cirpublications.com-ZFTu-1790880678-f249917157 1790880678 Clinical Intelligence Research Press info@cirpublications.com Clinical Intelligence Research Press Journal of Artificial Intelligence for Healthcare Systems J. Artif. Intell. Healthc. Syst. 3149-8981 2022 1 2 Nguyen Thanh Huy Pham Quang Minh Le Thi Bich 2022 10.68159/f249917157 https://cirpublications.com/pub/journal/1/article/f249917157 Wang Y, Lei L, Ji M, Tong J, Zhou CM, Yang JJ. Predicting postoperative delirium after microvascular decompression surgery with machine learning. J Clin Anesth. 2020;66:109896. 10.1016/j.jclinane.2020.109896 Röhr V, Blankertz B, Radtke FM, Spies C, Koch S. Machine-learning model predicting postoperative delirium in older patients using intraoperative frontal electroencephalographic signatures. Front Aging Neurosci. 2022;14:911088. 10.3389/fnagi.2022.911088 Hu XY, Liu H, Zhao X, Sun X, Zhou J, Gao X, et al. Automated machine learning-based model predicts postoperative delirium using readily extractable perioperative collected electronic data. CNS Neurosci Ther. 2022;28(4):608-18. 10.1111/cns.13783 Zhao H, You J, Peng Y, Feng Y. Machine learning algorithm using electronic chart-derived data to predict delirium after elderly hip fracture surgeries: a retrospective case-control study. Front Surg. 2021;8:634629. 10.3389/fsurg.2021.634629 Jauk S, Kramer D, Großauer B, Rienmüller S, Avian A, Berghold A, et al. Risk prediction of delirium in hospitalized patients using machine learning: an implementation and prospective evaluation study. J Am Med Inform Assoc. 2020;27(9):1383-92. Li GH, Zhao L, Lu Y, Wang W, Ma T, Zhang YX, et al. Development and validation of a risk score for predicting postoperative delirium after major abdominal surgery by incorporating preoperative risk factors and surgical Apgar score. J Clin Anesth. 2021;75:110408. 10.1016/j.jclinane.2021.110408 Fliegenschmidt J, Hulde N, Preising MG, Ruggeri S, Szymanowski R, Meesseman L, et al. Artificial intelligence predicts delirium following cardiac surgery: a case study. J Clin Anesth. 2021;75:110473. 10.1016/j.jclinane.2021.110473 Kwon Y, Won JH, Kim BJ, Paik MC. Uncertainty quantification using Bayesian neural networks in classification: application to biomedical image segmentation. Comput Stat Data Anal. 2020;142:106816. 10.1016/j.csda.2019.106816 Ghoshal B, Tucker A, Sanghera B, Lup Wong W. Estimating uncertainty in deep learning for reporting confidence to clinicians in medical image segmentation and diseases detection. Comput Intell. 2021;37(2):701-34. 10.1111/coin.12303 Begoli E, Bhattacharya T, Kusnezov D. The need for uncertainty quantification in machine-assisted medical decision making. Nat Mach Intell. 2019;1(1):20-3. 10.1038/s42256-018-0004-1 Bishara A, Chiu C, Whitlock EL, Douglas VC, Lee S, Butte AJ, et al. Postoperative delirium prediction using machine learning models and preoperative electronic health record data. BMC Anesthesiol. 2022;22(1):8. 10.1186/s12871-021-01592-7 Jung JW, Hwang S, Ko S, Jo C, Park HY, Han HS, et al. A machine-learning model to predict postoperative delirium following knee arthroplasty using electronic health records. BMC Psychiatry. 2022;22(1):436. 10.1186/s12888-022-04076-4 Valen J, Balki I, Mendez M, Qu W, Levman J, Bilbily A, et al. Quantifying uncertainty in machine learning classifiers for medical imaging. Int J Comput Assist Radiol Surg. 2022;17(4):711-8. 