基于深度学习的医疗电子数据特征学习方法

doi:10.3969/j.issn.0255-8297.2023.01.004

Abstract

Abstract: How can we effectively carry out the feature learning of high-dimensional and heterogeneous medical electronic data to optimize the risk prediction of concurrent medical use in patients? To address the problem, this paper proposed a method of multi-stage deep feature learning. Firstly, we performed the feature learning of medical use data with temporal properties by combining deep learning models of long short-term memory (LSTM) and auto-encoder (AE), and generated the synthetic factor of concurrent medical use with bisecting k-means clustering method. Secondly, we constructed two types of feature vectors for patients to predict adverse event risk, and analyzed the associated factors of high risk. Finally, we compared the performance of the proposed method with existing methods on real-word dataset, and the results show that the proposed method increases the accuracy by 5%~10%, and reduces the false rate by 3%~5% in the risk prediction of concurrent medical use.

Key words: deep learning, feature learning, medical electronic data, concurrent medical use, adverse events

CLC Number:

TP391

WANG Ting, WANG Na, CUI Yunpeng, LIU Juan. Medical Electronic Data Feature Learning Method Based on Deep Learning[J]. Journal of Applied Sciences, 2023, 41(1): 41-54.

References

[1] Shen Y, Zhang L, Zhang J, et al. CBN:constructing a clinical Bayesian network based on data from the electronic medical record[J]. Journal of Biomedical Informatics, 2018, 88:1-10.
[2] Gao X T, Stephen L, Wong Y T. Automatic feature learning to grade nuclear cataracts based on deep learning[J]. IEEE Transactions on Biomedical Engineering, 2015, 62(11):2693-2701.
[3] Cirean D C, Giusti A, Gambardella L M, et al. Mitosis detection in breast cancer histology images with deep neural networks[C]//2013 International Conference on Medical Image Computing and Computer-Assisted Intervention, 2018:1-8.
[4] Suk H I, Lee S W, Shen D, et al. Hierarchical feature representation and multimodal fusion with deep learning for AD/MCI diagnosis[J]. Neuroimage, 2014, 101:569-582.
[5] Ithapu V K, Singh V, Okonkwo O C, et al. Imaging-based enrichment criteria using deep learning algorithms for efficient clinical trials in mild cognitive impairment[J]. The Journal of the Alzheimers Association, 2015, 11(12):1489-1499.
[6] Suk H II, Lee S W, Shen D. Deep sparse multi-task learning for feature selection in Alzheimer's disease diagnosis[J]. Brain Structure & Function, 2016, 221(5):2569-2587.
[7] Suk H I, Lee S W, Shen D. Latent feature representation with stacked auto-encoder for AD/MCI diagnosis[J]. Brain Structure & Function, 2013, 220(2):841-859.
[8] Wee C Y, Liu C, Lee A, et al. Cortical graph neural network for AD and MCI diagnosis and transfer learning across populations[J]. NeuroImage:Clinical, 2019, 23:19-29.
[9] Agah A. Medical applications of artificial intelligence[EB/OL]. (2013-10-30)[2022-10-15]. https://doi.org/10.1201/b15618.
[10] Xu J, Lei X, Hang R L, et al. Stacked sparse autoencoder for nuclei detection on breast cancer histopathology images[J]. IEEE Transactions on Medical Imaging, 35(1):19-30.
[11] Fakoor R, Ladhak F, Nazi A, et al. Using deep learning to enhance cancer diagnosis and classification[R/OL]. (2013-06-10)[2022-10-15]. https://www.researchgate.net/publication/281857285_Using_deep_learning_to_enhance_cancer_diagnosis_and_classification.
[12] Zuluaga G J, Almasry Z, Benaggoune K, et al. A CNN-based methodology for breast cancer diagnosis using thermal images[J]. Computer Methods in Biomechanics and Biomedical Engineering:Imaging & Visualization, 2021, 9(2):131-145.
[13] Shamshirband S, Fathi M, Dehzangi A, et al. A review on deep learning approaches in healthcare systems:taxonomies, challenges, and open issues[J]. Journal of Biomedical Informatics, 2021, 113:27-36.
[14] Liu G P, Yan J J, Wang Y Q, et al. Deep learning based syndrome diagnosis of chronic gastritis[J]. Computational and Mathematical Methods in Medicine, 2014:1-8.
