Journal Home > Volume 28 , Issue 4
Tsinghua Science and Technology 2023, 28(4): 639-648 https://doi.org/10.26599/TST.2022.9010027
Open Access | Issue | Published: 06 January 2023

Use of Deep Learning for Continuous Prediction of Mortality for All Admissions in Intensive Care Units

Show Author's Information Guangjian Zeng1,  Jinhu Zhuang1,  Haofan Huang1,  Mu Tian1,  Yi Gao1,  Yong Liu2( ),  Xiaxia Yu1( )
School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen 518060, China
Shenzhen Hospital, Southern Medical University, Shenzhen 518060, China
Keywords:
deep learning, representation learning, risk prediction, mortality, critical care
Cite this article:
Zeng G, Zhuang J, Huang H, et al. Use of Deep Learning for Continuous Prediction of Mortality for All Admissions in Intensive Care Units. Tsinghua Science and Technology, 2023, 28(4): 639-648. https://doi.org/10.26599/TST.2022.9010027
Download citation
EndNote(RIS)
BibTeX

1443

Views

319

Downloads

Citations

1

Crossref

0

WoS

1

Scopus

0

CSCD

Abstract Full text About this article

The mortality rate in the intensive care unit (ICU) is a key metric of hospital clinical quality. To enhance hospital performance, many methods have been proposed for the stratification of patients’ different risk categories, such as severity scoring systems and machine learning models. However, these methods make capturing time sequence information difficult, posing challenges to the continuous assessment of a patient’s severity during their hospital stay. Therefore, we built a predictive model that can make predictions throughout the patient’s stay and obtain the patient’s risk of death in real time. Our proposed model performed much better than other machine learning methods, including logistic regression, random forest, and XGBoost, in a full set of performance evaluation processes. Thus, the proposed model can support physicians’ decisions by allowing them to pay more attention to high-risk patients and anticipate potential complications to reduce ICU mortality.


menu
Abstract
Full text
Outline
About this article

Use of Deep Learning for Continuous Prediction of Mortality for All Admissions in Intensive Care Units

Show Author's information Guangjian Zeng1, Jinhu Zhuang1, Haofan Huang1, Mu Tian1, Yi Gao1, Yong Liu2( ), Xiaxia Yu1( )
School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen 518060, China
Shenzhen Hospital, Southern Medical University, Shenzhen 518060, China

Abstract

The mortality rate in the intensive care unit (ICU) is a key metric of hospital clinical quality. To enhance hospital performance, many methods have been proposed for the stratification of patients’ different risk categories, such as severity scoring systems and machine learning models. However, these methods make capturing time sequence information difficult, posing challenges to the continuous assessment of a patient’s severity during their hospital stay. Therefore, we built a predictive model that can make predictions throughout the patient’s stay and obtain the patient’s risk of death in real time. Our proposed model performed much better than other machine learning methods, including logistic regression, random forest, and XGBoost, in a full set of performance evaluation processes. Thus, the proposed model can support physicians’ decisions by allowing them to pay more attention to high-risk patients and anticipate potential complications to reduce ICU mortality.

Keywords: deep learning, representation learning, risk prediction, mortality, critical care

References(45)

