J Med Syst
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The surgical Apgar score predicts major 30-day postoperative complications using data assessed at the end of surgery. We hypothesized that evaluating the surgical Apgar score continuously during surgery may identify patients at high risk for postoperative complications. We retrospectively identified general, vascular, and general oncology patients at Vanderbilt University Medical Center. ⋯ To optimize the tradeoff between inadequate and excessive alerting with future real-time notifications, we recommend a threshold probability of 0.24. Continuous assessment of the surgical Apgar score is predictive for major postoperative complications. In the future, real-time notifications might allow for detection and mitigation of changes in a patient's accumulating risk of complications during a surgical procedure.
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Operation theatre is one of the most significant assets in a hospital as the greatest source of revenue as well as the largest cost unit. This paper focuses on surgery scheduling optimization, which is one of the most crucial tasks in operation theatre management. A combined scheduling policy composed of three simple scheduling rules is proposed to optimize the performance of scheduling operation theatre. ⋯ With two optimization objectives, the response surface method is adopted to search for the optimal weight of simple rules in a combined scheduling policy in the model. Moreover, the weights configuration can be revised to cope with dispatching dynamics according to real-time change at the operation theatre. Finally, performance comparison between the proposed combined scheduling policy and tabu search algorithm indicates that the combined scheduling policy is capable of sequencing surgery appointments more efficiently.
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Early and accurate diagnosis of Parkinson's disease (PD) remains challenging. Neuropathological studies using brain bank specimens have estimated that a large percentages of clinical diagnoses of PD may be incorrect especially in the early stages. In this paper, a comprehensive computer model is presented for the diagnosis of PD based on motor, non-motor, and neuroimaging features using the recently-developed enhanced probabilistic neural network (EPNN). ⋯ The results are compared to four other commonly-used machine learning algorithms: the probabilistic neural network (PNN), support vector machine (SVM), k-nearest neighbors (k-NN) algorithm, and classification tree (CT). The EPNN had the highest classification accuracy at 92.5% followed by the PNN (91.6%), k-NN (90.8%) and CT (90.2%). The EPNN exhibited an accuracy of 98.6% when classifying healthy control (HC) versus PD, higher than any previous studies.