[15] Ikenoyama Y, Hirasawa T, Ishioka M, et al. Detecting early gastric cancer:comparison between the diagnostic ability of convolutional neural networks and endoscopists[J]. Digestive Endoscopy, 2021, 33(1):141-150.
[16] Kumar D, Wong A, Clausi D A. Lung nodule classification using deep features in CT images[J]. Wireless Personal Communications, 2015, 116(2021):655-690.
[17] Ma J, Sheridan R P, Liaw A, et al. Deep neural nets as a method for quantitative structure-activity relationships[J]. Journal of Chemical Information & Modeling, 2015, 55(2):263-274.
[18] Bhinder B, Gilvary C, Madhukar N S, et al. Artificial intelligence in cancer research and precision medicine[J]. Cancer Discovery, 2021, 11(4):900-915.
[19] Prasoon A, Petersen K, Igel C, et al. Deep feature learning for knee cartilage segmentation using a triplanar convolutional neural network[C]//2013 International Conference on Medical Image Computing and Computer-Assisted Intervention. Berlin, Heidelberg:Springer, 2013:246-253.
[20] Song Y, Zhang L, Chen S, et al. A deep learning based framework for accurate segmentation of cervical cytoplasm and nuclei[C]//The 36th Annual International Conference of IEEE Engineering in Medicine and Biology Society, 2014:2903.
[21] Majumdar A. Real-time dynamic MRI reconstruction using stacked denoising autoencoder[R/OL]. (2015-03-22)[2022-10-15]. https://arxiv.org/ftp/arxiv/papers/1503/1503.06383.pdf.
[22] Chaudhari A S, Sandino C M, Cole E K, et al. Prospective deployment of deep learning in MRI:a framework for important considerations, challenges, and recommendations for best practices[J]. Journal of Magnetic Resonance Imaging, 2021, 54(2):357-371.
[23] Zhang W, Li R, Deng H, et al. Deep convolutional neural networks for multi-modality isointense infant brain image segmentation[J]. Neuroimage, 2015, 108:214-224.
[24] Zeng T, Li R J, Mukkamala R, et al. Deep convolutional neural networks for annotating gene expression patterns in the mouse brain[J]. BMC Bioinformatics, 2015, 16:147-157.
[25] Wang D, Shang Y. Modeling physiological data with deep belief networks[J]. International Journal of Information and Education Technology, 2013, 3(5):505-511.
[26] Spencer M, Eickholt J, Jianlin C. A deep learning network approach to ab initio protein secondary structure prediction[J]. IEEE/ACM Transactions on Computational Biology & Bioinformatics, 2015, 12(1):103-112.
[27] Heffernan R, Paliwal K, Lyons J, et al. Improving prediction of secondary structure, local backbone angles, and solvent accessible surface area of proteins by iterative deep learning[J]. Scientific Reports, 2015, 5:47-53.
[28] Zhou J, Troyanskaya O G. Deep supervised and convolutional generative stochastic network for protein secondary structure prediction[J]. Journal of Machine Learning Research, 2014, 32(1):745-753.
[29] Lena P D, Nagata K, Baldi P. Deep architectures for protein contact map prediction[J]. Bioinformatics, 2012, 28(19):2449-2457.
[30] Yuvaraj N, Srihari K, Chandragandhi S, et al. Analysis of protein-ligand interactions of SARS-Cov-2 against selective drug using deep neural networks[J]. Big Data Mining and Analytics, 2021, 4(2):76-83.
[31] Quang D, Chen Y, Xie X. DANN:a deep learning approach for annotating the pathogenicity of genetic variants[J]. Bioinformatics, 2014, 31(5):761-763.
[32] Li J, Pu Y, Tang J, et al. A hybrid category attention neural network for identifying functional effects of DNA sequences[J]. Briefings in Bioinformatics, 2021, 22(3):159-170.
[33] Leung M K K, Xiong H Y, Lee L J, et al. Deep learning of the tissue-regulated splicing code[J]. Bioinformatics, 2014, 30(12):121-129.
[34] Xiong H Y, Alipanahi B, Lee L J, et al. The human splicing code reveals new insights into the genetic determinants of disease[J]. Science, 2015, 347(6218):1254806.
[35] Bhattacharjee S, Ghosh A, Saha B, et al. Machine learning and systems biology in genomics and health[M].[S.l.]:Springer, 2022:69-90.
[36] Tibshirani R, Walther G, Hastie T. Estimating the number of clusters in a data set via the gap statistic[J]. Journal of the Royal Statistical Society:Series B (Statistical Methodology), 2001, 63(2):411-423.

