Skip to main content
Springer Nature Link
Log in

Machine Learning Models to Analyze the Effect of Drugs on Neonatal-ICU Length of Stay

  • Conference paper
Applied Intelligence and Informatics (AII 2022)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1724))

Included in the following conference series:

  • 600 Accesses

Abstract

The Neonatal intensive care unit (NICU) is a specialized section for newborn babies. The neonates are in vulnerable conditions in the ICU, so the predictive models will help to indicate the seriousness of the patients and assist the doctors in taking immediate actions. The Medical Information Mart for Intensive Care III (MIMIC-III) dataset is used in this research. The medicines medicated in critical newborn children were detected, and how the drugs and the doses of drugs can affect the Length of Stay (LOS) in NICU is analyzed. The predictive result of ICU Length of Stay (LOS) for the patients admitted to NICU for seven days is analyzed. Different Machine Learning algorithms were implemented for developing the classification model, and Logistic Regression Algorithm performed well and showed an F1 score of about 85%, which was better than the F1 score of the deep learning model long Short-Term Memory (LSTM). The automated Machine Learning (AutoML) tool, AutoNLP was also implemented for classifying LOS. But traditional methods demonstrated better performance in comparison to AutoML.

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

Access this chapter

Log in via an institution

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. Adiba, F.I., Islam, T., Kaiser, M.S., Mahmud, M., Rahman, M.A.: Effect of corpora on classification of fake news using Naive Bayes classifier. Int. J. Autom. AI Mach. Learn. (IJAAIML) 1(1) (2020). https://researchlakejournals.com/index.php/AAIML/article/view/45/32

  2. Adiba, F.I., Sharwardy, S.N., Rahman, M.Z.: Multivariate time series prediction of pediatric ICU data using deep learning. In: 2021 International Conference on Innovative Trends in Information Technology (ICITIIT), pp. 1–6 (2021). https://doi.org/10.1109/ICITIIT51526.2021.9399593

  3. Ahmed, S., Hossain, M.F., Nur, S.B., Shamim Kaiser, M., Mahmud, M.: Toward machine learning-based psychological assessment of autism spectrum disorders in school and community. In: Kaiser, M.S., Bandyopadhyay, A., Ray, K., Singh, R., Nagar, V. (eds.) Proceedings of Trends in Electronics and Health Informatics. LNNS, vol. 376, pp. 139–149. Springer, Singapore (2022). https://doi.org/10.1007/978-981-16-8826-3_13

    Chapter  Google Scholar 

  4. Niamat Ullah Akhund, T.M., Mahi, M.J.N., Hasnat Tanvir, A.N.M., Mahmud, M., Kaiser, M.S.: ADEPTNESS: Alzheimer’s disease patient management system using pervasive sensors - early prototype and preliminary results. In: Wang, S., et al. (eds.) BI 2018. LNCS (LNAI), vol. 11309, pp. 413–422. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-05587-5_39

    Chapter  Google Scholar 

  5. Akter, T., Ali, M.H., Satu, M.S., Khan, M.I., Mahmud, M.: Towards autism subtype detection through identification of discriminatory factors using machine learning. In: Mahmud, M., Kaiser, M.S., Vassanelli, S., Dai, Q., Zhong, N. (eds.) BI 2021. LNCS (LNAI), vol. 12960, pp. 401–410. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-86993-9_36

    Chapter  Google Scholar 

  6. Al Banna, M.H., Ghosh, T., Taher, K.A., Kaiser, M.S., Mahmud, M.: A monitoring system for patients of autism spectrum disorder using artificial intelligence. In: Mahmud, M., Vassanelli, S., Kaiser, M.S., Zhong, N. (eds.) BI 2020. LNCS (LNAI), vol. 12241, pp. 251–262. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-59277-6_23

    Chapter  Google Scholar 

  7. Al Mamun, S., Kaiser, M.S., Mahmud, M.: An artificial intelligence based approach towards inclusive healthcare provisioning in society 5.0: a perspective on brain disorder. In: Mahmud, M., Kaiser, M.S., Vassanelli, S., Dai, Q., Zhong, N. (eds.) BI 2021. LNCS (LNAI), vol. 12960, pp. 157–169. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-86993-9_15

    Chapter  Google Scholar 

  8. Johnson, A., Pollard, T., Mark, R.: MIMIC-III clinical database demo (version 1.4) (2019). https://doi.org/10.13026/C2HM2Q

