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A Supervised Learning Approach for ICU Mortality Prediction Based on Unstructured Electrocardiogram Text Reports

  • Gokul S. Krishnan
  • S. Sowmya Kamath
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10859)

Abstract

Extracting patient data documented in text-based clinical records into a structured form is a predominantly manual process, both time and cost-intensive. Moreover, structured patient records often fail to effectively capture the nuances of patient-specific observations noted in doctors’ unstructured clinical notes and diagnostic reports. Automated techniques that utilize such unstructured text reports for modeling useful clinical information for supporting predictive analytics applications can thus be highly beneficial. In this paper, we propose a neural network based method for predicting mortality risk of ICU patients using unstructured Electrocardiogram (ECG) text reports. Word2Vec word embedding models were adopted for vectorizing and modeling textual features extracted from the patients’ reports. An unsupervised data cleansing technique for identification and removal of anomalous data/special cases was designed for optimizing the patient data representation. Further, a neural network model based on Extreme Learning Machine architecture was proposed for mortality prediction. ECG text reports available in the MIMIC-III dataset were used for experimental validation. The proposed model when benchmarked against four standard ICU severity scoring methods, outperformed all by 10–13%, in terms of prediction accuracy.

Keywords

Unstructured text analysis Healthcare analytics Clinical Decision Support Systems Word2Vec NLP Machine Learning 

Notes

Acknowledgement

We gratefully acknowledge the use of the facilities at the Department of Information Technology, NITK Surathkal, funded by Govt. of India’s DST-SERB Early Career Research Grant (ECR/2017/001056) to the second author.

References

  1. 1.
    Barak-Corren, Y., et al.: Predicting suicidal behavior from longitudinal electronic health records. Am. J. Psychiatry 174(2), 154–162 (2016)CrossRefGoogle Scholar
  2. 2.
    Belle, A., et al.: Big data analytics in healthcare. BioMed Res. Int. 2015, 16 (2015)CrossRefGoogle Scholar
  3. 3.
    Boughorbel, S., et al.: Optimal classifier for imbalanced data using matthews correlation coefficient metric. PloS one 12(6), e0177678 (2017)CrossRefGoogle Scholar
  4. 4.
    Calvert, J., et al.: Using EHR collected clinical variables to predict medical intensive care unit mortality. Ann. Med. Surg. 11, 52–57 (2016)CrossRefGoogle Scholar
  5. 5.
    Clermont, G., et al.: Predicting hospital mortality for patients in the ICU: a comparison of artificial neural networks with logistic regression models. Crit. Care Med. 29(2), 291–296 (2001)MathSciNetCrossRefGoogle Scholar
  6. 6.
    Gall, L., et al.: A simplified acute physiology score for ICU patients. Crit. Care Med. 12(11), 975–977 (1984)CrossRefGoogle Scholar
  7. 7.
    Huang, G., et al.: Trends in extreme learning machines: a review. Neural Netw. 61, 32–48 (2015)CrossRefGoogle Scholar
  8. 8.
    Johnson, A.E., et al.: A new severity of illness scale using a subset of acute physiology and chronic health evaluation data elements shows comparable predictive accuracy. Crit. Care Med. 41(7), 1711–1718 (2013)CrossRefGoogle Scholar
  9. 9.
    Johnson, E.W., et al.: MIMIC-III, a freely accessible critical care database. Sci. Data 3, 1–9 (2016). 160035CrossRefGoogle Scholar
  10. 10.
    Kim, S., et al.: A comparison of ICU mortality prediction models through the use of data mining techniques. Healthc. Inform. Res. 17(4), 232–243 (2011)CrossRefGoogle Scholar
  11. 11.
    Knaus, W.A., et al.: Apache-a physiologically based classification system. Crit. Care Med. 9(8), 591–597 (1981)CrossRefGoogle Scholar
  12. 12.
    Knaus, W.A., et al.: The APACHE III prognostic system: risk prediction of hospital mortality for critically ill hospitalized adults. Chest 100(6), 1619–1636 (1991)CrossRefGoogle Scholar
  13. 13.
    Le Gall, J., et al.: A new simplified acute physiology score (SAPS II) based on a European/North American multicenter study. JAMA 270(24), 2957–2963 (1993)CrossRefGoogle Scholar
  14. 14.
    Marafino, D., et al.: N-gram support vector machines for scalable procedure and diagnosis classification, with applications to clinical free text data from the intensive care unit. JAMIA 21(5), 871–875 (2014)Google Scholar
  15. 15.
    Mikolov, T., Chen, K., et al.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013)
  16. 16.
    Nimgaonkar, A., et al.: Prediction of mortality in an Indian ICU. Intensive Care Med. 30(2), 248–253 (2004)CrossRefGoogle Scholar
  17. 17.
    O’malley, A.S., Grossman, J.M., et al.: Are electronic medical records helpful for care coordination? Experiences of physician practices. J. Gen. Internal Med. 25(3), 177–185 (2010)CrossRefGoogle Scholar
  18. 18.
    Pirracchio, R., et al.: Mortality prediction in intensive care units with the super ICU learner algorithm (SICULA): a population-based study. Lancet Respir. Med. 3(1), 42–52 (2015)CrossRefGoogle Scholar
  19. 19.
    Poulin, S., et al.: Predicting the risk of suicide by analyzing the text of clinical notes. PloS one 9(1), e85733 (2014)CrossRefGoogle Scholar
  20. 20.
    Salton, G., Buckley, C.: Term-weighting approaches in automatic text retrieval. Inf. Process. Manag. 24(5), 513–523 (1988)CrossRefGoogle Scholar
  21. 21.
    Vincent, J.L., et al.: The SOFA score to describe organ dysfunction/failure. Intensive Care Med. 22(7), 707–710 (1996)CrossRefGoogle Scholar
  22. 22.
    Williams, F., Boren, S.: The role of the electronic medical record (EMR) in care delivery development in developing countries: a systematic review. J. Innov. Health Inform. 16(2), 139–145 (2008)CrossRefGoogle Scholar
  23. 23.
    Yi, K., Beheshti, J.: A hidden Markov model-based text classification of medical documents. J. Inf. Sci. 35(1), 67–81 (2009)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Information TechnologyNational Institute of Technology KarnatakaSurathkalIndia

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