基于电子病历的疾病风险预测
Disease Risk Prediction Based on EHR
DOI: 10.12677/HJDM.2020.101005, PDF,    国家自然科学基金支持
作者: 李宁宁, 陆 迁, 梁 敏, 莫毓昌*：华侨大学，数学科学学院，计算科学福建省高校重点实验室，福建 泉州；林 栋：福建中医药大学，针灸学院，福建 福州
关键词: 深度学习；电子病历；词嵌入；长短期记忆网络；Deep Learning； Electronic Health Records； Word Embedding； Long Short-Term Memory Network
摘要: 数据驱动的医疗保健(data driven healthcare)作为可用的大型医疗数据的使用，以提供最好的和最个性化的护理，正成为医疗行业革命成功的主要趋势之一。电子病历是推动这一数据驱动的医疗革命成功的主要载体。本文运用深度学习的方法，基于词嵌入模型实现对电子病历信息的表示，利用长短期记忆(LSTM)网络模型特性，实现对电子病历信息时间上的无规律性和疾病信息长期依赖挑战的解决，实现对疾病风险的预测，通过与卷积神经网络(CNN)模型进行比对，实验结果显示本文方法的有效性。
Abstract: Data driven health care, as the use of available large-scale medical data, to provide the best and most personalized care, is becoming one of the main trends of the success of the revolution in the medical industry. Electronic health record is the main carrier to promote the success of this da-ta-driven medical revolution. In this paper, we use the method of deep learning, based on the word embedding model to express the EHR information, and use the characteristics of the long-term memory network model to solve the irregular time of EHR information and the long-term depend-ence of disease information, so as to achieve the prediction of disease risk. Compared with the con-volution neural network model, the results show the effectiveness of this method.
文章引用：李宁宁, 陆迁, 梁敏, 林栋, 莫毓昌. 基于电子病历的疾病风险预测[J]. 数据挖掘, 2020, 10(1): 47-55. https://doi.org/10.12677/HJDM.2020.101005

参考文献

[1] Jensen, P.B., Jensen, L.J. and Brunak, S. (2012) Mining Electronic Health Records: Towards Better Research Applica-tions and Clinical Care. Nature Reviews Genetics, 13, 395-405.
[Google Scholar] [CrossRef] [PubMed]
[2] Caroprese, L., Veltri, P., Vocaturo, E. and Zumpano, E. (2018) Deep Learning Techniques for Electronic Health Record Analysis. 2018 9th International Conference on Information, Intelligence, Systems and Applications, Zakynthos, Greece, 23-25 July 2018, 1-4.
[Google Scholar] [CrossRef] 
[3] Madsen, L.B. (2014) Data-Driven Healthcare: How Analytics and BI Are Transforming the Industry. MIT Technology Review Business Report, 117, 1-19.
[4] Madsen, L.B. (2014) Data-Driven Healthcare: How Analytics and BI Are Transforming the Industry. Wiley, New York.
[5] Ho, J.C., Ghosh, J. and Sun, J.M. (2014) Marble: High-Throughput Phenotyping from Electronic Health Records via Sparse Nonnegative Tensor Factorization. In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Dining (KDD’14), 115-124.
[6] Cheng, Y., Wang, F., Zhang, P. and Hu, J.Y. (2016) Risk Pre-diction with Electronic Health Records: A Deep Learning Approach. Proceedings of the 2016 SIAM International Con-ference on Data Mining (SDM’ 16), Miami, FL, 5-7 May 2016, 432-440.
[Google Scholar] [CrossRef] 
[7] Zhou, J., Wang, F., Hu, J. and Ye, J. (2014) From Micro to Macro: Data Driven Phenotyping by Densification Longitudinal Electronic Medical Records. In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ACM, New York, 135-144.
[Google Scholar] [CrossRef] 
[8] Wang, F., Zhang, P., Qian, B., Wang, X. and Davidson, I. (2014) Clinical Risk Prediction with Multilinear Sparse Logistic Regression. In: Proceedings of the 20th ACM SIGKDD Inter-national Conference on Knowledge Discovery and Data mining, ACM, New York, 145-154.
[Google Scholar] [CrossRef] 
[9] Chen, L. (2019) Assertion Detection in Clinical Natural Language Pro-cessing: A Knowledge-Poor Machine Learning Approach. 2019 IEEE 2nd International Conference on Information and Computer Technologies, Kahului, HI, 14-17 March 2019, 37-40.
[Google Scholar] [CrossRef] 
[10] Hochreiter, S. and Schmidhuber, J. (1997) Long Short-Term Memory. Neural Computation, 9, 1735-1780.
[Google Scholar] [CrossRef] [PubMed]
[11] Rumelhart, D.E. (1986) Learning Representations by Back-Propagating Errors. Nature, 323, 533-536.
[Google Scholar] [CrossRef] 
[12] Choi, E., Bahadori, M.T., Searles, E., et al. (2016) Multi-Layer Representa-tion Learning for Medical Concepts. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, August 2016, 1495-1504.
[Google Scholar] [CrossRef] 
[13] Choi, E., Bahadori, M., Song, L., et al. (2016) GRAM: Graph-Based Attention Model for Healthcare Representation Learning. Computer Science, 787-795.
[14] Ma, F.L., Radha, C. and Zhou, J. (2017) Dipole: Diagnosis Prediction in Healthcare via Attention-based Bidirectional Recurrent Neural Networks. Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, August 2017, 1903-1911.
[Google Scholar] [CrossRef] 
[15] Che, Z., Cheng, Y., Zhai, S., Sun, Z. and Liu, Y. (2017) Boosting Deep Learning Risk Prediction with Generative Adversarial Networks for Electronic Health Records. 2017 IEEE Inter-national Conference on Data Mining, New Orleans, LA, 18-21 November 2017, 787-792.
[Google Scholar] [CrossRef] 
[16] Choi, E., Bahadori, M.T., Kulas, J.A., Schuetz, A. and Stewart, W.F. (2016) RETAIN: An Interpretable Predictive Model for Healthcare Using Reverse Time Attention Mechanism. Computer Science, 3504-3512.
[17] Suo, Q., Ma, F., Yuan, Y., et al. (2017) Personalized Disease Prediction Using a CNN-Based Similarity Learning Method. 2017 IEEE International Conference on Bioinformatics and Biomedicine, Kansas City, MO, 13-16 November 2017, 811-816.
[Google Scholar] [CrossRef] 

为你推荐