  9. Alsinglawi, B., et al.: Predicting length of stay for cardiovascular hospitalizations in the intensive care unit: machine learning approach. In: 2020 42nd Annual International Conference of the IEEE Engineering in Medicine Biology Society (EMBC), pp. 5442–5445 (2020). https://doi.org/10.1109/EMBC44109.2020.9175889

  10. AutoML.org: What is AutoML? https://www.automl.org/automl/. Accessed 31 Mar 2022

  11. Bhapkar, H.R., Mahalle, P.N., Shinde, G.R., Mahmud, M.: Rough sets in COVID-19 to predict symptomatic cases. In: Santosh, K.C., Joshi, A. (eds.) COVID-19: Prediction, Decision-Making, and its Impacts. LNDECT, vol. 60, pp. 57–68. Springer, Singapore (2021). https://doi.org/10.1007/978-981-15-9682-7_7

    Chapter  Google Scholar 

  12. Bird, S., Klein, E., Loper, E.: Natural Language Processing with Python: Analyzing Text with the Natural Language Toolkit. O’Reilly Media (2009). https://books.google.com.bd/books?id=KGIbfiiP1i4C

  13. Bishop, C.M.: Pattern Recognition and Machine Learning (Information Science and Statistics). Springer, Heidelberg (2006)

    MATH  Google Scholar 

  14. Biswas, M., Kaiser, M.S., Mahmud, M., Al Mamun, S., Hossain, M.S., Rahman, M.A.: An XAI based autism detection: the context behind the detection. In: Mahmud, M., Kaiser, M.S., Vassanelli, S., Dai, Q., Zhong, N. (eds.) BI 2021. LNCS (LNAI), vol. 12960, pp. 448–459. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-86993-9_40

    Chapter  Google Scholar 

  15. Biswas, M., et al.: Indoor navigation support system for patients with neurodegenerative diseases. In: Mahmud, M., Kaiser, M.S., Vassanelli, S., Dai, Q., Zhong, N. (eds.) BI 2021. LNCS (LNAI), vol. 12960, pp. 411–422. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-86993-9_37

    Chapter  Google Scholar 

  16. Biswas, M., et al.: ACCU\(^3\)RATE: a mobile health application rating scale based on user reviews. PLoS ONE 16(12), e0258050 (2021)

    Article  Google Scholar 

  17. Chen, T., et al.: A dominant set-informed interpretable fuzzy system for automated diagnosis of dementia. Front. Neurosci. 16, Article ID 867664 (2022)

    Google Scholar 

  18. Das, T.R., Hasan, S., Sarwar, S.M., Das, J.K., Rahman, M.A.: Facial spoof detection using support vector machine. In: Kaiser, M.S., Bandyopadhyay, A., Mahmud, M., Ray, K. (eds.) Proceedings of International Conference on Trends in Computational and Cognitive Engineering. AISC, vol. 1309, pp. 615–625. Springer, Singapore (2021). https://doi.org/10.1007/978-981-33-4673-4_50

    Chapter  Google Scholar 

  19. Deepa, B., Murugappan, M., Sumithra, M., Mahmud, M., Al-Rakhami, M.S.: Pattern descriptors orientation and map firefly algorithm based brain pathology classification using hybridized machine learning algorithm. IEEE Access 10, 3848–3863 (2021)

    Article  Google Scholar 

  20. Drugs.com. https://www.drugs.com/search.php?searchterm=heparin &a=1. Accessed 25 Mar 2022

  21. Elaziz, M., Al-qaness, M., Ewees, A., Dahou, A.: Recent Advances in NLP: The Case of Arabic Language. Studies in Computational Intelligence. Springer, Cham (2019). https://books.google.com.bd/books?id=HQ3BDwAAQBAJ

  22. Farhin, F., Kaiser, M.S., Mahmud, M.: Secured smart healthcare system: blockchain and Bayesian inference based approach. In: Kaiser, M.S., Bandyopadhyay, A., Mahmud, M., Ray, K. (eds.) Proceedings of International Conference on Trends in Computational and Cognitive Engineering. AISC, vol. 1309, pp. 455–465. Springer, Singapore (2021). https://doi.org/10.1007/978-981-33-4673-4_36

