Antibiotics-Resistant Bacteria Infection Prediction Based on Deep Learning

Sung-Woo Oh, Hankil Lee, Ji-Yeon Shin, Jung-Hoon Lee


The World Health Organization (WHO) and other government agencies aroundthe world have warned against antibiotic-resistant bacteria due to abuse of antibiotics and are strengthening their care and monitoring to prevent infection. However, it is highly necessary to develop an expeditious and accurate prediction and estimating method for preemptive measures. Because it takes several days to cultivate the infecting bacteria to identify the infection, quarantine and contact are not effective to prevent spread of infection. In this study, the disease diagnosis and antibiotic prescriptions included in Electronic Health Records were embedded through neural embedding model and matrix factorization, and deep learning based classification predictive model was proposed. The f1-score of the deep learning model increased from 0.525 to 0.617when embedding information on disease and antibiotics, which are the main causes of antibiotic resistance, added to the patient’s basic information and hospital use information. And deep learning model outperformed the traditional machine hospital use information. And deep learning model outperformed the traditional machine learning models.As a result of analyzing the characteristics of antibiotic resistant patients, resistant patients were more likely to use antibiotics in J01 than nonresistant patients who were diagnosed with the same diseases and were prescribed 6.3 times more than DDD.

Full Text:



A Medium Corporation, “I’ll tell you why Deep Learning is so popular and in demand,”, 2019. 02. 22.

Arango-Argoty, G., Garner, E., Pruden, A., Heath, L. S., Vikesland, P., and Zhang, L., “DeepARG: a deep learning approach for predicting antibiotic resistance genes from metagenomic data,” Microbiome, Vol. 6, No. 1, p. 23, 2018.

Bullinaria, J. A. and Levy, J. P., “Extracting semantic representations from word co-occurrence statistics: stop-lists, stemming, and SVD,” Behavior Research Methods, Vol. 44, No. 3, pp. 890-907, 2012.

Chen, M. L., Doddi, A., Royer, J., Freschi, L., Schito, M., Ezewudo, M., and Farhat, M., “Deep Learning Predicts Tuberculosis Drug Resistance Status from Whole-Genome Sequencing Data,” BioRxiv, 2018.

Choi, J. W. and Lee, H. J., “An Integrated Perspective of User Evaluating Personalized Recommender Systems: Performance-Driven or User-Centric,” The Journal of Society for e-Business Studies, Vol. 17, No. 3, pp. 85-103, 2012.

Chung, J., Bhat, A., Kim, C. J., Yong, D., and Ryu, C. M., “Combination therapy with polymyxin B and netropsin against clinical isolates of multidrug-resistant Acinetobacter baumannii,” Scientific Reports, Vol. 6, p. 28168, 2016.

Dumais, S. T., “Latent semantic analysis,” Annual Review of Information Science and Technology, Vol. 38, No. 1, pp. 188-230, 2004.

Fonarev, A. Matrix Factorization Methods For Training Embeddings, 2018.

Gamallo, P. and Bordag, S., “Is Singular Value Decomposition Useful for Word Similarity Extraction?,” Lang. Resour. Eval., Vol. 45, No. 2, pp. 95-119, 2011.

Gao, X. W. and Qian, Y., “Prediction of Multidrug-Resistant TB from CT Pulmonary Images Based on Deep Learning Techniques,” Molecular Pharmaceutics, Vol. 15, No. 10, pp. 4326-4335, 2018.

Ho, J. C., Ghosh, J., and Sun, J., “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 Mining, pp. 115–124, New York, NY, USA: ACM, 2014.

Ho, J. C., Ghosh, J., Steinhubl, S. R., Stewart, W. F., Denny, J. C., Malin, B. A., and Sun, J., “Limestone: High-throughput candidate phenotype generation via tensor factorization,” Journal of Biomedical Informatics, Vol. 52, pp. 199-211, 2014.

Jensen, P. B., Jensen, L. J., and Brunak, S., “Mining electronic health records: towards better research applications and clinical care,” Nature Reviews Genetics, Vol. 13, No. 6, p. 395, 2012.

Kim, L., Sakong, J., Kim, Y., Kim, S., Kim, S., Tchoe, B., and Lee, T., “Developing the Inpatient Sample for the National Health Insurance Claims Data,” Health Policy and Management, Vol. 23, No. 2, pp. 152-161, 2015.

Kim, Y. H., Shin, G. W., and Lee, Y. H., “The Forecast of Future Technology Based on Deep Learning,” Proceedings of KIIT Conference, pp. 219-220, 2015.

Koren, Y., Bell, R., and Volinsky, C., “Matrix Factorization Techniques for Recommender Systems,” Computer, Vol. 42, No. 8, pp. 30-37, 2009.

Krizhevsky, A., Sutskever, I., and Hinton, G. E., “ImageNet Classification with Deep Convolutional Neural Networks,” In Proceedings of the 25th International Conferenceon Neural Information Processing Systems, Vol. 1, pp. 1097-1105, USA: Curran Associates Inc, 2012.

