Abstract
Personalized medicine (PM) aiming at tailoring medical treatment to individual patient is critical in guiding precision prescription. An important challenge for PM is comorbidity due to the complex interrelation of diseases, medications and individual characteristics of the patient. To address this, we study the problem of PM for comorbidity and propose a neural network framework Deep Personalized Prescription for Comorbidity (PPC). PPC exploits multi-source information from massive electronic medical records (EMRs), such as demographic information and laboratory indicators, to support personalized prescription. Patient-level, disease-level and drug-level representations are simultaneously learned and fused with a trilinear method to achieve personalized prescription for comorbidity. Experiments on a publicly real world EMRs dataset demonstrate PPC outperforms state-of-the-art works.
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We have also examined LSTM and other activation functions to learn to represent diagnosis, but they have less efficiency and worse performance.
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We have examined both l1-norm and l2-norm, and find their performance are similar.
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References
PMC2017: The personalized medicine opportunity, challenges, and the future (2017). http://www.personalizedmedicinecoalition.org/Userfiles/PMC-Corporate/file/The-Personalized-Medicine-Report1.pdf
Spear, B., Heath-Chiozzi, M., Huff, J.: Clinical application of pharmacogenetics. Trends Mol. Med. 7(5), 201–204 (2001)
Berwick, D.M., Finkelstein, J.A.: Preparing medical students for the continual improvement of health and health care: Abraham flexner and the new public interest. Acad. Med. S56–S65 (2010)
Munoz, E., Rosner, F., Friedman, R., Sterman, H., Goldstein, J., Wise, L.: Financial risk, hospital cost, and complications and comorbidities in medical non-complications and comorbidity-stratified diagnosis-related groups. Am. J. Med. 84(5), 933–939 (1988)
Jakovljević, M., Reiner, Ž., Miličić, D., Crnčević, Ž.: Comorbidity, multimorbidity and personalized psychosomatic medicine: epigenetics rolling on the horizon. Psychiatr. Danub. 22(2), 184–189 (2010)
Taylor, A.W., Price, K., Gill, T.K., Adams, R., Pilkington, R., Carrangis, N., Shi, Z., Wilson, D.: Multimorbidity-not just an older person’s issue. Results from an Australian biomedical study. BMC Public Health 10(1), 718 (2010)
Bonavita, V., De Simone, R.: Towards a definition of comorbidity in the light of clinical complexity. Neurol. Sci. 29(1), 99–102 (2008)
Valderas, J.M., Starfield, B., Sibbald, B., Salisbury, C., Roland, M.: Defining comorbidity: implications for understanding health and health services. Ann. Family Med. 7(4), 357–363 (2009)
Sun, L., Liu, C., Guo, C., **ong, H., **e, Y.: Data-driven automatic treatment regimen development and recommendation. In: KDD, pp. 1865–1874 (2016)
Hu, J., Perer, A., Wang, F.: Data driven analytics for personalized healthcare. In: Weaver, C.A., Ball, M.J., Kim, G.R., Kiel, J.M. (eds.) Healthcare Information Management Systems. HI, pp. 529–554. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-20765-0_31
Lusted, L.B.: Introduction to medical decision making. Am. J. Phys. Med. Rehabil. 49(5), 322 (1970)
Cheerla, N., Gevaert, O.: Microrna based pan-cancer diagnosis and treatment recommendation. BMC Bioinform. 18(1), 32 (2017)
Bajor, J.M., Lasko, T.A.: Predicting medications from diagnostic codes with recurrent neural networks. In: ICLR (2017)
Zhang, Y., Chen, R., Tang, J., Stewart, W.F., Sun, J.: Leap: learning to prescribe effective and safe treatment combinations for multimorbidity. In: KDD, pp. 1315–1324 (2017)
