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Gaussian method fashions (GPMs) are typically used to investigate complex physiologic statistics for the cause of identifying patterns and predicting outcomes of disorder states. In this study, 14-day pre-operative facts from 73 patients with white-blood-cellular-negative spontaneous pleural effusions were used to optimize the capacity of GPMs to predict postoperative pulmonary effusion formation. The statistics contained scientific measures (pre-operative temperature, albumin degrees, radiographic features (pleural flocculation, and so on.), and echocardiography measures (right atria length, etc.) as input variables for the GPMs. Through optimization of hyper parameters, pre-processing techniques, and characteristic choice algorithms, the performance of the GPMs was advanced drastically, with an AUC price that passed 0.95.

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Optimizing the Capabilities of Gaussian Process Models for Pulmonary Effusion Prediction Analysis

  • 会议论文

  • 发表日期：

    2025年

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  • R. Kavitha,
  • Preeti Naval,
  • Murli Manohar Gour,
  • Manish Kaushik

摘要

Gaussian method fashions (GPMs) are typically used to investigate complex physiologic statistics for the cause of identifying patterns and predicting outcomes of disorder states. In this study, 14-day pre-operative facts from 73 patients with white-blood-cellular-negative spontaneous pleural effusions were used to optimize the capacity of GPMs to predict postoperative pulmonary effusion formation. The statistics contained scientific measures (pre-operative temperature, albumin degrees, radiographic features (pleural flocculation, and so on.), and echocardiography measures (right atria length, etc.) as input variables for the GPMs. Through optimization of hyper parameters, pre-processing techniques, and characteristic choice algorithms, the performance of the GPMs was advanced drastically, with an AUC price that passed 0.95.