    Chapter  Google Scholar 

  23. Ferdous, H., Siraj, T., Setu, S.J., Anwar, M.M., Rahman, M.A.: Machine learning approach towards satellite image classification. In: Kaiser, M.S., Bandyopadhyay, A., Mahmud, M., Ray, K. (eds.) Proceedings of International Conference on Trends in Computational and Cognitive Engineering. AISC, vol. 1309, pp. 627–637. Springer, Singapore (2021). https://doi.org/10.1007/978-981-33-4673-4_51

    Chapter  Google Scholar 

  24. Gentimis, T., Alnaser, A.J., Durante, A., Cook, K., Steele, R.: Predicting hospital length of stay using neural networks on MIMIC III data. In: 2017 IEEE 15th International Conference on Dependable, Autonomic and Secure Computing, 15th International Conference on Pervasive Intelligence and Computing, 3rd International Conference on Big Data Intelligence and Computing and Cyber Science and Technology Congress (DASC/PiCom/DataCom/CyberSciTech), pp. 1194–1201 (2017). https://doi.org/10.1109/DASC-PICom-DataCom-CyberSciTec.2017.191

  25. Ghosh, T., et al.: Artificial intelligence and internet of things in screening and management of autism spectrum disorder. Sustain. Cities Soc. 74, 103189 (2021)

    Article  Google Scholar 

  26. Health Science Center: The Neonatal Intensive Care Unit (NICU). https://www.stanfordchildrens.org/en/topic/default?id=the-neonatal-intensive-care-unit-nicu-90-P02389. Accessed 24 Mar 2022

  27. Jesmin, S., Kaiser, M.S., Mahmud, M.: Artificial and internet of healthcare things based Alzheimer care during COVID 19. In: Mahmud, M., Vassanelli, S., Kaiser, M.S., Zhong, N. (eds.) BI 2020. LNCS (LNAI), vol. 12241, pp. 263–274. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-59277-6_24

    Chapter  Google Scholar 

  28. Jesmin, S., Kaiser, M.S., Mahmud, M.: Towards artificial intelligence driven stress monitoring for mental wellbeing tracking during Covid-19. In: Proceedings of the WI-IAT, pp. 845–851 (2020)

    Google Scholar 

  29. Johnson, A.E., et al.: MIMIC-III, a freely accessible critical care database. Sci. Data 3, 160035 (2016). https://doi.org/10.1038/sdata.2016.35

    Article  Google Scholar 

  30. Kaiser, M.S., et al.: 6G access network for intelligent internet of healthcare things: opportunity, challenges, and research directions. In: Kaiser, M.S., Bandyopadhyay, A., Mahmud, M., Ray, K. (eds.) Proceedings of International Conference on Trends in Computational and Cognitive Engineering. AISC, vol. 1309, pp. 317–328. Springer, Singapore (2021). https://doi.org/10.1007/978-981-33-4673-4_25

    Chapter  Google Scholar 

  31. Kumar, I., et al.: Dense tissue pattern characterization using deep neural network. Cogn. Comput. 14, 1–24 (2022). https://doi.org/10.1007/s12559-021-09970-2

    Article  Google Scholar 

  32. Kumar, S., et al.: Forecasting major impacts of Covid-19 pandemic on country-driven sectors: challenges, lessons, and future roadmap. Pers. Ubiquitous Comput. 1–24 (2021)

    Google Scholar 

  33. Lalotra, G.S., Kumar, V., Bhatt, A., Chen, T., Mahmud, M.: iReTADS: an intelligent real-time anomaly detection system for cloud communications using temporal data summarization and neural network. Secur. Commun. Netw. 2022, 1–15, Article ID 9149164 (2022)

    Google Scholar 

  34. Scikit learn: sklearn.feature_extraction.text.tfidfvectorizer. https://scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.text.TfidfVectorizer.html. Accessed 5 Jan 2022

  35. Ma, X., Si, Y., Wang, Z., Wang, Y.: Length of stay prediction for ICU patients using individualized single classification algorithm. Comput. Methods Programs Biomed. 186, 105224 (2020). https://doi.org/10.1016/j.cmpb.2019.105224. https://www.sciencedirect.com/science/article/pii/S0169260719316529

  36. Mahima, K.T.Y., Ginige, T.N.D.S., De Zoysa, K.: Evaluation of sentiment analysis based on AutoML and traditional approaches. Int. J. Adv. Comput. Sci. Appl. 12(2) (2021). https://doi.org/10.14569/IJACSA.2021.0120277