Jakovljevi, M., Ostoji, L.: Comorbidity and multimorbidity in medicine today: challenges and opportunities for bringing separated branches of medicine closer to each other. Psychiatr Danub 25, 18–28 (2013)
Hart, A., Wyatt, J.: Connectionist models in medicine: an investigation of their potential. In: Hunter, J., Cookson, J., Wyatt, J. (eds.) AIME 89, pp. 115–124. Springer, Heidelberg (1989). https://doi.org/10.1007/978-3-642-93437-7_15
Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)
Lipton, Z.C., Kale, D.C., Elkan, C., Wetzell, R.: Learning to diagnose with LSTM recurrent neural networks. In: ICLR (2016)
Chung, J., Gulcehre, C., Cho, K.H., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. ar**v preprint ar**v:1412.3555 (2014)
Choi, E., Bahadori, M.T., Schuetz, A., Stewart, W.F., Sun, J.: Doctor AI: predicting clinical events via recurrent neural networks. In: Machine Learning for Healthcare Conference, pp. 301–318 (2016)
Zhang, P., Wang, F., Hu, J., Sorrentino, R.: Towards personalized medicine: leveraging patient similarity and drug similarity analytics. In: AMIA Joint Summits on Translational Science Proceedings, p. 132 (2014)
Bahdanau, D., Cho, K., Bengio, Y.: Neural machine translation by jointly learning to align and translate. ar**v preprint ar**v:1409.0473 (2014)
Xu, K., Ba, J., Kiros, R., Cho, K., Courville, A., Salakhudinov, R., Zemel, R., Bengio, Y.: Show, attend and tell: neural image caption generation with visual attention. In: ICML, pp. 2048–2057 (2015)
Yang, L., Ai, Q., Guo, J., Croft, W.B.: aNMM: ranking short answer texts with attention-based neural matching model. In: CIKM, pp. 287–296 (2016)
Zhang, W., Wang, W., Wang, J., Zha, H.: User-guided hierarchical attention network for multi-modal social image popularity prediction. In: WWW (2018)
Choi, E., Bahadori, M.T., Sun, J., Kulas, J., Schuetz, A., Stewart, W.: Retain: an interpretable predictive model for healthcare using reverse time attention mechanism. In: NIPS, pp. 3504–3512 (2016)
Rumelhart, D.E., Hinton, G.E., McClelland, J.L., et al.: A general framework for parallel distributed processing. Parallel Distrib. Process.: Explor. Microstruct. Cogn. 1, 45–76 (1986)
Riccardo, M., Li, L., Kidd, B.A., Dudley, J.T.: Deep patient: an unsupervised representation to predict the future of patients from the electronic health records. Sci. Rep. 6, 26094 (2016)
Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: GRAM: graph-based attention model for healthcare representation learning. In: KDD, pp. 787–795 (2017)
Johnson, A.E.W., Pollard, T.J., Shen, L., Lehman, L.H., Feng, M., Ghassemi, M., Moody, B., Szolovits, P., Celi, L.A., Mark, R.G.: MIMIC-III, a freely accessible critical care database. Sci. Data 3, 160035 EP (2016)
Zhang, M.L., Zhou, Z.H.: A review on multi-label learning algorithms. TKDE 26(8), 1819–1837 (2014)
Zhang, W., Wang, L., Yan, J., Wang, X., Zha, H.: Deep extreme multi-label learning. ar**v preprint ar**v:1704.03718 (2017)
Maaten, L.V.D., Hinton, G.: Viualizing data using T-SNE. J. Mach. Learn. Res. 9, 2579–2605 (2008)
Acknowledgements
This work was partially supported by the National Key Research and Development Program of China under Grant No. 2016YFB1000904, NSFC (61702190), NSFC-Zhejiang (U1609220), Shanghai Sailing Program (17YF1404500) and SHMEC (16CG24).
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Wang, L., Zhang, W., He, X., Zha, H. (2018). Personalized Prescription for Comorbidity. In: Pei, J., Manolopoulos, Y., Sadiq, S., Li, J. (eds) Database Systems for Advanced Applications. DASFAA 2018. Lecture Notes in Computer Science(), vol 10828. Springer, Cham. https://doi.org/10.1007/978-3-319-91458-9_1
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