Predicting Post-operative Visual Acuity for LASIK Surgeries

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• 关键词：LASIK surgeries ; UCVA ; Uncorrected visual acuity ; Regression
• 刊名：Lecture Notes in Computer Science
• 出版年：2016
• 出版时间：2016
• 年：2016
• 卷：9651
• 期：1
• 页码：489-501
• 全文大小：962 KB
• 参考文献：1.Applegate, R.A., Ballentine, C., Gross, H., Sarver, E.J., Sarver, C.A.: Visual acuity as a function of zernike mode and level of root mean square error. Optom. Vis. Sci. 80(2), 97–105 (2003)CrossRef
  2.Azar, D.T., Koch, D.: LASIK (Laser in Situ Keratomileusis): Fundamentals, Surgical Techniques, and Complications. CRC Press, Boca Raton (2002)
  3.Baron, W.S., Munnerlyn, C.: Predicting visual performance following excimer photorefractive keratectomy. Refract. Corneal Surg. 8(5), 355–362 (1991)
  4.Roy, S.B., Teredesai, A., Zolfaghar, K., Liu, R., Hazel, D., Newman, S., Marinez, A.: Dynamic hierarchical classification for patient risk-of-readmission. In: Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1691–1700. ACM (2015)
  5.Caballero Barajas, K.L., Akella, R.: Dynamically modeling patient’s health state from electronic medical records: a time series approach. In: Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 69–78. ACM (2015)
  6.Caruana, R., Lou, Y., Gehrke, J., Koch, P., Sturm, M., Elhadad, N.: Intelligible models for healthcare: predicting pneumonia risk and hospital 30-day readmission. In: Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1721–1730. ACM (2015)
  7.Che, Z., Kale, D., Li, W., Bahadori, M.T., Liu, Y.: Deep computational phenotyping. In: Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 507–516. ACM (2015)
  8.Cheng, X., Bradley, A., Thibos, L.N.: Predicting subjective judgment of best focus with objective image quality metrics. J. Vis. 4(4), 7 (2004)CrossRef
  9.Fan, K., Eisenberg, M., Walsh, A., Aiello, A., Heller, K.: Hierarchical graph-coupled HMMs for heterogeneous personalized health data. In: Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 239–248. ACM (2015)
  10.Faulkner, W.: Laser interferometric prediction of postoperative visual acuity in patients with cataracts. Am. J. Ophthalmol. 95(5), 626–636 (1983)CrossRef
  11.Ghassemi, M., Naumann, T., Doshi-Velez, F., Brimmer, N., Joshi, R., Rumshisky, A., Szolovits, P.: Unfolding physiological state: mortality modelling in intensive care units. In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 75–84. ACM (2014)
  12.Guirao, A., Williams, D.R., et al.: A method to predict refractive errors from wave aberration data. Optom. Vis. Sci. 80(1), 36–42 (2003)CrossRef
  13.Hall, M.A.: Correlation-based Feature Subset Selection for Machine Learning. Ph.D. thesis, University of Waikato, Hamilton, New Zealand (1998)
  14.Ho, J.C., Ghosh, J., 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. ACM (2014)
  15.Liu, C., Wang, F., Jianying, H., Xiong, H.: Temporal phenotyping from longitudinal electronic health records: a graph based framework. In: Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 705–714. ACM (2015)
  16.Nori, N., Kashima, H., Yamashita, K., Ikai, H., Imanaka, Y.: Simultaneous modeling of multiple diseases for mortality prediction in acute hospital care. In: Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 855–864. ACM (2015)
  17.Olsen, T.W., Summers, C.G., Knobloch, W.H.: Predicting visual acuity in children with colobomas involving the optic nerve. J. Pediatr. Ophthalmol. Strabismus 33(1), 47–51 (1995)
  18.Somanchi, S., Adhikari, S., Lin, A., Eneva, E., Ghani, R.: Early prediction of cardiac arrest (code blue) using electronic medical records. In: pp. 1691–1700 (2015)
  19.Spurny, R.C., Zaldivar, R., Davis Belcher, C., Simmons, R.J.: Instruments for predicting visual acuity: a clinical comparison. Arch. Ophthalmol. 104(2), 196–200 (1986)CrossRef
  20.Thibos, L.N., Hong, X., Bradley, A., Applegate, R.A.: Accuracy and precision of objective refraction from wavefront aberrations. J. Vis. 4(4), 9 (2004)CrossRef
  21.Watson, A.B., Ahumada, A.J.: Predicting visual acuity from wavefront aberrations. J. Vis. 8(4), 17 (2008)CrossRef
  
• 作者单位：Manish Gupta (19)
  Prashant Gupta (19)
  Pravin K. Vaddavalli (20)
  Asra Fatima (20)
  
  19. Microsoft, Hyderabad, India
  20. L. V. Prasad Eye Institute (LVPEI), Hyderabad, India
  
• 丛书名：Advances in Knowledge Discovery and Data Mining
• ISBN：978-3-319-31753-3
• 刊物类别：Computer Science
• 刊物主题：Artificial Intelligence and Robotics
  Computer Communication Networks
  Software Engineering
  Data Encryption
  Database Management
  Computation by Abstract Devices
  Algorithm Analysis and Problem Complexity
  
• 出版者：Springer Berlin / Heidelberg
• ISSN：1611-3349

文摘

LASIK (Laser-Assisted in SItu Keratomileusis) surgeries have been quite popular for treatment of myopia (nearsightedness), hyperopia (farsightedness) and astigmatism over the past two decades. In the past decade, over 10 million LASIK procedures had been performed in the United States alone with an average cost of approximately $2000 USD per surgery. While 99 % of such surgeries are successful, the commonest side effect is a residual refractive error and poor uncorrected visual acuity (UCVA). In this work, we aim at predicting the UCVA post LASIK surgery. We model the task as a regression problem and use the patient demography and pre-operative examination details as features. To the best of our knowledge, this is the first work to systematically explore this critical problem using machine learning methods. Further, LASIK surgery settings are often determined by practitioners using manually designed rules. We explore the possibility of determining such settings automatically to optimize for the best post-operative UCVA by including such settings as features in our regression model. Our experiments on a dataset of 791 surgeries provides an RMSE (root mean square error) of 0.102, 0.094 and 0.074 for the predicted post-operative UCVA after one day, one week and one month of the surgery respectively.