На основании анализа зарубежных публикаций проведена сравнительная характеристика методов и моделей прогнозирования длительности хирургических операций. Показана важность оценки точности прогнозирования длительности операций для эффективного планирования использования операционных помещений и высокотехнологического оборудования. Проанализированы статистические и регрессионные методы прогнозирования длительности операций, а также использование искусственных нейронных сетей для оценки продолжительности операции. Приведены математические выражения, позволяющие оценить длительность операции в целом, а также данные о погрешностях прогнозирования.
Литература
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4. Dexter F. Application of prediction levels to OR scheduling. AORN Journal, 63(3), 607–615 8p. (1996). doi:10.1016/S0001–2092(06)63398-X.
5. Sorge M., Computerized O. R. scheduling: Is it an accurate predictor of surgical time? Canadian Operating Room Nursing Journal, 19(4), 7–18 11p. (2001).
6. Pandit J.J. & Carey A. Estimating the duration of common elective operations: Implications for operating list management. Anaesthesia, 61(8), 768–776 9p. (2006).
7. Eijkemans M.J., van Houdenhoven M., Nguyen T., Boersma E., Steyerberg E. W., Kazemier G. Predicting the unpredictable: A new prediction model for operating room times using individual characteristics and the surgeon’s estimate. Anesthesiology, 112(1), 41–49. (2010). doi:10.1097/ALN.0b013e3181c294c2).
8. Macario A., Dexter F. Estimating the Duration of a Case When the Surgeon Has Not Recently Scheduled the Procedure at The Surgical Suite. Anesth. Analg. 89; 1241–5. (1999).
9. Zhou J., Dexter F.: Method to assist in the scheduling of add-on surgical cases: Upper prediction bounds for surgical case durations based on the lognormal distribution. Anesthesiology, Nov. Volume 89; 8911228–3. (1998).
10. Strum D.P., May J. H., Vargas L. G. Modeling the uncertainty of surgical procedure times: comparison of log- normal and normal models. Anesthesiology. 92(4):1160–7. (2000).
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14. Edelman E.I., van Kuijk S. M. J., Hamaeker A. E. W., de Korte M. J. M., van Merode G. G., Buhre W. F. Improving the Prediction of Total surgical Procedure Time Using linear regression Modeling. Original Research published: 19 June 2017. doi: 10.3389/fmed.2017.00085.
15. Li Y., Zhang S., Baugh R. F., Huang J. Z. Predicting surgical case durations using ill- conditioned CPT code matrix. IIE Transactions (Institute of Industrial Engineers), 42(2), Pp.121–135. (2010).
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17. Nathan H.Ng., Rodney A.Gabriel, Julian McAuley, Charles Elkan, Zachary C. Lipton Predicting Surgery Duration with Neural Heteroscedastic Regression. Proceedings of Machine Learning for Healthcare. Juli, 2017.
18. Галушкин А. И. Теория нейронных сетей. Кн.1: Учеб. пособие для вузов / Общая ред. А. И. Галушкина. – М.: ИПРЖР, 2000. – 416 с.
19. Hamid A., Dwivedi U. S., Singh T. N., Gopi Kishore M., Mahmood M., Singh H. et al. Artificial neural networks in predicting optimum renal stone fragmentation by extracorporeal shock wave lithotripsy: a preliminary study. BJU International. Volume: 91. 2003; Jun. Pp. 821–824.
20. Seckiner Ilker, Seckiner Serap et al. A neural network – based algorithm for predicting stone-free status after ESWL therapy. Int Braz J. Urol. 2017 Jul 20;43. doi: 10.1590/S1677–5538.IBJU.2016.0630.
21. ShahabiKargar Z., Khanna S. Predicting Procedure Duration to Improve Scheduling of Elective Surgery. PRICAI Proceedings: Trends in Artificial Intelligence Pp. 998 1009. (2014).
22. Franke S., Meixensberger J., Neumuth T. Intervention time prediction from surgical low-level tasks. Journal of Biomedical Informatics 46 (2013) Pp.152–159.
23. Guédon A.C. P., Paalvast M., Meeuwsen F. C. It is Time to Prepare the Next patient’ Real-Time Prediction of Procedure Duration in Laparoscopic Cholecystectomies. J. Med. Syst. (2016) 40: 271 DOI 10.1007/ s10916 016 0631 1.
24. Brown P.T. A Retrospective Analysis of Surgeon Estimated Time and Actual Operative Time to Develop an Efficient Surgical Scheduling Model. DNP, RN, CNOR. https://hsrc.himmelfarb.gwu.edu/son_dnp/2.
25. Kayis E., Wang H., Patel M., Gonzalez T., Jain S., Ramamurthi R.J., Santos C., Singhal S., Suermondt J., Sylvester K. Improving Prediction of Surgery Duration using Operational and Temporal Factors. AMIA Annu Symp Proc. 2012; 2012:456–62. Epub. 2012. Nov. 3. https://www.ncbi.nlm.nih.gov/pmc/articles/ PMC3540440/pdf/ amia_2012_symp_0456.pdf.
2. Schofield W.N., Rubin G. L., Piza M., Lai Y. Y., Sindhusake D., Fearnside M. R., Klineberg, P.L.: Cancellation of operations on the day of intended surgery at a major Australian referral hospital. Med. J. Aust. 182(12), 612–615 (2005).
3. Brown P.T. A Retrospective Analysis of Surgeon Estimated Time and Actual Operative Time to Develop an Efficient Operating Room Scheduling System. DNP, RN, CNOR (2017). https://hsrc.himmelfarb.gwu. edu/son_dnp/2.