  37. Mahmud, M., Kaiser, M.S.: Machine learning in fighting pandemics: a COVID-19 case study. In: Santosh, K.C., Joshi, A. (eds.) COVID-19: Prediction, Decision-Making, and its Impacts. LNDECT, vol. 60, pp. 77–81. Springer, Singapore (2021). https://doi.org/10.1007/978-981-15-9682-7_9

    Chapter  Google Scholar 

  38. Mahmud, M., Kaiser, M.S., Hussain, A., Vassanelli, S.: Applications of deep learning and reinforcement learning to biological data. IEEE Trans. Neural Netw. Learn. Syst. 29(6), 2063–2079 (2018)

    Article  MathSciNet  Google Scholar 

  39. Mahmud, M., et al.: Towards explainable and privacy-preserving artificial intelligence for personalisation in autism spectrum disorder. In: Antona, M., Stephanidis, C. (eds.) HCII 2022. LNCS, pp. 356–370. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-05039-8_26

    Chapter  Google Scholar 

  40. Mammoottil, M.J., Kulangara, L.J., Cherian, A.S., Mohandas, P., Hasikin, K., Mahmud, M.: Detection of breast cancer from five-view thermal images using convolutional neural networks. J. Healthcare Eng. 2022, Article ID 4295221 (2022)

    Google Scholar 

  41. Mansouri, A., Noei, M., Abadeh, M.S.: Predicting hospital length of stay of neonates admitted to the NICU using data mining techniques. In: 2020 10th International Conference on Computer and Knowledge Engineering (ICCKE), pp. 629–635 (2020). https://doi.org/10.1109/ICCKE50421.2020.9303666

  42. MIT-LCP: MIMIC-III documentation. https://mimic.mit.edu/docs/iii/. Accessed 2 Jan 2022

  43. Mukherjee, H., et al.: Automatic lung health screening using respiratory sounds. J. Med. Syst. 45(2), 1–9 (2021)

    Article  MathSciNet  Google Scholar 

  44. Mukherjee, P., Bhattacharyya, I., Mullick, M., Kumar, R., Roy, N.D., Mahmud, M.: iConDet: an intelligent portable healthcare app for the detection of conjunctivitis. In: Mahmud, M., Kaiser, M.S., Kasabov, N., Iftekharuddin, K., Zhong, N. (eds.) AII 2021. CCIS, vol. 1435, pp. 29–42. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-82269-9_3

    Chapter  Google Scholar 

  45. Nahiduzzaman, M., Tasnim, M., Newaz, N.T., Kaiser, M.S., Mahmud, M.: Machine learning based early fall detection for elderly people with neurological disorder using multimodal data fusion. In: Mahmud, M., Vassanelli, S., Kaiser, M.S., Zhong, N. (eds.) BI 2020. LNCS (LNAI), vol. 12241, pp. 204–214. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-59277-6_19

    Chapter  Google Scholar 

  46. UAPR Office: Counting what matters - MPDSR report maternal and perinatal death surveillance and response systems in Asia-pacific during the Covid-19 pandemic. https://asiapacific.unfpa.org/en/counting-what-matters-MPDSR-report. Accessed 2 Feb 2022

  47. Paul, A., Basu, A., Mahmud, M., Kaiser, M.S., Sarkar, R.: Inverted bell-curve-based ensemble of deep learning models for detection of Covid-19 from chest X-rays. Neural Comput. Appl. 1–15 (2022)

    Google Scholar 

  48. Prakash, N., Murugappan, M., Hemalakshmi, G., Jayalakshmi, M., Mahmud, M.: Deep transfer learning for Covid-19 detection and infection localization with superpixel based segmentation. Sustain. Cities Soc. 75, 103252 (2021)

    Article  Google Scholar 

  49. Rahman, M.A., Brown, D.J., Shopland, N., Burton, A., Mahmud, M.: Explainable multimodal machine learning for engagement analysis by continuous performance test. In: Antona, M., Stephanidis, C. (eds.) HCII 2022. LNCS, vol. 13309, pp. 386–399. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-05039-8_28

    Chapter  Google Scholar 

  50. Rakib, A.B., Rumky, E.A., Ashraf, A.J., Hillas, M.M., Rahman, M.A.: Mental healthcare chatbot using sequence-to-sequence learning and BiLSTM. In: Mahmud, M., Kaiser, M.S., Vassanelli, S., Dai, Q., Zhong, N. (eds.) BI 2021. LNCS (LNAI), vol. 12960, pp. 378–387. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-86993-9_34