[1]
G. Rouleau, M. P. Gagnon, and J. Côté, Impacts of information and communication technologies on nursing care: An overview of systematic reviews (protocol), Syst. Rev., vol. 4, no. 1, p. 75, 2015.
DOI Google Scholar
[2]
K. G. M. Moons, R. F. Wolff, R. D. Riley, P. F. Whiting, M. Westwood, G. S. Collins, J. B. Reitsma, J. Kleijnen, and S. Mallett, PROBAST: A tool to assess risk of bias and applicability of prediction model studies: Explanation and elaboration, Ann. Intern. Med., vol. 170, no.1, pp. W1–W33, 2019.
DOI Google Scholar
[3]
R. Lilford and P. Pronovost, Using hospital mortality rates to judge hospital performance: A bad idea that just won’t go away, BMJ, vol. 340, p. c2016, 2010.
DOI Google Scholar
[4]
K. M. D. M. Karunarathna, Predicting ICU death with summarized patient data, presented at the 2018 IEEE 8th Annu. Computing and Communication Workshop and Conf. (CCWC), Las Vegas, NV, USA, 2018, pp. 238–247.
DOI Google Scholar
[5]
R. T. Thomson, D. Leuttel, F. Healey, and S. Scobie, Safer Care for the Acutely Ill Patient: Learning from Serious Incidents. London, UK: National Patient Safety Agency, 2007.
[6]
R. Sadeghi, T. Banerjee, and W. Romine, Early hospital mortality prediction using vital signals, Smart Health, vols. 9–10, pp. 265–274, 2018.
DOI Google Scholar
[7]
B. E. Keuning, T. Kaufmann, R. Wiersema, A. Granholm, V. Pettilä, M. H. Møller, C. F. Christiansen, J. C. Forte, H. Snieder, F. Keus, et al., Mortality prediction models in the adult critically ill: A scoping review, Acta Anaesthesiol. Scand., vol. 64, no. 4, pp. 424–442, 2020.
DOI Google Scholar
[8]
S. Lemeshow and J. R. Le Gall, Modeling the severity of illness of ICU patients: A systems update, JAMA, vol. 272, no. 13, pp. 1049–1055, 1994.
DOI Google Scholar
[9]
J. I. F. Salluh and M. Soares, ICU severity of illness scores: APACHE, SAPS and MPM, Curr. Opin. Crit. Care, vol. 20, no. 5, pp. 557–565, 2014.
DOI Google Scholar
[10]
J. L. Vincent, R. Moreno, J. Takala, S. Willatts, A. De Mendonça, H. Bruining, C. K. Reinhart, P. M. Suter, and L. G. Thijs, The SOFA (Sepsis-related Organ Failure Assessment) score to describe organ dysfunction/failure, Intensive Care Med., vol. 22, no. 7, pp. 707–710, 1996.
DOI Google Scholar
[11]
J. E. Zimmerman, A. A. Kramer, D. S. Mcnair, and F. M. Malila, Acute Physiology and Chronic Health Evaluation (APACHE) IV: Hospital mortality assessment for today’s critically ill patients, Crit. Care Med., vol. 34, no. 5, pp. 1297–1310, 2006.
DOI Google Scholar
[12]
W. A. Knaus, E. A. Draper, D. P. Wagner, and J. E. Zimmerman, APACHE II: A severity of disease classification system, Crit. Care Med., vol. 13, no. 10, pp. 818–829, 1985.
DOI Google Scholar
[13]
T. L. Higgins, D. Teres, W. Copes, B. Nathanson, M. Stark, and A. Kramer, Updated mortality probability model (MPM-III), Chest, vol. 128, no. 4, p. 348S, 2005.
DOI Google Scholar
[14]
K. Strand and H. Flaatten, Severity scoring in the ICU: A review, Acta Anaesthesiol. Scand., vol. 52, no. 4, pp. 467–478, 2008.
DOI Google Scholar
[15]
S. Vairavan, L. Eshelman, S. Haider, A. Flower, and A. Seiver, Prediction of mortality in an intensive care unit using logistic regression and a hidden Markov model, presented at the 2012 Computing in Cardiology, Krakow, Poland, 2012, pp. 393–396.
Google Scholar
[16]
S. Barnes, E. Hamrock, M. Toerper, S. Siddiqui, and S. Levin, Real-time prediction of inpatient length of stay for discharge prioritization, J. Am. Med. Inform. Assoc., vol. 23, no. e1, pp. e2–e10, 2016.
DOI Google Scholar
[17]
N. El-Rashidy, S. El-Sappagh, T. Abuhmed, S. Abdelrazek, and H. M. El-Bakry, Intensive care unit mortality prediction: An improved patient-specific stacking ensemble model, IEEE Access, vol. 8, pp. 133541–133564, 2020.
DOI Google Scholar
[18]
G. Gutierrez, Artificial intelligence in the intensive care unit, Crit. Care, vol. 24, no. 1, p. 101, 2020.
