KIPS Transactions on Software and Data Engineering (정보처리학회논문지:소프트웨어 및 데이터공학)

Korea Information Processing Society (한국정보처리학회)
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Mortality Prediction of Older Adults Using Random Forest and Deep Learning

랜덤 포레스트와 딥러닝을 이용한 노인환자의 사망률 예측

  • 박준혁 (한국교통대학교 컴퓨터정보공학과) ;
  • 이성욱 (한국교통대학교 컴퓨터정보공학전공)
  • Received : 2020.07.16
  • Accepted : 2020.08.26
  • Published : 2020.10.31
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Abstract

We predict the mortality of the elderly patients visiting the emergency department who are over 65 years old using Feed Forward Neural Network (FFNN) and Convolutional Neural Network (CNN) respectively. Medical data consist of 99 features including basic information such as sex, age, temperature, and heart rate as well as past history, various blood tests and culture tests, and etc. Among these, we used random forest to select features by measuring the importance of features in the prediction of mortality. As a result, using the top 80 features with high importance is best in the mortality prediction. The performance of the FFNN and CNN is compared by using the selected features for training each neural network. To train CNN with images, we convert medical data to fixed size images. We acquire better results with CNN than with FFNN. With CNN for mortality prediction, F1 score and the AUC for test data are 56.9 and 92.1 respectively.

우리는 응급실을 방문한 65세 이상 노인환자의 의료 데이터를 각각 피드 포워드 신경망과 합성곱 신경망에 학습하여 사망률을 예측하였다. 의료 데이터는 노인환자의 성별, 연령, 체온, 심박 수 등의 기초적인 정보뿐 아니라 과거 병력, 다양한 혈액 검사 및 배양 검사 결과 등 다양하고 복잡한 정보를 포함하여 총 99가지의 자질로 구성된다. 이 중 사망률 예측에 크게 기여하는 자질을 선택하기 위해 랜덤 포레스트를 이용하여 자질의 중요도를 계산하였고, 그 결과 중요도가 높은 상위 80개의 자질을 선택하였다. 선택된 자질을 각각 피드 포워드 신경망과 합성곱 신경망의 학습에 사용하여 두 신경망의 성능을 비교하였다. 합성곱 신경망 학습을 위해 의료 데이터를 고정된 크기의 이미지로 변환하였으며 합성곱 신경망이 피드 포워드 신경망을 이용한 것보다 성능이 좋았다. 합성곱 신경망의 사망률 예측 성능으로 테스트 데이터에 대해 F1 점수는 56.9, AUC는 92.1을 각각 얻었다.

Keywords

References

  1. Richard Niska, Farida Bhuiya, and Jianmin Xu, "National Hospital Ambulatory Medical Care Survey: 2007 emergency department summary," National Health Stat Report, Vol.26, No.26, pp.1-31, Aug. 2010.
  2. Lorraine Mion, Robert Palmer, Georgia J. Anetzberger, and Stephen W. Meldon, "Establishing a casefinding and Referral System for At-risk Older Individuals in the Emergency Department Setting: the SIGNET Model," Journal of the American Geriatrics Society, Vol.49, No.10, pp.1379-1186, Oct. 2001. https://doi.org/10.1046/j.1532-5415.2001.49270.x
  3. Nikolaos Samaras, Thierry Chevalley, Dimitrios Samaras, and Gabriel Gold, "Older Patients in the Emergency Department: A Review," Annals of Emergency Medicine, Vol.56, No.3, pp.261-269, Sep. 2010. https://doi.org/10.1016/j.annemergmed.2010.04.015
  4. G. R. Strange and E. H. Chen, "Use of emergency departments by Elder Patients: a Five-year Follow-up Study," Annals of Emergency Medicine, Vol.5, No.12, pp.1157-1162, Dec. 1998.
  5. Junhyeok Park and Songwook Lee, "Mortality Prediction of Older Adults Admitted to the Emergency Department," KIPS Transactions on Software and Data Engineering (KTSDE), Vol.7, No.7, pp.275-280, Jul. 2018. https://doi.org/10.3745/KTSDE.2018.7.7.275
  6. Maggie Makar, Marzyeh Ghassemi, David Cutler, and Ziad Obermeyer, "Short-term Mortality Prediction for Elderly Patients Using Medicare Claims Data," International journal of Machine Learning and Computing, Vol.5, No.3, pp. 192-197, Jun. 2015. https://doi.org/10.7763/IJMLC.2015.V5.506
  7. Soo Hyun Kim, Jeong Hoon Yeon, Kyu Nam Park, Sang Hoon Oh, Seung Pill Choi, Young Min Kim, Han Joon Kim, and Chun Song Youn, "The Association of Red Cell Distribution Width and in-hospital Mortality in Older Adults Admitted to the Emergency Department," Scandinavian Journal of Trauma, Resuscitation and Emergency Medicine, Jun. 2016.
  8. Supreeth P. Shashikumar, Matthew D. Stanley, Ismail Sadiq, Qiao Li, Andre Holder, Gari D. Clifford, and Shamim Nemati, "Early Sepsis Detection in Critical Care Patients using Multiscale Blood Pressure and Heart Rate Dynamics," Journal of Electrocardiology, Vol.50, No.6, pp.739-743, Aug. 2017. https://doi.org/10.1016/j.jelectrocard.2017.08.013
  9. Cuong Nguyen, Yong Wang, and Ha Nam Nguyen, "Random Forest Classifier Combined with Feature Selection for Breast Cancer Diagnosis and Prognostic," Journal of Biomedical Science and Engineering, Vol.6, No.5, pp.551-560, May 2013. https://doi.org/10.4236/jbise.2013.65070
  10. Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fe, "Imagenet: A large-scale Hierarchical Image Database," IEEE Conference on Computer Vision and Pattern Recognition, 2009.
  11. Alex Krizhevsky, Ilya Sutskever, and Geoffrey E. Hinton, "Imagenet Classification with Deep Convolutional Neural Networks," in Neural Information Processing Systems, 2012.
  12. Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, and Andrew Rabinovich, "Going deeper with convolutions," in IEEE Conference on Computer Vision and Pattern Recognition 2015.
  13. He Yang, Hengyong Yu, and Ge Wang, "Deep Learning for the Classification of Lung Nodules," arXiv preprint, arXiv:1611.06651, 2016.
  14. Samuel G Armato III, "The lung image database consortium (LIDC) and Image Database Resource Initiative (IDRI): a Completed Reference Database of Lung Nodules on CT Scans," Medical Physics Online, Vol.38, No.2, pp.915-931, Feb. 2011. https://doi.org/10.1118/1.3528204