Levy, O. and Goldberg, Y., “Neural Word Embedding As Implicit Matrix Factorization,” In Proceedings of the 27th International Conference on Neural Information Processing Systems, Vol. 2, pp. 2177-2185, Cambridge, MA, USA: MIT Press, 2014.

Liang, D., Altosaar, J., Charlin, L., and Blei, D. M., “Factorization Meets the Item Embedding: Regularizing Matrix Factorization with Item Co-occurrence,” In Proceedings of the 10th ACM Conference on Recommender Systems, pp. 59-66, New York, NY, USA: ACM, 2016.

Martinez, J. L., Baquero, F., and Andersson, D. I., “Predicting antibiotic resistance,” Nature Reviews Microbiology, Vol. 5, No. 12, pp. 958-956, 2007.

Mcadam, A. J., Hooper, D. C., Demaria, A., Limbago, M. B., O’brien, T. F., and Mccaughey, B., “Antibiotic Resistance: How Serious Is the Problem, and What Can Be Done?,” Clinical Chemistry, Vol. 58, No. 8, pp. 1182-1186, 2012.

Mikolov, T., Sutskever, I., Chen, K., Corrado, G., and Dean, J., “Distributed Representations of Words and Phrases and Their Compositionality,” In Proceedings of the 26th International Conference on Neural Information Processing Systems,Vol. 2, pp. 3111-3119, USA: Curran Associates Inc, 2013.

Moradigaravand, D., Martin, P., Anne, F., Ville, M., Warringer, J., and Parts, L., “Prediction of antibiotic resistance in Escherichia coli from large-scale pan-genome data,” PLOS Computational Biology, Vol. 14, No. 12, pp. 1-17, 2018.

Omlin, C. W. and Giles, C. L., “Stable Encoding of Large Finite-State Automata in Recurrent Neural Networks with Sigmoid Discriminants,” Neural Computation, Vol. 8, No. 4, pp. 675-696, 1996.

Park, S. H., “Management of multi-drug resistant organisms in healthcare settings,” Vol. 61, No. 1, pp. 26-35, 2018.

Polley, E. C. and van der Laan, M. J., “Super Learner in Prediction,” U.C. Berkeley Division of Biostatistics Working Paper, 1-19, 2010.

Purushotham, S., Meng, C., Che, Z., and Liu, Y., “Benchmark of Deep Learning Models on Large Healthcare MIMIC Datasets,” CoRR, 2017.

Rajkomar, A., Oren, E., Chen, K., Dai, A. M., Hajaj, N., Hardt, M., and Dean, J., “Scalable and accurate deep learning with electronic health records,” Npj Digital Medicine, Vol. 1, No. 1, p. 18, 2018.

Roh, J. H., Kim, H. J., and Chang, J. Y., “Improving Hypertext Classification Systems through WordNet-based Feature Abstraction,” The Jounal of Society for e-Business Studies, Vol. 18, No. 2, pp. 95-110, 2013.

Santos, R. P., Mayo, T. W., and Siegel, J. D., “Active Surveillance Cultures and Contact Precautions for Control of Multidrug-Resistant Organisms-Ethical Considerations,” Clinical Infectious Disease, Vol. 47, No. 1, pp. 110-116, 2008.

Socher, R., Lin, C. C. Y., Ng, A. Y., and Manning, C. D., “Parsing Natural Scenes and Natural Language with Recursive Neural Networks,” In Proceedings of the 28th International Conference on International Conference on Machine Learning, pp. 129-136, USA: Omnipress, 2011.

Song, J. H., “Current status and future strategies of antimicrobial resistance in Korea,” The Korean Journal of Medicine, Vol. 77, No. 2, pp. 143-151, 2009.

Van der Laan, M. J., Polley, E. C., and Hubbard, A. E., “Super Learner,” Statistical Applications in Genetics and Molecular Biology, Vol. 6, No. 1, 2007.

Xiang, T., Ray, D., Lohrenz, T., Dayan, P., and Montague, P. R., “Computational Phenotyping of Two-Person Interactions Reveals Differential Neural Response to Depth-of-Thought,” PLOS Computational Biology, Vol. 8, No. 12, e1002841, 2012.

Xu, X., Liang, T., Zhu, J., Zheng, D., and Sun, T., “Review of classical dimensionality reduction and sample selection methods for large-scale data processing,” Neurocomputing, Vol. 328, pp. 5-15, 2019.

Yang, Y., Niehaus, K. E., and Clifton, D. A., “Predicting antibiotic resistance from genomic data,” In Machine learning for healthcare technologies, pp. 203-226, IET, 2016.

Young, S., Abdou, T., and Bener, A., “Deep super learner: A deep ensemble for classification problems,” Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 10832 LNAI, pp. 84-95, 2018.

Zhang, M., Hu, B., Shi, C., Wu, B., and Wang, B. (n.d.)., “Matrix Factorization meets Social Network Embedding for Rating Prediction,” In Asia-Pacific Web (APWeb) and Web-Age Information Management (WAIM) Joint International Conference on Web and Big Data, pp. 121-129, Springer, Cham, 2018.


  • There are currently no refbacks.