4. Dexter F. Application of prediction levels to OR scheduling. AORN Journal, 63(3), 607–615 8p. (1996). doi:10.1016/S0001–2092(06)63398-X.
5. Sorge M., Computerized O. R. scheduling: Is it an accurate predictor of surgical time? Canadian Operating Room Nursing Journal, 19(4), 7–18 11p. (2001).
6. Pandit J.J. & Carey A. Estimating the duration of common elective operations: Implications for operating list management. Anaesthesia, 61(8), 768–776 9p. (2006).
7. Eijkemans M.J., van Houdenhoven M., Nguyen T., Boersma E., Steyerberg E. W., Kazemier G. Predicting the unpredictable: A new prediction model for operating room times using individual characteristics and the surgeon’s estimate. Anesthesiology, 112(1), 41–49. (2010). doi:10.1097/ALN.0b013e3181c294c2).
8. Macario A., Dexter F. Estimating the Duration of a Case When the Surgeon Has Not Recently Scheduled the Procedure at The Surgical Suite. Anesth. Analg. 89; 1241–5. (1999).
9. Zhou J., Dexter F.: Method to assist in the scheduling of add-on surgical cases: Upper prediction bounds for surgical case durations based on the lognormal distribution. Anesthesiology, Nov. Volume 89; 8911228–3. (1998).
10. Strum D.P., May J. H., Vargas L. G. Modeling the uncertainty of surgical procedure times: comparison of log- normal and normal models. Anesthesiology. 92(4):1160–7. (2000).
11. Hosseini N., Sir M. Y., Jankowski C. J., Pasupathy K. S. Surgical Duration Estimation via Data Mining and Predictive Modeling: A Case Study. AMIA Annu Symp Proc. 2015, Nov 5; 2015: Pp. 640–648.
12. Мисюк Н. С. Корреляционно-регрессионный анализ в клинической медицине / Н. С. Мисюк, А. С. Мастыкин, Г. П. Кузнецов. – М.: Медицина, 1975. – 200 с.
13. Реброва О. Ю. Статистический анализ медицинских данных. Применение пакета прикладных программ STATISTICA. – М.: МедиаСфера, 2003. – 305 с.
14. Edelman E.I., van Kuijk S. M. J., Hamaeker A. E. W., de Korte M. J. M., van Merode G. G., Buhre W. F. Improving the Prediction of Total surgical Procedure Time Using linear regression Modeling. Original Research published: 19 June 2017. doi: 10.3389/fmed.2017.00085.
15. Li Y., Zhang S., Baugh R. F., Huang J. Z. Predicting surgical case durations using ill- conditioned CPT code matrix. IIE Transactions (Institute of Industrial Engineers), 42(2), Pp.121–135. (2010).
16. Kargar Z.S., Khanna S., Good N., Sattar A., Lind J., O’Dwyer, J. Predicting Procedure Duration to Improve Scheduling of Elective Surgery / Conference Paper. December, 2014. DOI: 10.1007/978 3 319 13560 1_86. https://www.researchgate.net/publication/269287656_Predicting_ Procedure_Duration_to_Improve_Scheduling_of_Elective_Surgery.
17. Nathan H.Ng., Rodney A.Gabriel, Julian McAuley, Charles Elkan, Zachary C. Lipton Predicting Surgery Duration with Neural Heteroscedastic Regression. Proceedings of Machine Learning for Healthcare. Juli, 2017.
18. Галушкин А. И. Теория нейронных сетей. Кн.1: Учеб. пособие для вузов / Общая ред. А. И. Галушкина. – М.: ИПРЖР, 2000. – 416 с.
19. Hamid A., Dwivedi U. S., Singh T. N., Gopi Kishore M., Mahmood M., Singh H. et al. Artificial neural networks in predicting optimum renal stone fragmentation by extracorporeal shock wave lithotripsy: a preliminary study. BJU International. Volume: 91. 2003; Jun. Pp. 821–824.
20. Seckiner Ilker, Seckiner Serap et al. A neural network – based algorithm for predicting stone-free status after ESWL therapy. Int Braz J. Urol. 2017 Jul 20;43. doi: 10.1590/S1677–5538.IBJU.2016.0630.
21. ShahabiKargar Z., Khanna S. Predicting Procedure Duration to Improve Scheduling of Elective Surgery. PRICAI Proceedings: Trends in Artificial Intelligence Pp. 998 1009. (2014).
22. Franke S., Meixensberger J., Neumuth T. Intervention time prediction from surgical low-level tasks. Journal of Biomedical Informatics 46 (2013) Pp.152–159.
23. Guédon A.C. P., Paalvast M., Meeuwsen F. C. It is Time to Prepare the Next patient’ Real-Time Prediction of Procedure Duration in Laparoscopic Cholecystectomies. J. Med. Syst. (2016) 40: 271 DOI 10.1007/ s10916 016 0631 1.
24. Brown P.T. A Retrospective Analysis of Surgeon Estimated Time and Actual Operative Time to Develop an Efficient Surgical Scheduling Model. DNP, RN, CNOR. https://hsrc.himmelfarb.gwu.edu/son_dnp/2.
25. Kayis E., Wang H., Patel M., Gonzalez T., Jain S., Ramamurthi R.J., Santos C., Singhal S., Suermondt J., Sylvester K. Improving Prediction of Surgery Duration using Operational and Temporal Factors. AMIA Annu Symp Proc. 2012; 2012:456–62. Epub. 2012. Nov. 3. https://www.ncbi.nlm.nih.gov/pmc/articles/ PMC3540440/pdf/ amia_2012_symp_0456.pdf.
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