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

Korea Information Processing Society (한국정보처리학회)
권호 표지
DOI QR코드

DOI QR Code

Mortality Prediction of Older Adults Admitted to the Emergency Department

응급실 방문 노인 환자의 사망률 예측

  • 박준혁 (한국교통대학교 컴퓨터정보공학과) ;
  • 이성욱 (한국교통대학교 컴퓨터정보공학전공)
  • Received : 2018.04.16
  • Accepted : 2018.04.29
  • Published : 2018.07.31
Download PDF
⟨ Previous Next ⟩

Abstract

As the global population becomes aging, the demand for health services for the elderly is expected to increase. In particular, The elderly visiting the emergency department sometimes have complex medical, social, and physical problems, such as having a variety of illnesses or complaints of unusual symptoms. The proposed system is designed to predict the mortality of the elderly patients who are over 65 years old and have admitted the emergency department. For mortality prediction, we compare the support vector machines and Feed Forward Neural Network (FFNN) trained with medical data such as age, sex, blood pressure, body temperature, etc. The results of the FFNN with a hidden layer are best in the mortality prediction, and F1 score and the AUC is 52.0%, 88.6% respectively. If we improve the performance of the proposed system by extracting better medical features, we will be able to provide better medical services through an effective and quick allocation of medical resources for the elderly patients visiting the emergency department.

세계 인구의 고령화가 진행되는 오늘날 노인들을 위한 의료 서비스의 수요는 점차 증가할 것으로 보인다. 특히, 응급실을 방문하는 노인 환자는 일반 환자보다 다양한 질병을 갖고 있거나, 특이한 증상을 호소하는 등 복잡한 의학적, 사회적 및 신체적 문제를 가지고 있는 경우가 많다. 우리는 65세 이상의 응급실을 방문한 노인 환자의 사망률 예측을 위해 연령, 성별, 혈압, 체온, 혈액검사, 주증상명 등의 의료 데이터를 사용하였다. Feed Forward 신경망과 지지벡터기계를 각각 학습하여 사망률을 예측하고 그 성능을 비교하였다. 1개의 은닉층을 사용한 Feed Forward 신경망의 실험결과가 가장 좋았으며, 이 때 F1 점수는 52.0%, AUC는 88.6%이다. 좀 더 좋은 의료 자질을 추출하여 제안 시스템의 성능을 향상시킨다면 응급실에 방문한 노인 환자들을 위한 효과적이고 신속한 의료 자원 배분을 통해 더 좋은 의료 서비스를 제공할 수 있을 것이다.

Keywords

References

  1. L. C. Mion, R. M. Palmer, G. J. Anetzberger, and S. 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
  2. D. C. Roberts, M. P. McKay, and A. Shaffer, "Increasing rates of emergency department visits for elderly patients in the United States, 1993 to 2003," Annals of Emergency Medicine, Vol.51, No.6, pp.769-774, Jun. 2008. https://doi.org/10.1016/j.annemergmed.2007.09.011
  3. F. Aminzadeh, and W. B. Dalziel, "Older adults in the emergency department: a systematic review of patterns of use, adverse outcomes, and effectiveness of interventions," Annals of Emergency Medicine, Vol.39, No.3, pp.238-247, Mar. 2002. https://doi.org/10.1067/mem.2002.121523
  4. N. Samaras, T. Chevalley, D. Samaras, and G. 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
  5. 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.
  6. M. Coslovsky, J. Takala, A. K. Exadaktylos, L. Martinolli, and T. M. Merz, "A clinical prediction model to identify patients at high risk of death in the emergency department," Intensive Care Medicine, Vol.41, No.6, pp.1029-1036, Jun. 2015. https://doi.org/10.1007/s00134-015-3737-x
  7. S. H. Kim, J. H. Yeon, K. N. Park, S. H. Oh, S. P. Choi, Y. M. Kim, H. J. Kim, and C. S. 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, Vol.24, No.1, p.81, Jun. 2016. https://doi.org/10.1186/s13049-016-0274-8
  8. S. P. Shashikumar, M. D. Stanley, I. Sadiq, Q. Li, A. Holder, G. D. Clifford, and S. 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. M. Makar, M. Ghassemi, D. M. Cutler, and Z. 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
  10. Y. Jo, N. Loghmanpour, and C. P. Rose, "Time Series Analysis of Nursing Notes for Mortality Prediction via a State Transition Topic Model," in Proceedings of International Conference on Information and Knowledge Management, pp.1171-1180, Oct. 2015.
  11. Y. Jo, L. Lee, and S. Palaskar, "Combining LSTM and Latent Topic Modeling for Mortality Prediction," in Proceedings of International Conference on Information and Knowledge Management, arXiv preprint arXiv:1709.02842, Sep. 2017.
  12. M. A. Morid, O. R. L. Sheng, and S. Abdelrahman, "PPMF: A Patient-based Predictive Modeling Framework for Early ICU Mortality Prediction," in Proceedings of International Conference on Information and Knowledge Management, arXiv preprint arXiv:1704.07499, Apr. 2017.