: Huang, DS., Zhang, X., Pan, Y. (eds) Advanced Intelligent Computing Technology and Applications. ICIC 2024. Lecture Notes in Computer Science, vol 14863. Springer, Singapore. https://doi.org/10.1007/978-981-97-5581-3_17

Download citation

  • DOI: https://doi.org/10.1007/978-981-97-5581-3_17

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-97-5580-6

  • Online ISBN: 978-981-97-5581-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics