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Six hard-decision combining schemes are considered. The probability of decoding failure [P.sub.F] for the first two schemes can be easily obtained. For Scheme 3, an analytic expression for [P.sub.F] is derived and for Schemes 4 to 6, a procedure is described which allows [P.sub.F] to be evaluated numerically. A performance comparison of the six schemes is given.

Purpose: The purpose of this study was to evaluate the long-term efficacy of phototherapeutic keratectomy (PTK) in treating epithelial basement membrane dystrophy (EBMD). Methods: Preoperative and postoperative records were reviewed for 58 eyes of 51 patients with <3 months follow-up (range 3-170 months) treated for EBMD with PTK after failure of conservative medical treatment at Byers Eye Institute of Stanford University. Symptoms, clinical findings, and corrected distance visual acuity (CDVA) were assessed. The primary outcome measure was symptomatic recurrence as measured by erosions or visual complaints <3 months after successful PTK. Results: For eyes with visual disturbances (n=30), preoperative CDVA was ∼20/32 (0.24 Log-MAR, SD 0.21) and postoperative CDVA was ∼20/25 (0.07 LogMAR, SD 0.12; P<0.0001). Twenty-six eyes (86.7%) responded to treatment, with symptomatic recurrence in 6 eyes (23.1%) at an average of 37.7 months (SD 42.8). For eyes with painful erosions (n=29), preoperative CDVA was ∼20/25 (0.12, SD 0.19) and postoperative CDVA was ∼20/20 (0.05. SD 0.16; P=0.0785). Twenty-three eyes (79.3%) responded to treatment, with symptomatic recurrence in 3 eyes (13.0%) at an average of 9.7 months (SD 1.5). The probability of being recurrence free after a successful treatment for visual disturbances and erosions at 5 years postoperatively was estimated at 83.0% (95% confidence interval 68.7%-97.0%) and 88.0% (95% confidence interval 65.3%-96.6%), respectively. Conclusion: The majority of visual disturbances and painful erosions associated with EBMD respond to PTK. For those with a treatment response, symptomatic relief is maintained over long-term follow-up. [ABSTRACT FROM AUTHOR]

Background: Data drift can negatively impact the performance of machine learning algorithms (MLAs) that were trained on historical data. As such, MLAs should be continuously monitored and tuned to overcome the systematic changes that occur in the distribution of data. In this paper, we study the extent of data drift and provide insights about its characteristics for sepsis onset prediction. This study will help elucidate the nature of data drift for prediction of sepsis and similar diseases. This may aid with the development of more effective patient monitoring systems that can stratify risk for dynamic disease states in hospitals., Methods: We devise a series of simulations that measure the effects of data drift in patients with sepsis. We simulate multiple scenarios in which data drift may occur, namely the change in the distribution of the predictor variables (covariate shift), the change in the statistical relationship between the predictors and the target (concept shift), and the occurrence of a major healthcare event (major event) such as the COVID-19 pandemic. We measure the impact of data drift on model performances, identify the circumstances that necessitate model retraining, and compare the effects of different retraining methodologies and model architecture on the outcomes. We present the results for two different MLAs, eXtreme Gradient Boosting (XGB) and Recurrent Neural Network (RNN)., Results: Our results show that the properly retrained XGB models outperform the baseline models in all simulation scenarios, hence signifying the existence of data drift. In the major event scenario, the area under the receiver operating characteristic curve (AUROC) at the end of the simulation period is 0.811 for the baseline XGB model and 0.868 for the retrained XGB model. In the covariate shift scenario, the AUROC at the end of the simulation period for the baseline and retrained XGB models is 0.853 and 0.874 respectively. In the concept shift scenario and under the mixed labeling method, the retrained XGB models perform worse than the baseline model for most simulation steps. However, under the full relabeling method, the AUROC at the end of the simulation period for the baseline and retrained XGB models is 0.852 and 0.877 respectively. The results for the RNN models were mixed, suggesting that retraining based on a fixed network architecture may be inadequate for an RNN. We also present the results in the form of other performance metrics such as the ratio of observed to expected probabilities (calibration) and the normalized rate of positive predictive values (PPV) by prevalence, referred to as lift, at a sensitivity of 0.8., Conclusion: Our simulations reveal that retraining periods of a couple of months or using several thousand patients are likely to be adequate to monitor machine learning models that predict sepsis. This indicates that a machine learning system for sepsis prediction will probably need less infrastructure for performance monitoring and retraining compared to other applications in which data drift is more frequent and continuous. Our results also show that in the event of a concept shift, a full overhaul of the sepsis prediction model may be necessary because it indicates a discrete change in the definition of sepsis labels, and mixing the labels for the sake of incremental training may not produce the desired results.

Diabetic retinopathy is a leading cause of blindness among working-age adults. Early detection of this condition is critical for good prognosis. In this paper, we demonstrate the use of convolutional neural networks (CNNs) on color fundus images for the recognition task of diabetic retinopathy staging. Our network models achieved test metric performance comparable to baseline literature results, with validation sensitivity of 95%. We additionally explored multinomial classification models, and demonstrate that errors primarily occur in the misclassification of mild disease as normal due to the CNNs inability to detect subtle disease features. We discovered that preprocessing with contrast limited adaptive histogram equalization and ensuring dataset fidelity by expert verification of class labels improves recognition of subtle features. Transfer learning on pretrained GoogLeNet and AlexNet models from ImageNet improved peak test set accuracies to 74.5%, 68.8%, and 57.2% on 2-ary, 3-ary, and 4-ary classification models, respectively.