DOI Google Scholar
[19]
S. Leteurtre, A. Duhamel, B. Grandbastien, F. Proulx, J. Cotting, R. Gottesman, A. Joffe, B. Wagner, P. Hubert, A. Martinot, et al., Daily estimation of the severity of multiple organ dysfunction syndrome in critically ill children, CMAJ, vol. 182, no. 11, pp. 1181–1187, 2010.
DOI Google Scholar
[20]
S. Leteurtre, A. Duhamel, V. Deken, J. Lacroix, F. Leclerc, and Groupe Francophone de Réanimation et Urgences Pédiatriques, Daily estimation of the severity of organ dysfunctions in critically ill children by using the PELOD-2 score, Crit. Care, vol. 19, no. 1, p. 324, 2015.
DOI Google Scholar
[21]
O. Badawi, X. G. Liu, E. Hassan, P. J. Amelung, and S. Swami, Evaluation of ICU risk models adapted for use as continuous markers of severity of illness throughout the ICU stay, Crit. Care Med., vol. 46, no. 3, pp. 361–367, 2018.
DOI Google Scholar
[22]
M. J. Rothman, S. I. Rothman, and J. Beals IV, Development and validation of a continuous measure of patient condition using the electronic medical record, J. Biomed. Inform., vol. 46, no. 5, pp. 837–848, 2013.
DOI Google Scholar
[23]
M. J. Rothman, .J. J. Tepas, A. J. Nowalk, J. E. Levin, J. M. Rimar, A. Marchetti, and A. L. Hsiao, Development and validation of a continuously age-adjusted measure of patient condition for hospitalized children using the electronic medical record, J. Biomed. Inform., vol. 66, pp. 180–193, 2017.
DOI Google Scholar
[24]
C. W. Hug and P. Szolovits, ICU acuity: Real-time models versus daily models, presented at the AMIA 2009 Symp. Proc., American Medical Informatics Association, San Francisco, CA, USA, 2009, pp. 260–264.
Google Scholar
[25]
B. Shickel, T. J. Loftus, L. Adhikari, T. Ozrazgat-Baslanti, A. Bihorac, and P. Rashidi, DeepSOFA: A continuous acuity score for critically ill patients using clinically interpretable deep learning, Sci. Rep., vol. 9, no. 1, p. 1879, 2019.
DOI Google Scholar
[26]
G. Clermont, D. C. Angus, S. M. Dirusso, M. Griffin, and W. T. Linde-Zwirble, Predicting hospital mortality for patients in the intensive care unit: A comparison of artificial neural networks with logistic regression models, Crit. Care Med., vol. 29, no. 2, pp. 291–296, 2001.
DOI Google Scholar
[27]
G. Meyfroidt, F. Güiza, J. Ramon, and M. Bruynooghe, Machine learning techniques to examine large patient databases, Best Pract. Res. Clin. Anaesthesiol., vol. 23, no. 1, pp. 127–143, 2009.
DOI Google Scholar
[28]
S. Kim, W. Kim, and R. W. Park, A comparison of intensive care unit mortality prediction models through the use of data mining techniques, Healthc. Inform. Res., vol. 17, no. 4, pp. 232–243, 2011.
DOI Google Scholar
[29]
M. Babaie, S. Kalra, A. Sriram, C. Mitcheltree, S. J. Zhu, A. Khatami, S. Rahnamayan, and H. R. Tizhoosh, Classification and retrieval of digital pathology scans: A new dataset, presented at the 2017 IEEE Conf. Computer Vision and Pattern Recognition Workshops, Honolulu, HI, USA, 2017, pp. 760–768.
DOI Google Scholar
[30]
A. Khatami, M. Babaie, A. Khosravi, H. R. Tizhoosh, S. M. Salaken, and S. Nahavandi, A deep-structural medical image classification for a Radon-based image retrieval, presented at the 2017 IEEE 30th Canadian Conf. Electrical and Computer Engineering (CCECE), Windsor, Canada, 2017, pp. 1–4.
DOI Google Scholar
[31]
A. Khatami, A. Khosravi, C. P. Lim, and S. Nahavandi, A wavelet deep belief network-based classifier for medical images, in Proc. 23rd Int. Conf. Neural Information Processing, Kyoto, Japan, 2016, pp. 467–474.
DOI Google Scholar
[32]
A. Khatami, A. Khosravi, T. Nguyen, C. P. Lim, and S. Nahavandi, Medical image analysis using wavelet transform and deep belief networks, Expert Syst. Appl., vol. 86, pp. 190–198, 2017.
DOI Google Scholar
[33]
S. Hochreiter and J. Schmidhuber, Long short-term memory, Neural Comput., vol. 9, no. 8, pp. 1735–1780, 1997.