    Chapter  Google Scholar 

  51. Satu, M.S., et al.: Short-term prediction of Covid-19 cases using machine learning models. Appl. Sci. 11(9), 4266 (2021)

    Article  Google Scholar 

  52. Shaffi, N., et al.: Triplet-loss based Siamese convolutional neural network for 4-way classification of Alzheimer’s disease. In: Mahmud, M., He, J., Vassanelli, S., van Zundert, A., Zhong, N. (eds.) BI 2022. LNCS, vol. 13406, pp. 277–287. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-15037-1_23

    Chapter  Google Scholar 

  53. Sumi, A.I., Zohora, M.F., Mahjabeen, M., Faria, T.J., Mahmud, M., Kaiser, M.S.: fASSERT: a fuzzy assistive system for children with autism using internet of things. In: Wang, S., et al. (eds.) BI 2018. LNCS (LNAI), vol. 11309, pp. 403–412. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-05587-5_38

    Chapter  Google Scholar 

  54. Toptas, M., Samanci, N.S., et al.: Factors affecting the length of stay in the intensive care unit: our clinical experience. Biomed. Res. Int. 2018, 1–4 (2018)

    Google Scholar 

  55. UNICEF: Pregnant mothers and babies born during Covid-19 pandemic threatened by strained health systems and disruptions in services. https://www.unicef.org/bangladesh/en/press-releases/pregnant-mothers-and-babies-born-during-covid-19-pandemic-threatened-strained-health. Accessed 2 Feb 2022

  56. Wadhera, T., Mahmud, M.: Brain networks in autism spectrum disorder, epilepsy and their relationship: a machine learning approach. In: Chen, T., Carter, J., Mahmud, M., Khuman, A.S. (eds.) Artificial Intelligence in Healthcare. BIH, pp. 125–142. Springer, Cham (2022). https://doi.org/10.1007/978-981-19-5272-2_6

    Chapter  Google Scholar 

  57. Wadhera, T., Mahmud, M.: Influences of social learning in individual perception and decision making in people with autism: a computational approach. In: Mahmud, M., He, J., Vassanelli, S., van Zundert, A., Zhong, N. (eds.) BI 2022. LNCS, vol. 13406, pp. 50–61. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-15037-1_5

    Chapter  Google Scholar 

  58. WHO: Newborns: improving survival and well-being. https://www.who.int/news-room/fact-sheets/detail/newborns-reducing-mortality. Accessed 24 Mar 2022

  59. Yahaya, S.W., Lotfi, A., Mahmud, M.: Towards the development of an adaptive system for detecting anomaly in human activities. In: Proceedings of the SSCI, pp. 534–541 (2020)

    Google Scholar 

  60. Yahaya, S.W., Lotfi, A., Mahmud, M.: Towards a data-driven adaptive anomaly detection system for human activity. Pattern Recogn. Lett. 145, 200–207 (2021)

    Article  Google Scholar 

  61. Zohora, M.F., Tania, M.H., Kaiser, M.S., Mahmud, M.: Forecasting the risk of type II diabetes using reinforcement learning. In: Proceedings of the ICIEV icIVPR, pp. 1–6 (2020)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, Jahangirnagar University, Dhaka, Bangladesh

    Farzana Islam Adiba & Mohammad Zahidur Rahman

Authors
  1. Farzana Islam Adiba
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mohammad Zahidur Rahman
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Farzana Islam Adiba .

Editor information

Editors and Affiliations

  1. Nottingham Trent University, Nottingham, UK

    Mufti Mahmud

  2. University Mediterranea of Reggio Calabria, Reggio Calabria, Italy

    Cosimo Ieracitano

  3. Jahangirnagar University, Dhaka, Bangladesh

    M. Shamim Kaiser

  4. University Mediterranea of Reggio Calabria, Reggio Calabria, Italy

    Nadia Mammone

  5. University Mediterranea of Reggio Calabria, Reggio Calabria, Italy

    Francesco Carlo Morabito

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Cite this paper

Adiba, F.I., Rahman, M.Z. (2022). Machine Learning Models to Analyze the Effect of Drugs on Neonatal-ICU Length of Stay. In: Mahmud, M., Ieracitano, C., Kaiser, M.S., Mammone, N., Morabito, F.C. (eds) Applied Intelligence and Informatics. AII 2022. Communications in Computer and Information Science, vol 1724. Springer, Cham. https://doi.org/10.1007/978-3-031-24801-6_14

Download citation

Publish with us

Policies and ethics