Medical Electronic Data Feature Learning Method Based on Deep Learning

PDF

Knowledge

Abstract

Cite this article

share this article

References

Related Articles 15

Recommended Articles

Metrics

Comments

[1]	SHEN Kunye, ZHOU Xiaofei, FEI Xiaobo, CHEN Yuzhong, ZHANG Jiyong, YAN Chenggang. Boundary-Aware Deeply Residual Network for Salient Object Detection of Strip Steel Surface Defects [J]. Journal of Applied Sciences, 2023, 41(6): 978-988.
[2]	ZHANG Chunsen, ZHU Jiangle, ZHANG Xuefen, LIU Xudong, SHI Shu. Void Filling of DEM in a Generative Adversarial Network Fused with Self-Attention Mechanism [J]. Journal of Applied Sciences, 2023, 41(5): 789-800.
[3]	HUANG Xianpei, MENG Qingxiang. Land Cover Classification of Sentinel-2 Image Based on Multi-feature Convolution Neural Network [J]. Journal of Applied Sciences, 2023, 41(5): 766-776.
[4]	WEI Jieling, MA Xiuli, JIN Yanliang, WANG Rui. Encrypted Traffic Classification Algorithm Based on VPN Channel [J]. Journal of Applied Sciences, 2023, 41(4): 646-656.
[5]	XIAO Xiaotong, DING Jianwei, ZHANG Qi. Segmented Backdoor Defense Based on Local Gradient and Global Gradient Ascent [J]. Journal of Applied Sciences, 2023, 41(2): 218-227.
[6]	ZENG Jing, LI Ying, QI Xiaosha, JI Genlin. Video Anomaly Detection Method Based on Secondary Prediction of Multi-layer Memory Enhancement Generative Adversarial Network [J]. Journal of Applied Sciences, 2023, 41(1): 80-94.
[7]	JIANG Xiaoyong, LI Zhongyi, HUANG Langyue, PENG Mengle, XU Shuyang. Review of Neural Network Pruning Techniques [J]. Journal of Applied Sciences, 2022, 40(5): 838-849.
[8]	LUO Changyin, CHEN Xuebin, SONG Shangwen, ZHANG Shufen, LIU Zhiyu. Federated Ensemble Algorithm Based on Deep Neural Network [J]. Journal of Applied Sciences, 2022, 40(3): 493-510.
[9]	LEI Qianhui, PAN Lili, SHAO Weizhi, HU Haipeng, HUANG Yao. Segmentation Model of COVID-19 Lesions Based on Triple Attention Mechanism [J]. Journal of Applied Sciences, 2022, 40(1): 105-115.
[10]	ZHU Li, YANG Qing, WU Tao, LI Chen, LI Ming. Emotional Analysis of Brain Waves Based on CNN and Bi-LSTM [J]. Journal of Applied Sciences, 2022, 40(1): 1-12.
[11]	OU Qiaofeng, XIAO Jiabing, XIE Qunqun, XIONG Bangshu. Multi-target Detection and Recognition for Vehicle Inspection Images Based on Deep Learning [J]. Journal of Applied Sciences, 2021, 39(6): 939-951.
[12]	KANG Huixian, YI Biao, WU Hanzhou. Recent Advances in Text Steganography and Steganalysis [J]. Journal of Applied Sciences, 2021, 39(6): 923-938.
[13]	GUO Yubo, LU Jun, DUAN Pengqi. Automatic Classification of Bamboo Flute Playing Skills Based on Deep Learning [J]. Journal of Applied Sciences, 2021, 39(4): 685-694.
[14]	LI Wenju, HE Maoxian, ZHANG Yaoxing, CHEN Huiling, LI Peigang. Crack Detection of Track Slab Based on Convolutional Neural Network and Voting Mechanism [J]. Journal of Applied Sciences, 2021, 39(4): 627-640.
[15]	DU Chengze, DUAN Youxiang, SUN Qifeng. Seismic Fault Identification Method Based on ResUNet and Dense CRF Model [J]. Journal of Applied Sciences, 2021, 39(3): 367-366.