DOI Google Scholar
[34]
Y. LeCun, Y. Bengio, and G. Hinton, Deep learning, Nature, vol. 521, no. 7553, pp. 436–444, 2015.
DOI Google Scholar
[35]
E. Laksana, M. Aczon, L. Ho, C. Carlin, D. Ledbetter, and R. Wetzel, The impact of extraneous features on the performance of recurrent neural network models in clinical tasks, J. Biomed. Inform., vol. 102, p. 103351, 2020.
DOI Google Scholar
[36]
C. S. Carlin, L. V. Ho, D. R. Ledbetter, M. D. Aczon, and R. C. Wetzel, Predicting individual physiologically acceptable states at discharge from a pediatric intensive care unit, J. Am. Med. Inform. Assoc., vol. 25, no. 12, pp. 1600–1607, 2018.
DOI Google Scholar
[37]
M. C. Winter, T. E. Day, D. R. Ledbetter, M. D. Aczon, C. J. L. Newth, R. C. Wetzel, and P. A. Ross, Machine learning to predict cardiac death within 1 hour after terminal extubation, Pediatr. Crit. Care Med., vol. 22, no. 2, pp. 161–171, 2021.
DOI Google Scholar
[38]
E. Choi, A. Schuetz, W. F. Stewart, and J. M. Sun, Using recurrent neural network models for early detection of heart failure onset, J. Am. Med. Inform. Assoc., vol. 24, no. 2, pp. 361–370, 2017.
DOI Google Scholar
[39]
A. Rajkomar, E. Oren, K. Chen, A. M. Dai, N. Hajaj, M. Hardt, P. J. Liu, X. B. Liu, J. Marcus, M. M. Sun, et al., Scalable and accurate deep learning with electronic health records, NPJ Digit. Med., vol. 1, p. 18, 2018.
DOI Google Scholar
[40]
M. Saqib, Y. Sha, and M. D. Wang, Early prediction of sepsis in EMR records using traditional ML techniques and deep learning LSTM networks, presented at the 2018 40th Annu. Int. Conf. IEEE Engineering in Medicine and Biology Society (EMBC), Honolulu, HI, USA, 2018, pp. 4038–4041.
DOI Google Scholar
[41]
S. Kannan, G. Yengera, D. Mutter, J. Marescaux, and N. Padoy, Future-state predicting LSTM for early surgery type recognition, IEEE Trans. Med. Imaging, vol. 39, no. 3, pp. 556–566, 2020.
DOI Google Scholar
[42]
N. Tomašev, N. Harris, S. Baur, A. Mottram, X. Glorot, J. W. Rae, M. Zielinski, H. Askham, A. Saraiva, V. Magliulo, et al., Use of deep learning to develop continuous-risk models for adverse event prediction from electronic health records, Nat. Protoc., vol. 16, no. 6, pp. 2765–2787, 2021.
DOI Google Scholar
[43]
A. E. W. Johnson, T. J. Pollard, L. Shen, L. W. H. Lehman, M. L. Feng, M. Ghassemi, B. Moody, P. Szolovits, L. A. Celi, and R. G. Mark, MIMIC-III, a freely accessible critical care database, Sci. Data, vol. 3, p. 160035, 2016.
DOI Google Scholar
[44]
A. J. Vickers, A. M. Cronin, E. B. Elkin, and M. Gonen, Extensions to decision curve analysis, a novel method for evaluating diagnostic tests, prediction models and molecular markers, BMC Med. Inform. Decis. Mak., vol. 8, no. 1, p. 53, 2008.
DOI Google Scholar
[45]
K. F. Kerr, M. D. Brown, K. H. Zhu, and H. Janes, Assessing the clinical impact of risk prediction models with decision curves: Guidance for correct interpretation and appropriate use, J. Clin. Oncol., vol. 34, no. 21, pp. 2534–2540, 2016.
DOI Google Scholar
Publication history
Copyright
Acknowledgements
Rights and permissions

Publication history

Received: 09 January 2022
Revised: 12 May 2022
Accepted: 05 July 2022
Published: 06 January 2023
Issue date: August 2023

Copyright

© The author(s) 2023.

Acknowledgements

This research was supported by the Key Discipline Fund of Shenzhen Hospital of Southern Medical University (No. 2021-2023ICU), the New-Generation Information Technology by the Scientific Research Platform of Institutions of Higher Education of the Education Department of Guangdong Province (No. 2021ZDZX1014), and the Shenzhen University (SZU) Top Ranking Project (No. 86000000210). We thank LetPub (www.letpub.com) for its linguistic assistance during the preparation of this manuscript.

Rights and permissions

The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).

Return