10.1007/s11548-021-02553-4 Stoean R, Stoean C, Atencia M, Rodríguez-Labrada R, Joya G. Ranking information extracted from uncertainty quantification of the prediction of a deep learning model on medical time series data. Mathematics. 2020;8(7):1078. 10.3390/math8071078 Kompa B, Snoek J, Beam AL. Second opinion needed: communicating uncertainty in medical machine learning. NPJ Digit Med. 2021;4(1):4. 10.1038/s41746-020-00367-3 Loftus TJ, Shickel B, Ruppert MM, Balch JA, Ozrazgat-Baslanti T, Tighe PJ, et al. Uncertainty-aware deep learning in healthcare: a scoping review. PLOS Digit Health. 2022;1(8):e0000085. 10.1371/journal.pdig.0000085 Caldeira J. Deeply uncertain: comparing methods of uncertainty quantification in deep learning algorithms [slides]. Batavia (IL): Fermi National Accelerator Laboratory; 2020. Kurz A, Hauser K, Mehrtens HA, Krieghoff-Henning E, Hekler A, Kather JN, et al. Uncertainty estimation in medical image classification: systematic review. JMIR Med Inform. 2022;10(8):e36427. 10.2196/36427 Lemay A, Hoebel K, Bridge CP, Befano B, De Sanjosé S, Egemen D, et al. Improving the repeatability of deep learning models with Monte Carlo dropout. NPJ Digit Med. 2022;5(1):174. 10.1038/s41746-022-00715-5 Milanés-Hermosilla D, Trujillo Codorniú R, López-Baracaldo R, Sagaró-Zamora R, Delisle-Rodriguez D, Villarejo-Mayor JJ, et al. Monte Carlo dropout for uncertainty estimation and motor imagery classification. Sensors (Basel). 2021;21(21):7241. 10.3390/s21217241 Stoean C, Stoean R, Atencia M, Abdar M, Velázquez-Pérez L, Khosravi A, et al. Automated detection of presymptomatic conditions in Spinocerebellar Ataxia type 2 using Monte Carlo dropout and deep neural network techniques with electrooculogram signals. Sensors (Basel). 2020;20(11):3032. 10.3390/s20113032 Nguyen D, Sadeghnejad Barkousaraie A, Bohara G, Balagopal A, McBeth R, Lin MH, et al. A comparison of Monte Carlo dropout and bootstrap aggregation on the performance and uncertainty estimation in radiation therapy dose prediction with deep learning neural networks. Phys Med Biol. 2021;66(5):054002. Alvarsson J, McShane SA, Norinder U, Spjuth O. Predicting with confidence: using conformal prediction in drug discovery. J Pharm Sci. 2021;110(1):42-9. 10.1016/j.xphs.2020.09.055 Davis SE, Lasko TA, Chen G, Siew ED, Matheny ME. Calibration drift in regression and machine learning models for acute kidney injury. J Am Med Inform Assoc. 2017;24(6):1052-61. Huang Y, Li W, Macheret F, Gabriel RA, Ohno-Machado L. A tutorial on calibration measurements and calibration models for clinical prediction models. J Am Med Inform Assoc. 2020;27(4):621-33. Lin YH, Li GH. A Bayesian deep learning framework for RUL prediction incorporating uncertainty quantification and calibration. IEEE Trans Ind Inform. 2022;18(10):7274-84. 10.1109/TII.2022.3151297 Peng W, Ye ZS, Chen N. Bayesian deep-learning-based health prognostics toward prognostics uncertainty. IEEE Trans Ind Electron. 2020;67(3):2283-93. 10.1109/TIE.2019.2907440 Hernández S, López JL. Uncertainty quantification for plant disease detection using Bayesian deep learning. Appl Soft Comput. 2020;96:106597. 10.1016/j.asoc.2020.106597 Sagar A. Uncertainty quantification using variational inference for biomedical image segmentation. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision; 2022. p. 44-51.